{
    "cells": [
        {
            "cell_type": "markdown",
            "id": "b51d81ec-29ce-4538-b14a-d9cedced84e5",
            "metadata": {
                "tags": []
            },
            "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": 1,
            "id": "eea82369-66fa-45ca-8f9d-28e58ce41f79",
            "metadata": {},
            "outputs": [],
            "source": [
                "# install dependencies and select solver\n",
                "%pip install -q amplpy numpy pandas\n",
                "\n",
                "SOLVER_CONIC = \"mosek\"  # ipopt, mosek, gurobi, knitro\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": 2,
            "id": "924b77c7",
            "metadata": {
                "scrolled": true
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Please provide a valid license UUID. You can use a free https://ampl.com/ce license.\n"
                    ]
                },
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "5d202256df4f4fd6815d0b9ccacffa6a",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "VBox(children=(Output(), HBox(children=(Text(value='', description='License UUID:', style=TextStyle(descriptio…"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "import numpy as np\n",
                "import pandas as pd"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "1ae49489-5c8a-4841-ad23-4573e1a1132f",
            "metadata": {},
            "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": 3,
            "id": "1b1df390-8b08-46b6-9c0f-e5c85329e7fc",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Optimal order size = 3651.5 items with cost 2738.61\n"
                    ]
                },
                {
                    "data": {
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",
                        "text/plain": [
                            "<Figure size 800x600 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "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": {},
            "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": 4,
            "id": "f2aeb40f-6f33-4776-b38e-dbb1aad57895",
            "metadata": {},
            "outputs": [
                {
                    "data": {
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",
                        "text/plain": [
                            "<Figure size 800x600 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "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": {},
            "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": 5,
            "id": "e4ece0b0-021d-4a79-941a-699a752e5891",
            "metadata": {},
            "outputs": [
                {
                    "data": {
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",
                        "text/plain": [
                            "<Figure size 900x700 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "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": {},
            "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": 6,
            "id": "9336d832",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "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": 7,
            "id": "a841982e-ff21-4182-88d7-0ae381d0a2f9",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "MOSEK 10.0.43: MOSEK 10.0.43: unknown (0, problem status: 0), stalling\n",
                        "0 simplex iterations\n",
                        "15 barrier iterations\n",
                        "\n",
                        "EOQ = 3653.04\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.option[\"solver\"] = SOLVER_CONIC\n",
                "ampl.solve()\n",
                "\n",
                "\n",
                "# solution\n",
                "print(f\"\\nEOQ = { ampl.get_value('(t + v)/2') :.2f}\")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "c5a9b97c-8602-4305-9c68-fcd7ee646fdd",
            "metadata": {},
            "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": 8,
            "id": "8a8e8404",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "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": 9,
            "id": "35f1a89c-7a19-4807-9946-8c291c15bc61",
            "metadata": {
                "scrolled": true
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "MOSEK 10.0.43: MOSEK 10.0.43: unknown (0, problem status: 0), stalling\n",
                        "0 simplex iterations\n",
                        "15 barrier iterations\n",
                        "\n",
                        "EOQ = 3653.04\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.option[\"solver\"] = SOLVER_CONIC\n",
                "ampl.solve()\n",
                "\n",
                "\n",
                "# solution\n",
                "print(f\"\\nEOQ = { ampl.get_value('(t + v)/2') :.2f}\")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "7534195b-67d8-475c-ba5f-5e799ae9d6ff",
            "metadata": {},
            "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": 10,
            "id": "4df772e6",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Overwriting 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": 11,
            "id": "7c19f989-5f75-4da0-98f6-d40f6a0a55e7",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "MOSEK 10.0.43: MOSEK 10.0.43: optimal; objective 2738.612791\n",
                        "0 simplex iterations\n",
                        "10 barrier iterations\n",
                        "\n",
                        "EOQ = 3651.46\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.option[\"solver\"] = SOLVER_CONIC\n",
                "ampl.solve()\n",
                "\n",
                "\n",
                "# solution\n",
                "print(f\"\\nEOQ = { ampl.get_value('x') :.2f}\")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "160f0738-a418-4e06-828e-7afcd9234e5c",
            "metadata": {
                "tags": []
            },
            "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": 12,
            "id": "82130122-d23b-46e0-bea5-5486d7de5d59",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<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>"
                        ],
                        "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"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "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": 13,
            "id": "4ee787c6",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Overwriting 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": 14,
            "id": "e81ee126-4613-4e25-9c6a-cd3c2b5d07b8",
            "metadata": {
                "scrolled": false
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<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>"
                        ],
                        "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"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "MOSEK 10.0.43: MOSEK 10.0.43: optimal; objective 4239.725348\n",
                        "0 simplex iterations\n",
                        "16 barrier iterations\n"
                    ]
                },
                {
                    "data": {
                        "text/html": [
                            "<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>"
                        ],
                        "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"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1000x600 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "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.option[\"solver\"] = SOLVER_CONIC\n",
                "    ampl.solve()\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": {},
            "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": 15,
            "id": "39b0e0f9-8f6d-48ba-b334-61a4d2856042",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "MOSEK 10.0.43: MOSEK 10.0.43: optimal; objective 252756.6403\n",
                        "0 simplex iterations\n",
                        "16 barrier iterations\n"
                    ]
                },
                {
                    "data": {
                        "text/html": [
                            "<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>92.1</td>\n",
