{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "b51d81ec-29ce-4538-b14a-d9cedced84e5",
      "metadata": {
        "tags": [],
        "id": "b51d81ec-29ce-4538-b14a-d9cedced84e5"
      },
      "source": [
        "```{index} single: AMPL\n",
        "```\n",
        "```{index} single: AMPL; AMPL Python API\n",
        "```\n",
        "```{index} single: AMPL MP Library\n",
        "```\n",
        "```{index} single: conic optimization; second order cones\n",
        "```\n",
        "```{index} single: solver; mosek\n",
        "```\n",
        "```{index} single: application; inventory management\n",
        "```\n",
        "\n",
        "# Economic Order Quantity\n",
        "\n",
        "This notebook demonstrates the reformulation of hyperbolic constraints as standard SOCO and also the direct modeling of the hyperbolic constraint. The example is familiar to any MBA/business student, and has a significant range of applications including warehouse operations.\n",
        "\n",
        "## Usage notes\n",
        "\n",
        "* The notebook requires a solver that can handle conic constraints. AMPL provides interfaces to the commercial solvers Gurobi and Mosek that include conic solvers. Other nonlinear solvers may solve these problems using more general techniques that are not specific to conic constraints.\n",
        "* Free licenses are available for academic use of `mosek` and `gurobi`.\n",
        "* If you do not have access to Gurobi or Mosek, you can use the `ipopt` solver. Note, however, that `ipopt` is a general purpose interior point solver, and does not have algorithms specific to conic problems."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "eea82369-66fa-45ca-8f9d-28e58ce41f79",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "83b0ff676cf4492fa8c3b7f2d3b96713",
            "157ac88738e64478945567a1351fd74c",
            "7a265bd8f9124a20aed0ca2e937d59bc",
            "aa71326de8284f04a78db8c0617d1086",
            "60b768ce404e49a78babf352bcd1d352",
            "8dcc3ed123554674a6e7b70feec50f6d",
            "e967021552af4133aaa67c329884f540",
            "dc2d0565eea043e6b8b65f118caa555a",
            "ff3166b7055c4b1f8fa5c9e02f74cfc9",
            "1f9f1a5168a847a88a6eb9aff21d2c0c",
            "8d06078489664deeb2484163a13075d2",
            "4580e11d2df4473ca55f137e61a6f72d",
            "aea0193803af4a19bf782020a218f172"
          ]
        },
        "id": "eea82369-66fa-45ca-8f9d-28e58ce41f79",
        "outputId": "b56cdb8a-b8a6-4f16-f875-edbd7939662d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(Output(), HBox(children=(Text(value='', description='License UUID:', style=DescriptionStyle(des…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "83b0ff676cf4492fa8c3b7f2d3b96713"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# install dependencies and select solver\n",
        "%pip install -q amplpy numpy pandas matplotlib\n",
        "\n",
        "SOLVER_CONIC = \"mosek\"  # mosek, ipopt, knitro\n",
        "SOLVER_NLO = \"ipopt\"\n",
        "\n",
        "from amplpy import AMPL, ampl_notebook\n",
        "\n",
        "ampl = ampl_notebook(\n",
        "    modules=[\"coin\", \"mosek\"],  # modules to install. ipopt is part of coin\n",
        "    license_uuid=\"default\",  # license to use\n",
        ")  # instantiate AMPL object and register notebook magics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "924b77c7",
      "metadata": {
        "scrolled": true,
        "id": "924b77c7"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ae49489-5c8a-4841-ad23-4573e1a1132f",
      "metadata": {
        "id": "1ae49489-5c8a-4841-ad23-4573e1a1132f"
      },
      "source": [
        "## The EOQ model\n",
        "\n",
        "### Classical formulation for a single item\n",
        "\n",
        "The economic order quantity (EOQ) is a classical problem in inventory management attributed to Ford Harris (1915). The problem is to find the order quantity that minimizes the cost of maintaining a specific item in inventory\n",
        "\n",
        "The cost for maintaining an item in inventory given an order size $x$ is given by\n",
        "\n",
        "$$f(x) = \\frac{h x}{2} + \\frac{c d}{x}$$\n",
        "\n",
        "where $h$ is the annual cost of holding an item including financing charges, $c$ is the fixed cost of placing and receiving an order, and $d$ is the annual demand. The factor $\\frac{1}{2}$ is a result of demand depletes the inventory at a constant rate over the year. The economic order quantity is the value of $x$ that minimizes $f(x)$\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "EOQ = \\arg\\min_x\\ & f(x) = \\frac{h x}{2} + \\frac{c d}{x} \\\\\n",
        "\\qquad \\text{s.t.}\\quad & x > 0 \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "Given the rather simple domain, we can derive analytically the solution for the EOQ problem by setting the derivative of $f(x)$ equal to zero and solving the resulting equation, obtaining\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "EOQ = x^{opt} & = \\sqrt{\\frac{2 c d}{h}} \\\\\n",
        "f^{opt} & = \\sqrt{2 c d h}\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "The following chart illustrates the nature of the problem and its analytical solution."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "1b1df390-8b08-46b6-9c0f-e5c85329e7fc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "1b1df390-8b08-46b6-9c0f-e5c85329e7fc",
        "outputId": "9ceeda5c-c30b-4b0d-edf3-72439f086102"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal order size = 3651.5 items with cost 2738.61\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 6000.0)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "h = 0.75  # cost of holding one item for one year\n",
        "c = 500.0  # cost of processing one order\n",
        "d = 10000.0  # annual demand\n",
        "\n",
        "eoq = np.sqrt(2 * c * d / h)\n",
        "fopt = np.sqrt(2 * c * d * h)\n",
        "print(f\"Optimal order size = {eoq:0.1f} items with cost {fopt:0.2f}\")\n",
        "\n",
        "x = np.linspace(100, 10000, 1000)\n",
        "f = h * x / 2 + c * d / x\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "ax.plot(x, f, lw=3, label=\"total cost\")\n",
        "ax.plot(x, h * x / 2, \"--\", lw=1, label=\"holding costs\")\n",
        "ax.plot(x, c * d / x, \"--\", lw=1, label=\"ordering costs\")\n",
        "ax.set_xlabel(\"x = order size\")\n",
        "ax.set_ylabel(\"cost\")\n",
        "ax.plot(eoq, fopt, \"ro\", ms=10, label=\"EOQ\")\n",
        "ax.legend(loc=\"lower right\")\n",
        "ax.annotate(\n",
        "    f\"EOQ = {eoq:0.2f}\",\n",
        "    xy=(eoq, 0),\n",
        "    xytext=(1.2 * eoq, 0.2 * fopt),\n",
        "    arrowprops=dict(facecolor=\"black\", shrink=0.15, width=1, headwidth=6),\n",
        ")\n",
        "ax.plot([eoq, eoq, 0], [0, fopt, fopt], \"r--\")\n",
        "ax.set_xlim(0, 10000)\n",
        "ax.set_ylim(0, 6000)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ada08001-e446-4d1d-8196-0fdee25b3349",
