{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "BgpJa9pthV3-" }, "source": [ "```{index} single: solver; cbc\n", "```\n", "```{index} single: solver; cvxpy\n", "```\n", "```{index} single: solver; highs\n", "```\n", "\n", "# Extra material: Refinery production and shadow pricing with CVXPY\n", "\n", "This is a simple linear optimization problem in six variables, but with four equality constraints it allows for a graphical explanation of some unusually large shadow prices for manufacturing capacity. The notebook presents also contrasts AMPL with CVXPY modeling." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "immZxWu_qaff", "outputId": "4b7675bc-207d-42c7-b8e3-d3cba434910b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hUsing default Community Edition License for Colab. Get yours at: https://ampl.com/ce\n", "Licensed to AMPL Community Edition License for the AMPL Model Colaboratory (https://colab.ampl.com).\n" ] } ], "source": [ "# install AMPL and solvers\n", "%pip install -q amplpy\n", "\n", "SOLVER = \"cbc\"\n", "\n", "from amplpy import AMPL, ampl_notebook\n", "\n", "ampl = ampl_notebook(\n", " modules=[\"coin\"], # modules to install\n", " license_uuid=\"default\", # license to use\n", ") # instantiate AMPL object and register magics" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "_BOJTa5gqafi" }, "outputs": [], "source": [ "%pip install -q cvxpy" ] }, { "cell_type": "markdown", "metadata": { "id": "s4U0ctEDqafj" }, "source": [ "This example derived from Example 19.3 from Seborg, Edgar, Mellichamp, and Doyle.\n", "\n", "> Seborg, Dale E., Thomas F. Edgar, Duncan A. Mellichamp, and Francis J. Doyle III. Process dynamics and control. John Wiley & Sons, 2016.\n", "\n", "The changes include updating prices, new solutions using optimization modeling languages, adding constraints, and adjusting parameter values to demonstrate the significance of duals and their interpretation as shadow prices." ] }, { "cell_type": "markdown", "metadata": { "id": "Slbpo7Joqafk" }, "source": [ "## Problem data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 365 }, "id": "pogEPVlQgbuT", "outputId": "2ef99c23-ef7e-4d5d-e72e-12f580d5da8b" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " capacity price\n", "gasoline 24000 108\n", "kerosine 2000 72\n", "fuel oil 6000 63\n", "residual 2500 30" ], "text/html": [ "\n", "
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capacityprice
gasoline24000108
kerosine200072
fuel oil600063
residual250030
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availablepriceprocess_cost
crude 128000.072.01.5
crude 215000.045.03.0
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gasolinekerosinefuel oilresidual
crude 1805105
crude 244103610
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\n" ] }, "metadata": {} } ], "source": [ "import pandas as pd\n", "\n", "products = pd.DataFrame(\n", " {\n", " \"gasoline\": {\"capacity\": 24000, \"price\": 108},\n", " \"kerosine\": {\"capacity\": 2000, \"price\": 72},\n", " \"fuel oil\": {\"capacity\": 6000, \"price\": 63},\n", " \"residual\": {\"capacity\": 2500, \"price\": 30},\n", " }\n", ").T\n", "\n", "crudes = pd.DataFrame(\n", " {\n", " \"crude 1\": {\"available\": 28000, \"price\": 72, \"process_cost\": 1.5},\n", " \"crude 2\": {\"available\": 15000, \"price\": 45, \"process_cost\": 3},\n", " }\n", ").T\n", "\n", "# note: volumetric yields may not add to 100%\n", "yields = pd.DataFrame(\n", " {\n", " \"crude 1\": {\"gasoline\": 80, \"kerosine\": 5, \"fuel oil\": 10, \"residual\": 5},\n", " \"crude 2\": {\"gasoline\": 44, \"kerosine\": 10, \"fuel oil\": 36, \"residual\": 10},\n", " }\n", ").T\n", "\n", "display(products)\n", "display(crudes)\n", "display(yields)" ] }, { "cell_type": "markdown", "metadata": { "id": "Mx0f8UVO6wu3" }, "source": [ "## AMPL Model" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uhFeemwyqafn", "outputId": "31890b00-eeee-47e9-b462-4579bcdfd5ff" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Profit: 860275.86\n", "\n" ] } ], "source": [ "m = AMPL()\n", "\n", "m.eval(\n", " \"\"\"\n", " set CRUDES;\n", " set PRODUCTS;\n", "\n", " param price{PRODUCTS union CRUDES};\n", " param process_cost{CRUDES};\n", " param yields{PRODUCTS, CRUDES};\n", " param available{CRUDES};\n", " param capacity{PRODUCTS};\n", "\n", " # decision variables\n", " var x{c in CRUDES} >= 0 <= available[c];\n", " var y{p in PRODUCTS} >= 0 <= capacity[p];\n", "\n", " # objective\n", " var revenue = sum{p in PRODUCTS} price[p]*y[p];\n", " var feed_cost = sum{c in CRUDES} price[c]*x[c];\n", " var total_process_cost = sum{c in CRUDES} process_cost[c]*x[c];\n", "\n", " maximize profit: revenue - feed_cost - total_process_cost;\n", "\n", " # constraints\n", " subject to balances{p in PRODUCTS}:\n", " y[p] = sum{c in CRUDES} yields[p,c]*x[c] / 100;\n", "\"\"\"\n", ")\n", "\n", "m.set_data(crudes, \"CRUDES\")\n", "m.set_data(products, \"PRODUCTS\")\n", "m.param[\"yields\"] = yields.T\n", "\n", "# solution\n", "m.option[\"solver\"] = SOLVER\n", "m.solve(verbose=False)\n", "print(f\"Profit: {m.obj['profit'].value():0.2f}\\n\")" ] }, { "cell_type": "markdown", "metadata": { "id": "0-l7-qpxZs7Y" }, "source": [ "## CVXPY Model\n", "\n", "The `CVXPY` library for disciplined convex optimization is tightly integrated with `numpy`, the standard Python library for the numerical linear algebra. For example, where `AMPL` uses explicit indexing in constraints, summations, and other objects, `CVXPY` uses the implicit indexing implied when doing matrix and vector operations.