                            "      <td>2958.3</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 001</th>\n",
                            "      <td>64.6</td>\n",
                            "      <td>2075.1</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 002</th>\n",
                            "      <td>23.1</td>\n",
                            "      <td>742.9</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 003</th>\n",
                            "      <td>110.9</td>\n",
                            "      <td>3562.1</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 004</th>\n",
                            "      <td>123.1</td>\n",
                            "      <td>3952.4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 005</th>\n",
                            "      <td>150.4</td>\n",
                            "      <td>4830.2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 006</th>\n",
                            "      <td>58.8</td>\n",
                            "      <td>1886.9</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 007</th>\n",
                            "      <td>109.6</td>\n",
                            "      <td>3518.3</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 008</th>\n",
                            "      <td>116.8</td>\n",
                            "      <td>3751.3</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 009</th>\n",
                            "      <td>119.0</td>\n",
                            "      <td>3823.2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 010</th>\n",
                            "      <td>96.8</td>\n",
                            "      <td>3107.7</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 011</th>\n",
                            "      <td>109.6</td>\n",
                            "      <td>3519.6</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 012</th>\n",
                            "      <td>112.8</td>\n",
                            "      <td>3623.5</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 013</th>\n",
                            "      <td>110.9</td>\n",
                            "      <td>3562.2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 014</th>\n",
                            "      <td>129.5</td>\n",
                            "      <td>4160.2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 015</th>\n",
                            "      <td>98.1</td>\n",
                            "      <td>3152.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 016</th>\n",
                            "      <td>69.8</td>\n",
                            "      <td>2243.2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 017</th>\n",
                            "      <td>148.0</td>\n",
                            "      <td>4752.9</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 018</th>\n",
                            "      <td>88.3</td>\n",
                            "      <td>2834.6</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 019</th>\n",
                            "      <td>121.6</td>\n",
                            "      <td>3905.3</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 020</th>\n",
                            "      <td>117.3</td>\n",
                            "      <td>3765.9</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 021</th>\n",
                            "      <td>90.8</td>\n",
                            "      <td>2916.2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 022</th>\n",
                            "      <td>86.0</td>\n",
                            "      <td>2763.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 023</th>\n",
                            "      <td>146.5</td>\n",
                            "      <td>4703.9</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 024</th>\n",
                            "      <td>124.3</td>\n",
                            "      <td>3991.4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 025</th>\n",
                            "      <td>38.9</td>\n",
                            "      <td>1250.4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 026</th>\n",
                            "      <td>164.5</td>\n",
                            "      <td>5282.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 027</th>\n",
                            "      <td>134.3</td>\n",
                            "      <td>4311.7</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 028</th>\n",
                            "      <td>49.1</td>\n",
                            "      <td>1576.4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>product 029</th>\n",
                            "      <td>108.3</td>\n",
                            "      <td>3477.0</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "               EOQ  Space Req'd\n",
                            "product                        \n",
                            "product 000   92.1       2958.3\n",
                            "product 001   64.6       2075.1\n",
                            "product 002   23.1        742.9\n",
                            "product 003  110.9       3562.1\n",
                            "product 004  123.1       3952.4\n",
                            "product 005  150.4       4830.2\n",
                            "product 006   58.8       1886.9\n",
                            "product 007  109.6       3518.3\n",
                            "product 008  116.8       3751.3\n",
                            "product 009  119.0       3823.2\n",
                            "product 010   96.8       3107.7\n",
                            "product 011  109.6       3519.6\n",
                            "product 012  112.8       3623.5\n",
                            "product 013  110.9       3562.2\n",
                            "product 014  129.5       4160.2\n",
                            "product 015   98.1       3152.0\n",
                            "product 016   69.8       2243.2\n",
                            "product 017  148.0       4752.9\n",
                            "product 018   88.3       2834.6\n",
                            "product 019  121.6       3905.3\n",
                            "product 020  117.3       3765.9\n",
                            "product 021   90.8       2916.2\n",
                            "product 022   86.0       2763.0\n",
                            "product 023  146.5       4703.9\n",
                            "product 024  124.3       3991.4\n",
                            "product 025   38.9       1250.4\n",
                            "product 026  164.5       5282.0\n",
                            "product 027  134.3       4311.7\n",
                            "product 028   49.1       1576.4\n",
                            "product 029  108.3       3477.0"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
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",
                        "text/plain": [
                            "<Figure size 1000x600 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "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": {},
            "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"
            ]
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                "## 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",
                "$$\n",
                "\\begin{align*}\n",
                "\\min \\quad & \\sum_{i=1}^n \\frac{h x_i}{2} + c_i d_i y_i \\\\\n",
                "\\text{s.t.} \\quad \n",
                "& \\sum_{i=1}^n b_i x_i \\leq b_0 \\\\\n",
                "& t_i = x_i + y_i & \\forall i\\in 1, \\dots, n \\\\\n",
                "& u_i = 2 & \\forall i \\in 1, \\dots, n \\\\\n",
                "& v_i = x_i - y_i & \\forall i\\in 1, \\dots, n \\\\\n",
                "& u_i^2 + v_i^2 \\leq t_i^2 & \\forall i\\in 1, \\dots, n \\\\\n",
                "& 0 < lb_i \\leq x_i \\leq ub_i & \\forall i\\in 1, \\dots, n \\\\\n",
                "& t_i, u_i, v_i, y_i \\geq 0 & \\forall i\\in 1, \\dots, n \\\\\n",
                "\\end{align*}\n",
                "$$"
            ]
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