      "metadata": {
        "id": "ada08001-e446-4d1d-8196-0fdee25b3349"
      },
      "source": [
        "### Reformulating EOQ as a linear objective with hyperbolic constraint\n",
        "\n",
        "However, if the problem involved multiple products, an analytical solution would no longer be easily available. For this reason, we need to be able to solve the problem numerically, let us see how.\n",
        "\n",
        "It can be easily checked that the objective $f(x)$ is a convex function and therefore, the problem can be solved using any convex optimization solver. It is, however, a special type of convex problem which we shall show in the following reformulation.\n",
        "\n",
        "The optimization objective is linearized with the use of a second decision variable $y = 1/x$. The optimization problem is now a linear objective in two decision variables with a hyperbolic constraint $xy \\geq 1$.\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min_{x, y}\\ & f(x, y) = \\frac{h x}{2} + c d y \\\\\n",
        "\\quad \\text{s.t.}\\quad\n",
        "& x y  \\geq 1 \\\\\n",
        "& x, y > 0 \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "This constraint and the linear contours of the objective function are shown in the following diagrams. The solution of optimization problem occurs at a point where the constraint is tangent to  contours of the objective function.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "f2aeb40f-6f33-4776-b38e-dbb1aad57895",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "f2aeb40f-6f33-4776-b38e-dbb1aad57895",
        "outputId": "ef468b2e-9be5-428b-bbc1-3b997ba9943f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ca695a1ce90>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "h = 0.75  # cost of holding one item for one year\n",
        "c = 500.0  # cost of processing one order\n",
        "d = 10000.0  # annual demand\n",
        "\n",
        "x = np.linspace(100, 8000)\n",
        "y = (fopt - h * x / 2) / (c * d)\n",
        "\n",
        "eoq = np.sqrt(2 * c * d / h)\n",
        "fopt = np.sqrt(2 * c * d * h)\n",
        "yopt = (fopt - h * eoq / 2) / (c * d)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "ax.plot(x, 1 / x, lw=3, label=\"x y = 1\")\n",
        "ax.plot(x, (fopt - h * x / 2) / (c * d), \"g\", lw=3)\n",
        "for f in fopt * np.linspace(0, 3, 11):\n",
        "    ax.plot(x, (f - h * x / 2) / (c * d), \"g--\", alpha=0.5)\n",
        "ax.plot(eoq, yopt, \"ro\", ms=10)\n",
        "ax.annotate(\n",
        "    f\"EOQ = {eoq:0.2f}\",\n",
        "    xy=(eoq, 0),\n",
        "    xytext=(1.2 * eoq, 0.2 * yopt),\n",
        "    arrowprops=dict(facecolor=\"black\", shrink=0.15, width=1, headwidth=6),\n",
        ")\n",
        "\n",
        "ax.annotate(\n",
        "    \"\",\n",
        "    xytext=(4800, 0.0006),\n",
        "    xy=(4000, 1 / 3000),\n",
        "    arrowprops=dict(facecolor=\"black\", shrink=0.05, width=1, headwidth=6),\n",
        ")\n",
        "ax.text(4800, 0.0005, \"decreasing objective\")\n",
        "ax.fill_between(x, 1 / x, 0.0008, alpha=0.2, label=\"x y > 1\")\n",
        "ax.plot([eoq, eoq], [0, yopt], \"r--\")\n",
        "\n",
        "ax.set_xlim(0, 8000)\n",
        "ax.set_ylim(0, 0.0008)\n",
        "ax.set_xlabel(\"x = order size\")\n",
        "ax.set_ylabel(\"y\")\n",
        "ax.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f8dec6e2-f38a-4bbf-ae9d-0f0597d0cdd3",
      "metadata": {
        "id": "f8dec6e2-f38a-4bbf-ae9d-0f0597d0cdd3"
      },
      "source": [
        "## Reformulating the EOQ model with a linear objective and a second order cone constraint\n",
        "\n",
        "In elementary geometry, a hyperbola can be constructed from the intersection of a linear plane with cone. For this application, the hyperbola described by the constraint $x y \\geq 1$ invites the question of whether there is reformulation of EOQ that includes a cone constraint.\n",
        "\n",
        "A Lorenz cone is defined by\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "C & = \\{ (z, t)\\in\\mathbb{R}^3 \\ | \\ \\| z \\|_2 \\leq t \\}\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "where the components of are given by $z = \\begin{bmatrix} u \\\\ v \\end{bmatrix}$. The intersection of a plane aligned with the $t$ axis exactly describes a hyperbola. As described by Lobo, et al. (1998), the correspondence is given by\n",
        "\n",
        "$$w^2 \\leq x y,\\ x, y\\geq 0,\\ \\iff \\left \\|\\begin{bmatrix} 2w \\\\ x-y \\end{bmatrix} \\right \\|_2 \\leq x + y $$\n",
        "\n",
        "where the axis in the $w, x, y$ coordinates is tilted, displaced, and stretched compared to the coordinates shown in the diagram. The exact correspondence to the diagram is given by\n",
        "\n",
        "$$\\begin{align*}\n",
        "u & \\sim 2 w \\\\\n",
        "v & \\sim x - y \\\\\n",
        "t & \\sim x + y\n",
        "\\end{align*}$$\n",
        "\n",
        "The Python code below draws a hyperbola precisely as the intersection of a plane with Lorenz cone.\n",
        "\n",
        "Let us know rewrite the nonlinear constraint of the EOQ problem. Using the same geometric idea as above and leveraging the non-negativity of both variables, the constraint $xy \\geq 1$ can be reformulated using the following trick:\n",
        "\n",
        "$$\n",
        "    xy \\geq 1 \\quad \\Longleftrightarrow \\quad 4xy \\geq 4 \\quad \\Longleftrightarrow \\quad (x + y)^2 - (x - y)^2 \\geq 4  \\quad \\Longleftrightarrow \\quad \\left \\|\\begin{bmatrix} 2 \\\\ x-y \\end{bmatrix} \\right \\|_2 \\leq x + y,\n",
        "$$\n",
        "\n",
        "where we rely on the fact that $x + y \\geq 0$. The final constraint is known as a second-order conic optimization constraint (SOCO constraint). The result is a reformulation of the EOQ problem as a second order conic optimization (SOCO).\n",
        "\n",
        "$$\\begin{align*}\n",
        "\\min_{x, y}\\quad & f(x, y) = \\frac{h x}{2} + c d y \\\\\n",
        "\\text{s.t.}\\quad & \\left \\|\\begin{bmatrix} 2 \\\\ x-y \\end{bmatrix} \\right \\|_2 \\leq x + y\n",
        "\\end{align*}\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "e4ece0b0-021d-4a79-941a-699a752e5891",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "e4ece0b0-021d-4a79-941a-699a752e5891",
        "outputId": "9d451083-e322-4155-a9e9-68f64f9fcf39"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from mpl_toolkits import mplot3d\n",
        "import mpl_toolkits.mplot3d.art3d as art3d\n",
        "from matplotlib.patches import Rectangle\n",
        "\n",
        "t_max = 4\n",
        "w = 2\n",
        "n = 40\n",
        "\n",
        "fig = plt.figure(figsize=(9, 7))\n",
        "ax = plt.axes(projection=\"3d\")\n",
        "\n",
        "for t in np.linspace(0, t_max, n + 1):\n",
        "    if t < w:\n",
        "        a = np.linspace(0, 2 * np.pi, 30)\n",
        "        u = t * np.cos(a)\n",
        "        v = t * np.sin(a)\n",
        "        ax.plot3D(u, v, t, \"b\", lw=0.3)\n",