\n", "\n", "Another sharp contrast with `AMPL` is that `CXVPY` has no specific object to describe a set,or to define a objects variables or other modeling objects over arbitrary sets. `CVXPY` instead uses the zero-based indexing familiar to Python users.\n", "\n", "The following cell demonstrates these differences by presenting a `CVXPY` model for the small refinery example." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ou4iqNTogpvd", "outputId": "babec380-5652-4ba9-d047-c2fc9438b179" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "860275.8615603454" ] }, "metadata": {}, "execution_count": 6 } ], "source": [ "import numpy as np\n", "import cvxpy as cp\n", "\n", "# decision variables\n", "x = cp.Variable(len(crudes.index), pos=True, name=\"crudes\")\n", "y = cp.Variable(len(products.index), pos=True, name=\"products\")\n", "\n", "# objective\n", "revenue = products[\"price\"].to_numpy().T @ y\n", "feed_cost = crudes[\"price\"].to_numpy().T @ x\n", "process_cost = crudes[\"process_cost\"].to_numpy().T @ x\n", "profit = revenue - feed_cost - process_cost\n", "objective = cp.Maximize(profit)\n", "\n", "# constraints\n", "balances = y == yields.to_numpy().T @ x / 100\n", "feeds = x <= crudes[\"available\"].to_numpy()\n", "capacity = y <= products[\"capacity\"].to_numpy()\n", "constraints = [balances, feeds, capacity]\n", "\n", "# solution\n", "problem = cp.Problem(objective, constraints)\n", "problem.solve()" ] }, { "cell_type": "markdown", "metadata": { "id": "rc7Bvxy8VCX7" }, "source": [ "## Crude oil feed results" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "kgFALj_rK1b8", "outputId": "4579b6c4-28d6-4398-cb88-7b2a5e7cc6e3" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " available price process_cost consumption shadow price\n", "crude 1 28000.0 72.0 1.5 26206.9 0.0\n", "crude 2 15000.0 45.0 3.0 6896.6 0.0" ], "text/html": [ "\n", "
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availablepriceprocess_costconsumptionshadow price
crude 128000.072.01.526206.90.0
crude 215000.045.03.06896.60.0
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capacitypriceproductionunused capacityshadow price
gasoline2400010824000.00.014.0
kerosine2000722000.00.0262.6
fuel oil6000635103.4896.60.0
residual2500302000.0500.00.0
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\n" ] }, "metadata": {} } ], "source": [ "results_products = products\n", "results_products[\"production\"] = y.value\n", "results_products[\"unused capacity\"] = products[\"capacity\"] - y.value\n", "results_products[\"shadow price\"] = capacity.dual_value\n", "\n", "display(results_products.round(1))" ] }, { "cell_type": "markdown", "metadata": { "id": "qvaoi0nbYM6P" }, "source": [ "## Why is the shadow price of kerosine so high?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 582 }, "id": "ebwOwbkbYSRt", "outputId": "e79b8ccc-7bbb-4dc0-d54a-44239cb0f820" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(0.0, 24000.0)" ] }, "metadata": {}, "execution_count": 9 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "fig, ax = plt.subplots(figsize=(8, 6))\n", "ylim = 24000\n", "xlim = 32000\n", "\n", "ax.axvline(crudes[\"available\"][0], linestyle=\"--\", label=\"Crude 1\")\n", "ax.axhline(crudes[\"available\"][1], linestyle=\"--\", label=\"Crude 2\")\n", "\n", "xplot = np.linspace(0, xlim)\n", "for product in products.index:\n", " b = 100 * products.loc[product, \"capacity\"] / yields[product][1]\n", " m = -yields[product][0] / yields[product][1]\n", " line = ax.plot(xplot, m * xplot + b, label=product)\n", " ax.fill_between(xplot, m * xplot + b, 30000, color=line[0].get_color(), alpha=0.2)\n", "\n", "ax.plot(x.value[0], x.value[1], \"ro\", ms=10, label=\"max profit\")\n", "ax.set_title(\"Feasible operating regime\")\n", "ax.set_xlabel(crudes.index[0])\n", "ax.set_ylabel(crudes.index[1])\n", "ax.legend()\n", "ax.set_xlim(0, xlim)\n", "ax.set_ylim(0, ylim)" ] }, { "cell_type": "markdown", "metadata": { "id": "sTzw6XBdgLOB" }, "source": [ "## Suggested Exercises\n", "\n", "1. Suppose the refinery makes a substantial investment to double kerosene production in order to increase profits. What becomes the limiting constraint?\n", "\n", "2. How do prices of crude oil and refinery products change the location of the optimum operating point?\n", "\n", "2. A refinery is a financial asset for the conversion of commodity crude oils into commodity hydrocarbons. What economic value can be assigned to owning the option to convert crude oils into other commodities?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "_hyca6crqaft" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "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": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 0 }