        "    else:\n",
        "        b = np.arccos(w / t)\n",
        "        a = np.linspace(b, 2 * np.pi - b, 30)\n",
        "        u = t * np.cos(a)\n",
        "        v = t * np.sin(a)\n",
        "        ax.plot3D(u, v, t, \"b\", lw=0.3)\n",
        "        ax.plot3D([2, 2], [t * np.sin(b), -t * np.sin(b)], [t, t], \"b\", lw=0.3)\n",
        "\n",
        "t = np.linspace(w, t_max)\n",
        "v = t * np.sin(np.arccos(w / t))\n",
        "u = w * np.array([1] * len(t))\n",
        "ax.plot3D(u, v, t, \"b\")\n",
        "ax.plot3D(u, -v, t, \"b\")\n",
        "\n",
        "ax.plot3D([0, t_max + 0.5], [0, 0], [0, 0], \"k\", lw=3, alpha=0.4)\n",
        "ax.plot3D([0, 0], [0, t_max + 1], [0, 0], \"k\", lw=3, alpha=0.4)\n",
        "ax.plot3D([0, 0], [0, 0], [0, t_max + 1], \"k\", lw=3, alpha=0.4)\n",
        "\n",
        "ax.text3D(t_max + 1, 0, 0.5, \"u\", fontsize=24)\n",
        "ax.text3D(0, t_max, 0.5, \"v\", fontsize=24)\n",
        "ax.text3D(0, 0, t_max + 1.5, \"t\", fontsize=24)\n",
        "\n",
        "ax.view_init(elev=20, azim=40)\n",
        "\n",
        "r = Rectangle((-t_max, 0), 2 * t_max, t_max + 1, alpha=0.2)\n",
        "ax.add_patch(r)\n",
        "art3d.pathpatch_2d_to_3d(r, z=w, zdir=\"x\")\n",
        "\n",
        "ax.grid(False)\n",
        "ax.axis(\"off\")\n",
        "\n",
        "ax.set_xlabel(\"u\")\n",
        "ax.set_ylabel(\"v\")\n",
        "\n",
        "ax.set_xlim(-3, 3)\n",
        "ax.set_ylim(-3, 3)\n",
        "ax.set_zlim(1, 4)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "11f874f9-a62b-49fe-99fc-bec52e2b618a",
      "metadata": {
        "id": "11f874f9-a62b-49fe-99fc-bec52e2b618a"
      },
      "source": [
        "## AMPL modeling of second-order cones\n",
        "\n",
        "The SOCO formulation given above needs to be reformulated one more time into [one of the standard forms](https://amplmp.readthedocs.io/en/latest/rst/model-guide.html):\n",
        "\n",
        "$$\\textstyle \\sum_{i} x_i^2 \\leq r^2, \\ r \\geq 0$$\n",
        "\n",
        "where the $x_i$ and $r$ terms are AMPL variables. The first step is to introduce rotated coordinates $t = x+ y$ and $v = x - y$, and (optionally) introduce a new variable with fixed value $u = 2$,\n",
        "\n",
        "$$\\begin{align*}\n",
        "\\min_{x, y}\\quad & f(x, y) = \\frac{h x}{2} + c d y \\\\\n",
        "\\text{s.t.} \\quad\n",
        "& t  = x + y \\\\\n",
        "& u = 2 \\\\\n",
        "& v = x - y \\\\\n",
        "& u^2 + v^2 \\leq t^2 \\\\\n",
        "& x, y, t, u, v \\geq 0\n",
        "\\end{align*}$$\n",
        "\n",
        "This version of the model with variables $t, u, v, x, y$ could be implemented directly in AMPL. However, the model can be further reduced to yield a simpler version of the model.\n",
        "\n",
        "$$\\begin{align*}\n",
        "\\min_{t, u, v}\\quad &  f(u, v) = \\frac{1}{4}\\left[(h + 2 cd)\\,t + (h - 2 cd)\\, v\\right] \\\\\n",
        "\\text{s.t.} \\quad\n",
        "& u = 2 \\\\\n",
        "& u^2 + v^2 \\leq t^2 \\\\\n",
        "& t, u, v \\geq 0\n",
        "\\end{align*}$$\n",
        "\n",
        "The EOQ model is now ready to implement. AMPL provides drivers for the Mosek and Gurobi commercial solvers (note that academic licenses are available at no cost). These drivers recognize the SOCO constraints and pass them to the solvers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "9336d832",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9336d832",
        "outputId": "49e8c62c-e2d5-44be-c505-a94ef6f8396b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting eoq_soc_basic.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile eoq_soc_basic.mod\n",
        "\n",
        "param h;      # cost of holding one item for one year\n",
        "param c;      # cost of processing one order\n",
        "param d;      # annual demand\n",
        "\n",
        "# define variables for conic constraints\n",
        "var u >= 0;\n",
        "var v >= 0;\n",
        "var t >= 0;\n",
        "\n",
        "# relationships for conic constraints to decision variables\n",
        "s.t. u_eq:\n",
        "    u == 2;\n",
        "\n",
        "# conic constraint\n",
        "s.t. q:\n",
        "    t^2 >= u^2 + v^2;\n",
        "\n",
        "# linear objective\n",
        "minimize eoq:\n",
        "    ((h + 2*c*d)*t + (h - 2*c*d)*v)/4;\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "a841982e-ff21-4182-88d7-0ae381d0a2f9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a841982e-ff21-4182-88d7-0ae381d0a2f9",
        "outputId": "33da9f8a-fd88-4970-ee37-d8f7aeab9a47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "EOQ = 3651.48\n"
          ]
        }
      ],
      "source": [
        "h = 0.75  # cost of holding one item for one year\n",
        "c = 500.0  # cost of processing one order\n",
        "d = 10000.0  # annual demand\n",
        "\n",
        "# Create AMPL instance and load the model\n",
        "ampl = AMPL()\n",
        "ampl.read(\"eoq_soc_basic.mod\")\n",
        "\n",
        "# load the data\n",
        "ampl.param[\"h\"] = h\n",
        "ampl.param[\"c\"] = c\n",
        "ampl.param[\"d\"] = d\n",
        "\n",
        "# solve\n",
        "ampl.solve(solver=SOLVER_NLO, verbose=False)\n",
        "assert ampl.solve_result == \"solved\", ampl.solve_result\n",
        "\n",
        "# solution\n",
        "print(f\"\\nEOQ = { ampl.get_value('(t + v)/2') :.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c5a9b97c-8602-4305-9c68-fcd7ee646fdd",
      "metadata": {
        "id": "c5a9b97c-8602-4305-9c68-fcd7ee646fdd"
      },
      "source": [
        "## Another standard SOCO representation\n",
        "\n",
        "AMPL conic solvers allow the [alternative SOCO representation](https://amplmp.readthedocs.io/en/latest/rst/model-guide.html) according to the definition of a Lorenz cone:\n",
        "\n",
        "$$ ||x||_2 <= r $$\n",
        "\n",
        "Moreover here we go all the way and don't use the auxiliary variable `u` fixed to a constant value. The required value can simply be inserted directly into constraint specification as demonstrated below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "8a8e8404",
      "metadata": {
        "id": "8a8e8404",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d026d3a8-604b-4aba-f923-87d05ca45e51"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting eoq_soc.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile eoq_soc.mod\n",
        "\n",
        "param h;      # cost of holding one item for one year\n",
        "param c;      # cost of processing one order\n",
        "param d;      # annual demand\n",
        "\n",
        "# define variables for conic constraints\n",
        "var v >= 0;\n",
        "var t >= 0;\n",
        "\n",
        "# conic constraint\n",
        "s.t. q:\n",
        "    -t <= -sqrt(2^2 + v^2);\n",
        "\n",
        "# linear objective\n",
        "minimize eoq:\n",
        "    ((h + 2*c*d)*t + (h - 2*c*d)*v)/4;\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "35f1a89c-7a19-4807-9946-8c291c15bc61",
      "metadata": {
        "scrolled": true,
        "id": "35f1a89c-7a19-4807-9946-8c291c15bc61",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "642bbac3-0a80-44b5-93cc-daea13e977b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "EOQ = 3651.45\n"
          ]
        }
      ],
      "source": [
        "h = 0.75  # cost of holding one item for one year\n",
        "c = 500.0  # cost of processing one order\n",
        "d = 10000.0  # annual demand\n",
        "\n",
        "# Create AMPL instance and load the model\n",
        "ampl = AMPL()\n",
        "ampl.read(\"eoq_soc.mod\")\n",
        "\n",
        "# load the data\n",
        "ampl.param[\"h\"] = h\n",
        "ampl.param[\"c\"] = c\n",
        "ampl.param[\"d\"] = d\n",
        "\n",
        "# solve\n",
        "ampl.solve(solver=SOLVER_NLO, verbose=False)\n",
        "assert ampl.solve_result == \"solved\", ampl.solve_result\n",
        "\n",
        "# solution\n",
        "print(f\"\\nEOQ = { ampl.get_value('(t + v)/2') :.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7534195b-67d8-475c-ba5f-5e799ae9d6ff",
      "metadata": {
        "id": "7534195b-67d8-475c-ba5f-5e799ae9d6ff"
      },
      "source": [
        "## AMPL modeling with rotated second-order cones\n",
        "\n",
        "The need to rotate the natural coordinates of the EOQ problem to fit the programming interface to standard cones is not a big stumbling block, but does raise the question of whether there is a more natural way to express hyperbolic or cone constraints in AMPL.\n",
        "\n",
        "Rotated second-order cones have the form\n",
        "\n",
        "$$\\sum_{i} x_i^2 \\leq 2 r_1 r_2 \\quad r_1, r_2 \\geq 0$$\n",
        "\n",
        "This enables a direct expression of the hyperbolic constraint $x y \\geq 1$ by introducing an optional auxiliary variable $z$ with fixed value $z^2 = 2$, such that\n",
        "\n",
        "$$xy \\geq 1 \\iff z^2 \\leq 2 x y \\quad\\text{where }z^2 = 2$$\n",
        "\n",
        "The model to be implemented in AMPL is now\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min_{x, y}\\quad & f(x, y) = \\frac{h x}{2} + c d y \\\\\n",
        "\\text{s.t.} \\quad\n",
        "& z^2 \\leq 2 x\\,y  \\\\\n",
        "& z = \\sqrt{2} \\\\\n",
        "& x, y > 0 \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "However the trick with the auxilary variable $z$ is optional and omitted in the implementation below, substituting the constant value $\\sqrt2$ directly. Note the improvement in accuracy of this calculation, compared to the previous solutions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "4df772e6",
      "metadata": {
        "id": "4df772e6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55c25e39-4742-45ad-a2b8-192a2c4137d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing eoq_rsoc.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile eoq_rsoc.mod\n",
        "\n",
        "param h;      # cost of holding one item for one year\n",
        "param c;      # cost of processing one order\n",
        "param d;      # annual demand\n",
        "\n",
        "# define variables for conic constraints\n",
        "var x >= 0;\n",
        "var y >= 0;\n",
        "\n",
        "# conic constraint\n",
        "s.t. q:\n",
        "    x*y >= 1;\n",
        "\n",
        "# linear objective\n",
        "minimize eoq:\n",
        "    h*x/2 + c*d*y;\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "7c19f989-5f75-4da0-98f6-d40f6a0a55e7",
      "metadata": {
        "id": "7c19f989-5f75-4da0-98f6-d40f6a0a55e7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92f95455-e953-408e-d4de-e97a5d1eb3f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MOSEK 11.0.8: \b\b\b\b\b\b\b\b\b\b\b\b\b\bMOSEK 11.0.8: optimal; objective 2738.612776\n",
            "0 simplex iterations\n",
            "14 barrier iterations\n",
            "\n",
            "EOQ = 3651.48\n"
          ]
        }
      ],
      "source": [
        "h = 0.75  # cost of holding one item for one year\n",
        "c = 500.0  # cost of processing one order\n",
        "d = 10000.0  # annual demand\n",
        "\n",
        "# Create AMPL instance and load the model\n",
        "ampl = AMPL()\n",
        "ampl.read(\"eoq_rsoc.mod\")\n",
        "\n",
        "# load the data\n",
        "ampl.param[\"h\"] = h\n",
        "ampl.param[\"c\"] = c\n",
        "ampl.param[\"d\"] = d\n",
        "\n",
        "# solve\n",
        "ampl.solve(solver=SOLVER_CONIC)\n",
        "assert ampl.solve_result == \"solved\", ampl.solve_result\n",
        "\n",
        "# solution\n",
        "print(f\"\\nEOQ = { ampl.get_value('x') :.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "160f0738-a418-4e06-828e-7afcd9234e5c",
      "metadata": {
        "tags": [],
        "id": "160f0738-a418-4e06-828e-7afcd9234e5c"
      },
      "source": [
        "## Extending the EOQ model to multiple items with a shared resource\n",
        "\n",
        "Solving for the EOQ for a single item using SOCO optimization is using a sledgehammer to swat a fly. However, the problem becomes more interesting for determining economic order quantities when the inventories for multiple items compete for a shared resource in a common warehouse. The shared resource could be the financing available to hold inventory, space in a warehouse, or specialized facilities to hold a perishable good:\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min \\quad & \\sum_{i=1}^n \\frac{h x_i}{2} + \\frac{c_i d_i}{x_i} \\\\\n",
        "\\text{s.t.} \\quad\n",
        "& \\sum_{i=1}^n b_i x_i  \\leq b_0 \\\\\n",
        "& 0 < lb_i \\leq x_i \\leq ub_i & \\forall i\\in 1, \\dots, n \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "where $h_i$ is the annual holding cost for one unit of item $i$, $c_i$ is the cost to place an order and receive delivery for item $i$, and $d_i$ is the annual demand. The additional constraint models an allocation of $b_i$ units of the shared resource, and $b_0$ is the total resource available.\n",
        "\n",
        "Following the reformulation of the single item model, a variable $y_i\n",
        "\\geq 0$, $i=1, \\dots, n$ is introduced to linearize the objective\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min \\quad & \\sum_{i=1}^n \\frac{h x_i}{2} + c_i d_i y_i \\\\\n",
        "\\text{s.t.} \\quad\n",
        "& \\sum_{i=1}^n b_i x_i  \\leq b_0 \\\\\n",
        "& x_i y_i \\geq 1 & \\forall i\\in 1, \\dots, n \\\\\n",
        "& 0 < lb_i \\leq x_i \\leq ub_i & \\forall i\\in 1, \\dots, n \\\\\n",
        "& y_i \\geq 0 & \\forall i\\in 1, \\dots, n \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "The following AMPL model is a direct implementation of the multi-item EOQ formulation and applied to a hypothetical car parts store that maintains an inventory of tires."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "82130122-d23b-46e0-bea5-5486d7de5d59",
      "metadata": {
        "id": "82130122-d23b-46e0-bea5-5486d7de5d59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "e5971436-3165-4e26-f3e3-6ba1b9b471bb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               h      c       d    b\n",
              "all weather  1.0  200.0  1300.0  3.0\n",
              "truck        2.8  250.0   700.0  8.0\n",
              "heavy duty   1.2  200.0   500.0  5.0\n",
              "low cost     0.8  180.0  2000.0  3.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f136496e-13ad-4afa-82cb-a69bb955c629\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>h</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>b</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>all weather</th>\n",
              "      <td>1.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>truck</th>\n",
              "      <td>2.8</td>\n",
              "      <td>250.0</td>\n",
              "      <td>700.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>heavy duty</th>\n",
              "      <td>1.2</td>\n",
              "      <td>200.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>low cost</th>\n",
              "      <td>0.8</td>\n",
              "      <td>180.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f136496e-13ad-4afa-82cb-a69bb955c629')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f136496e-13ad-4afa-82cb-a69bb955c629 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f136496e-13ad-4afa-82cb-a69bb955c629');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cc3c44a8-8720-4202-8590-75e42e91a426\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cc3c44a8-8720-4202-8590-75e42e91a426')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cc3c44a8-8720-4202-8590-75e42e91a426 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_dd4d800f-d702-4eed-be5e-a546adae70f6\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_dd4d800f-d702-4eed-be5e-a546adae70f6 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"h\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9146948489341494,\n        \"min\": 0.8,\n        \"max\": 2.8,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.8,\n          0.8,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.860788111948196,\n        \"min\": 180.0,\n        \"max\": 250.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          200.0,\n          250.0,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"d\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 675.1543033509698,\n        \"min\": 500.0,\n        \"max\": 2000.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          700.0,\n          2000.0,\n          1300.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.362907813126304,\n        \"min\": 3.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.0,\n          8.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame(\n",
        "    {\n",
        "        \"all weather\": {\"h\": 1.0, \"c\": 200, \"d\": 1300, \"b\": 3},\n",
        "        \"truck\": {\"h\": 2.8, \"c\": 250, \"d\": 700, \"b\": 8},\n",
        "        \"heavy duty\": {\"h\": 1.2, \"c\": 200, \"d\": 500, \"b\": 5},\n",
        "        \"low cost\": {\"h\": 0.8, \"c\": 180, \"d\": 2000, \"b\": 3},\n",
        "    }\n",
        ").T\n",
        "\n",
        "display(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "4ee787c6",
      "metadata": {
        "id": "4ee787c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0988f066-490c-4fd9-974e-2e6ced1765d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing eoq_multi_rsoc.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile eoq_multi_rsoc.mod\n",
        "\n",
        "set ITEMS;\n",
        "\n",
        "param b0;            # resource total\n",
        "param b{ITEMS};      # resource per unit of each item\n",
        "param h{ITEMS};      # cost of holding each item for one year\n",
        "param c{ITEMS};      # cost of processing one order\n",
        "param d{ITEMS};      # annual demand of each item\n",
        "\n",
        "# define variables for conic constraints\n",
        "var x {ITEMS} >= 0;\n",
        "var y {ITEMS} >= 0;\n",
        "\n",
        "# conic constraints\n",
        "s.t. q {i in ITEMS}:\n",
        "    x[i]*y[i] >= 1;\n",
        "\n",
        "# resource capacity\n",
        "s.t. r:\n",
        "    sum {i in ITEMS} (b[i]*x[i]) <= b0;\n",
        "\n",
        "# linear objective\n",
        "minimize eoq:\n",
        "    sum {i in ITEMS}\n",
        "        (h[i]*x[i]/2 + c[i]*d[i]*y[i]);\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "e81ee126-4613-4e25-9c6a-cd3c2b5d07b8",
      "metadata": {
        "scrolled": false,
        "id": "e81ee126-4613-4e25-9c6a-cd3c2b5d07b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6f4dc3d7-1232-4916-816f-1e9b9e9fdeec"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               h      c       d    b\n",
              "all weather  1.0  200.0  1300.0  3.0\n",
              "truck        2.8  250.0   700.0  8.0\n",
              "heavy duty   1.2  200.0   500.0  5.0\n",
              "low cost     0.8  180.0  2000.0  3.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2844bcc1-9740-4e51-9d32-cfb4f822801c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>h</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>b</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>all weather</th>\n",
              "      <td>1.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>truck</th>\n",
              "      <td>2.8</td>\n",
              "      <td>250.0</td>\n",
              "      <td>700.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>heavy duty</th>\n",
              "      <td>1.2</td>\n",
              "      <td>200.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>low cost</th>\n",
              "      <td>0.8</td>\n",
              "      <td>180.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2844bcc1-9740-4e51-9d32-cfb4f822801c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2844bcc1-9740-4e51-9d32-cfb4f822801c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2844bcc1-9740-4e51-9d32-cfb4f822801c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1bea8c16-0f59-4ec9-89a9-1544d1d705fe\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1bea8c16-0f59-4ec9-89a9-1544d1d705fe')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1bea8c16-0f59-4ec9-89a9-1544d1d705fe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_78adfbbd-bd3d-4057-8a9d-74455fc77809\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_78adfbbd-bd3d-4057-8a9d-74455fc77809 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"h\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9146948489341494,\n        \"min\": 0.8,\n        \"max\": 2.8,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.8,\n          0.8,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.860788111948196,\n        \"min\": 180.0,\n        \"max\": 250.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          200.0,\n          250.0,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"d\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 675.1543033509698,\n        \"min\": 500.0,\n        \"max\": 2000.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          700.0,\n          2000.0,\n          1300.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.362907813126304,\n        \"min\": 3.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.0,\n          8.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MOSEK 11.0.8: \b\b\b\b\b\b\b\b\b\b\b\b\b\bMOSEK 11.0.8: optimal; objective 4239.725347\n",
            "0 simplex iterations\n",
            "16 barrier iterations\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               EOQ  Space Req'd\n",
              "product                        \n",
              "all weather  306.2        918.7\n",
              "truck        153.2       1225.3\n",
              "heavy duty   151.0        754.9\n",
              "low cost     367.0       1101.1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c97584a4-e072-4e10-9b66-54413a268ebf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>EOQ</th>\n",
              "      <th>Space Req'd</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>all weather</th>\n",
              "      <td>306.2</td>\n",
              "      <td>918.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>truck</th>\n",
              "      <td>153.2</td>\n",
              "      <td>1225.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>heavy duty</th>\n",
              "      <td>151.0</td>\n",
              "      <td>754.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>low cost</th>\n",
              "      <td>367.0</td>\n",
              "      <td>1101.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c97584a4-e072-4e10-9b66-54413a268ebf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c97584a4-e072-4e10-9b66-54413a268ebf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c97584a4-e072-4e10-9b66-54413a268ebf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0a39ad11-0267-4135-bef5-9304903785bd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0a39ad11-0267-4135-bef5-9304903785bd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0a39ad11-0267-4135-bef5-9304903785bd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"eoq_display_results(df, m)\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"truck\",\n          \"low cost\",\n          \"all weather\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EOQ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 109.37853232391323,\n        \"min\": 151.0,\n        \"max\": 367.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          153.2,\n          367.0,\n          306.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Space Req'd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 206.28863274548112,\n        \"min\": 754.9,\n        \"max\": 1225.3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1225.3,\n          1101.1,\n          918.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "display(df)\n",
        "\n",
        "\n",
        "def eoq(df, b):\n",
        "    # Create AMPL instance and load the model\n",
        "    ampl = AMPL()\n",
        "    ampl.read(\"eoq_multi_rsoc.mod\")\n",
        "\n",
        "    # load the data\n",
        "    ampl.set[\"ITEMS\"] = list(df.index)\n",
        "    ampl.set_data(df, \"ITEMS\")\n",
        "\n",
        "    ampl.param[\"b0\"] = b\n",
        "\n",
        "    # solve\n",
        "    ampl.solve(solver=SOLVER_CONIC)\n",
        "    assert ampl.solve_result == \"solved\", ampl.solve_result\n",
        "\n",
        "    return ampl\n",
        "\n",
        "\n",
        "def eoq_display_results(df, ampl):\n",
        "    x = ampl.get_variable(\"x\")\n",
        "    dfx = x.to_dict()\n",
        "\n",
        "    results = pd.DataFrame(\n",
        "        [[i, dfx[i], dfx[i] * df.loc[i, \"b\"]] for i in df.index],\n",
        "        columns=[\"product\", \"EOQ\", \"Space Req'd\"],\n",
        "    ).round(1)\n",
        "    results.set_index(\"product\", inplace=True)\n",
        "\n",
        "    display(results)\n",
        "    results.plot(\n",
        "        y=[\"EOQ\", \"Space Req'd\"],\n",
        "        kind=\"bar\",\n",
        "        subplots=True,\n",
        "        layout=(2, 1),\n",
        "        figsize=(10, 6),\n",
        "    )\n",
        "\n",
        "\n",
        "m = eoq(df, 4000)\n",
        "eoq_display_results(df, m)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c8e5ac1e-8d87-4c59-b757-7282a5a84158",
      "metadata": {
        "id": "c8e5ac1e-8d87-4c59-b757-7282a5a84158"
      },
      "source": [
        "## Testing the model on larger problems\n",
        "\n",
        "The following cell creates a random EOQ problem of size $n$ that can be used to test the model formulation and solver."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "39b0e0f9-8f6d-48ba-b334-61a4d2856042",
      "metadata": {
        "id": "39b0e0f9-8f6d-48ba-b334-61a4d2856042",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8cdfa817-4db5-4a62-9d5d-f33c13a11e8f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MOSEK 11.0.8: \b\b\b\b\b\b\b\b\b\b\b\b\b\bMOSEK 11.0.8: optimal; objective 259301.485\n",
            "0 simplex iterations\n",
            "15 barrier iterations\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               EOQ  Space Req'd\n",
              "product                        \n",
              "product 000   93.2       3000.3\n",
              "product 001  129.0       4154.1\n",
              "product 002  139.4       4491.2\n",
              "product 003  106.7       3437.4\n",
              "product 004  116.3       3745.4\n",
              "product 005  121.9       3925.3\n",
              "product 006  146.7       4723.5\n",
              "product 007  132.7       4273.5\n",
              "product 008  117.3       3777.7\n",
              "product 009  112.5       3622.3\n",
              "product 010   73.5       2367.6\n",
              "product 011   74.7       2406.1\n",
              "product 012  160.0       5153.7\n",
              "product 013   77.1       2482.8\n",
              "product 014   63.0       2028.8\n",
              "product 015  119.9       3861.5\n",
              "product 016   96.8       3116.9\n",
              "product 017  106.2       3418.9\n",
              "product 018  138.6       4464.1\n",
              "product 019  110.4       3555.8\n",
              "product 020   53.9       1734.7\n",
              "product 021  122.9       3957.6\n",
              "product 022  101.4       3265.0\n",
              "product 023  102.4       3298.7\n",
              "product 024   79.6       2564.9\n",
              "product 025  118.7       3822.8\n",
              "product 026   30.7        990.0\n",
              "product 027  101.8       3278.4\n",
              "product 028   64.3       2070.7\n",
              "product 029   93.5       3010.3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3a20a7d5-446b-4c06-bbff-ff645606a2cb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>EOQ</th>\n",
              "      <th>Space Req'd</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>product 000</th>\n",
              "      <td>93.2</td>\n",
              "      <td>3000.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 001</th>\n",
              "      <td>129.0</td>\n",
              "      <td>4154.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 002</th>\n",
              "      <td>139.4</td>\n",
              "      <td>4491.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 003</th>\n",
              "      <td>106.7</td>\n",
              "      <td>3437.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 004</th>\n",
              "      <td>116.3</td>\n",
              "      <td>3745.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 005</th>\n",
              "      <td>121.9</td>\n",
              "      <td>3925.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 006</th>\n",
              "      <td>146.7</td>\n",
              "      <td>4723.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 007</th>\n",
              "      <td>132.7</td>\n",
              "      <td>4273.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 008</th>\n",
              "      <td>117.3</td>\n",
              "      <td>3777.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 009</th>\n",
              "      <td>112.5</td>\n",
              "      <td>3622.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 010</th>\n",
              "      <td>73.5</td>\n",
              "      <td>2367.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 011</th>\n",
              "      <td>74.7</td>\n",
              "      <td>2406.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 012</th>\n",
              "      <td>160.0</td>\n",
              "      <td>5153.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 013</th>\n",
              "      <td>77.1</td>\n",
              "      <td>2482.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 014</th>\n",
              "      <td>63.0</td>\n",
              "      <td>2028.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 015</th>\n",
              "      <td>119.9</td>\n",
              "      <td>3861.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 016</th>\n",
              "      <td>96.8</td>\n",
              "      <td>3116.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 017</th>\n",
              "      <td>106.2</td>\n",
              "      <td>3418.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 018</th>\n",
              "      <td>138.6</td>\n",
              "      <td>4464.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 019</th>\n",
              "      <td>110.4</td>\n",
              "      <td>3555.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 020</th>\n",
              "      <td>53.9</td>\n",
              "      <td>1734.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 021</th>\n",
              "      <td>122.9</td>\n",
              "      <td>3957.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 022</th>\n",
              "      <td>101.4</td>\n",
              "      <td>3265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 023</th>\n",
              "      <td>102.4</td>\n",
              "      <td>3298.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 024</th>\n",
              "      <td>79.6</td>\n",
              "      <td>2564.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 025</th>\n",
              "      <td>118.7</td>\n",
              "      <td>3822.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 026</th>\n",
              "      <td>30.7</td>\n",
              "      <td>990.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 027</th>\n",
              "      <td>101.8</td>\n",
              "      <td>3278.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 028</th>\n",
              "      <td>64.3</td>\n",
              "      <td>2070.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product 029</th>\n",
              "      <td>93.5</td>\n",
              "      <td>3010.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a20a7d5-446b-4c06-bbff-ff645606a2cb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3a20a7d5-446b-4c06-bbff-ff645606a2cb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3a20a7d5-446b-4c06-bbff-ff645606a2cb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2507fe14-bb48-45a5-bbcf-adac9b6750ee\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2507fe14-bb48-45a5-bbcf-adac9b6750ee')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2507fe14-bb48-45a5-bbcf-adac9b6750ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"eoq_display_results(df_large, m)\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"product 027\",\n          \"product 015\",\n          \"product 023\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EOQ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.35603660335419,\n        \"min\": 30.7,\n        \"max\": 160.0,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          101.8,\n          119.9,\n          102.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Space Req'd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 945.4138864971305,\n        \"min\": 990.0,\n        \"max\": 5153.7,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          3278.4,\n          3861.5,\n          3298.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "n = 30\n",
        "\n",
        "df_large = pd.DataFrame()\n",
        "df_large[\"h\"] = np.random.uniform(0.5, 2.0, n)\n",
        "df_large[\"c\"] = np.random.randint(300, 500, n)\n",
        "df_large[\"d\"] = np.random.randint(100, 5000, n)\n",
        "df_large[\"b\"] = np.random.uniform(10, 50)\n",
        "df_large.set_index(pd.Series(f\"product {i:03d}\" for i in range(n)), inplace=True)\n",
        "\n",
        "df_large\n",
        "\n",
        "m = eoq(df_large, 100000)\n",
        "eoq_display_results(df_large, m)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "41283c56-ca51-43d9-a38f-45b029a1a778",
      "metadata": {
        "id": "41283c56-ca51-43d9-a38f-45b029a1a778"
      },
      "source": [
        "## Bibliographic notes\n",
        "\n",
        "The original formulation and solution of the economic order quantity problem is attributed to Ford Harris, but in a curious twist has been [cited incorrectly since 1931](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.35.7.898). The correct citation is:\n",
        "\n",
        ">Harris, F. W. (1915). Operations and Cost (Factory Management Series). A. W. Shaw Company, Chap IV, pp.48-52. Chicago.\n",
        "\n",
        "Harris later developed an extensive consulting business and the concept has become embedded in business practice for over 100 years. Harris's single item model was later extended to multiple items sharing a resource constraint. There may be earlier citations, but this model is generally attributed to Ziegler (1982):\n",
        "\n",
        "> Ziegler, H. (1982). Solving certain singly constrained convex optimization problems in production planning. Operations Research Letters, 1(6), 246-252. https://www.sciencedirect.com/science/article/abs/pii/016763778290030X\n",
        "\n",
        "> Bretthauer, K. M., & Shetty, B. (1995). The nonlinear resource allocation problem. Operations research, 43(4), 670-683. https://www.jstor.org/stable/171693?seq=1\n",
        "\n",
        "Reformulation of the multi-item EOQ model as a conic optimization problem is attributed to Kuo and Mittleman (2004) using techniques described by Lobo, et al. (1998):\n",
        "\n",
        "> Kuo, Y. J., & Mittelmann, H. D. (2004). Interior point methods for second-order cone programming and OR applications. Computational Optimization and Applications, 28(3), 255-285. https://link.springer.com/content/pdf/10.1023/B:COAP.0000033964.95511.23.pdf\n",
        "\n",
        "> Lobo, M. S., Vandenberghe, L., Boyd, S., & Lebret, H. (1998). Applications of second-order cone programming. Linear algebra and its applications, 284(1-3), 193-228. https://web.stanford.edu/~boyd/papers/pdf/socp.pdf\n",
        "\n",
        "The multi-item model has been used didactically many times since 2004. These are representative examples\n",
        "\n",
        "> Letchford, A. N., & Parkes, A. J. (2018). A guide to conic optimisation and its applications. RAIRO-Operations Research, 52(4-5), 1087-1106. http://www.cs.nott.ac.uk/~pszajp/pubs/conic-guide.pdf\n",
        "\n",
        "> El Ghaoui, Laurent (2018). Lecture notes on Optimization Models. https://inst.eecs.berkeley.edu/~ee127/fa19/Lectures/12_socp.pdf\n",
        "\n",
        "> Mosek Modeling Cookbook, section 3.3.5.  https://docs.mosek.com/modeling-cookbook/cqo.html.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5defe068-293e-492e-ba83-eaee5e542791",
      "metadata": {
        "tags": [],
        "id": "5defe068-293e-492e-ba83-eaee5e542791"
      },
      "source": [
        "## Appendix: Formulation with SOCO constraints\n",
        "\n",
        "AMPL's facility for direct handling of hyperbolic constraints bypasses the need to formulate SOCO constraints for the multi-item model. For completeness, however, that development is included here.\n",
        "\n",
        "As a short cut to reformulating the model with conic constraints, note that a \"completion of square\" gives the needed substitutions\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "(x_i + y_i)^2  = x_i^2 + 2 x_i y_i + y_i^2 \\\\\n",
        "- (x_i - y_i)^2  = - x_i^2 + 2 x_i y_i - y_i^2 \\\\\n",
        "\\rule{6cm}{0.4pt} \\\\\n",
        "\\implies (x_i + y_i)^2 - (x_i - y_i)^2  = 4 x_i y_i \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "The multi-item EOQ model is now written with conic constraints\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min \\quad & \\sum_{i=1}^n \\frac{h x_i}{2} + c_i d_i y_i \\\\\n",
        "\\text{s.t.} \\quad\n",
        "& \\sum_{i=1}^n b_i x_i  \\leq b_0 \\\\\n",
        "& 4 + (x_i - y_i)^2 \\leq (x_i + y_i)^2 & \\forall i\\in 1, \\dots, n \\\\\n",
        "& 0 < lb_i \\leq x_i \\leq ub_i & \\forall i\\in 1, \\dots, n \\\\\n",
        "& y_i \\geq 0 & \\forall i\\in 1, \\dots, n \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "Variables $t_i$, $u_i$, and $v_i$ are introduced t complete the reformulation for implementation with AMPL/Mosek.\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min \\quad & \\sum_{i=1}^n \\frac{h x_i}{2} + c_i d_i y_i \\\\\n",
        "\\text{s.t.} \\quad\n",
        "& \\sum_{i=1}^n b_i x_i \\leq b_0 \\\\\n",
        "& t_i = x_i + y_i & \\forall i\\in 1, \\dots, n \\\\\n",
        "& u_i = 2 & \\forall i \\in 1, \\dots, n \\\\\n",
        "& v_i = x_i - y_i & \\forall i\\in 1, \\dots, n \\\\\n",
        "& u_i^2 + v_i^2 \\leq t_i^2 & \\forall i\\in 1, \\dots, n \\\\\n",
        "& 0 < lb_i \\leq x_i \\leq ub_i & \\forall i\\in 1, \\dots, n \\\\\n",
        "& t_i, u_i, v_i, y_i \\geq 0 & \\forall i\\in 1, \\dots, n \\\\\n",
        "\\end{align*}\n",
        "$$"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.6"
    },
    "colab": {
      "provenance": []
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "83b0ff676cf4492fa8c3b7f2d3b96713": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_157ac88738e64478945567a1351fd74c",
              "IPY_MODEL_7a265bd8f9124a20aed0ca2e937d59bc",
              "IPY_MODEL_aa71326de8284f04a78db8c0617d1086",
              "IPY_MODEL_60b768ce404e49a78babf352bcd1d352"
            ],
            "layout": "IPY_MODEL_8dcc3ed123554674a6e7b70feec50f6d"
          }
        },
        "157ac88738e64478945567a1351fd74c": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_8d06078489664deeb2484163a13075d2",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "AMPL License UUID (you can use free https://ampl.com/ce or https://ampl.com/courses licenses):\n"
                ]
              }
            ]
          }
        },
        "7a265bd8f9124a20aed0ca2e937d59bc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_e967021552af4133aaa67c329884f540"
            ],
            "layout": "IPY_MODEL_dc2d0565eea043e6b8b65f118caa555a"
          }
        },
        "aa71326de8284f04a78db8c0617d1086": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_4580e11d2df4473ca55f137e61a6f72d",
            "msg_id": "",
            "outputs": []
          }
        },
        "60b768ce404e49a78babf352bcd1d352": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_aea0193803af4a19bf782020a218f172",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "Licensed to AMPL Community Edition License for the AMPL Model Colaboratory (https://ampl.com/colab).\n"
                ]
              }
            ]
          }
        },
        "8dcc3ed123554674a6e7b70feec50f6d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e967021552af4133aaa67c329884f540": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "TextModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "TextModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "TextView",
            "continuous_update": true,
            "description": "License UUID:",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_ff3166b7055c4b1f8fa5c9e02f74cfc9",
            "placeholder": "​",
            "style": "IPY_MODEL_1f9f1a5168a847a88a6eb9aff21d2c0c",
            "value": ""
          }
        },
        "dc2d0565eea043e6b8b65f118caa555a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ff3166b7055c4b1f8fa5c9e02f74cfc9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1f9f1a5168a847a88a6eb9aff21d2c0c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": "initial"
          }
        },
        "8d06078489664deeb2484163a13075d2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4580e11d2df4473ca55f137e61a6f72d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "aea0193803af4a19bf782020a218f172": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}