{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BgpJa9pthV3-"
   },
   "source": [
    "```{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 Pyomo with CVXPY modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preamble: Install Pyomo and a solver\n",
    "\n",
    "The following cell sets and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and the HiGHS solver, while, if run elsewhere, it assumes Pyomo and HiGHS have been previously installed. It then sets to use HiGHS as solver via the appsi module and a test is performed to verify that it is available. The solver interface is stored in a global object `SOLVER` for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    " \n",
    "if 'google.colab' in sys.modules:\n",
    "    %pip install pyomo >/dev/null 2>/dev/null\n",
    "    %pip install highspy >/dev/null 2>/dev/null\n",
    " \n",
    "solver = 'appsi_highs'\n",
    "\n",
    "import pyomo.environ as pyo\n",
    "SOLVER = pyo.SolverFactory(solver)\n",
    "\n",
    "assert SOLVER.available(), f\"Solver {solver} is not available.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q cvxpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BgpJa9pthV3-"
   },
   "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": {},
   "source": [
    "## Problem data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 365
    },
    "executionInfo": {
     "elapsed": 303,
     "status": "ok",
     "timestamp": 1649679696767,
     "user": {
      "displayName": "Jeffrey Kantor",
      "userId": "09038942003589296665"
     },
     "user_tz": 240
    },
    "id": "pogEPVlQgbuT",
    "outputId": "8f70dadc-1ae4-4b98-ece2-3c48f5f12878"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\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>capacity</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gasoline</th>\n",
       "      <td>24000</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kerosine</th>\n",
       "      <td>2000</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel oil</th>\n",
       "      <td>6000</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residual</th>\n",
       "      <td>2500</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          capacity  price\n",
       "gasoline     24000    108\n",
       "kerosine      2000     72\n",
       "fuel oil      6000     63\n",
       "residual      2500     30"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>available</th>\n",
       "      <th>price</th>\n",
       "      <th>process cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crude 1</th>\n",
       "      <td>28000.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crude 2</th>\n",
       "      <td>15000.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         available  price  process cost\n",
       "crude 1    28000.0   72.0           1.5\n",
       "crude 2    15000.0   45.0           3.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>gasoline</th>\n",
       "      <th>kerosine</th>\n",
       "      <th>fuel oil</th>\n",
       "      <th>residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crude 1</th>\n",
       "      <td>80</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crude 2</th>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         gasoline  kerosine  fuel oil  residual\n",
       "crude 1        80         5        10         5\n",
       "crude 2        44        10        36        10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cvxpy as cp\n",
    "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": [
    "## Pyomo Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "3NBWq4z86vkW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "860275.832\n"
     ]
    }
   ],
   "source": [
    "m = pyo.ConcreteModel()\n",
    "\n",
    "m.CRUDES = pyo.Set(initialize=crudes.index)\n",
    "m.PRODUCTS = pyo.Set(initialize=products.index)\n",
    "\n",
    "# decision variables\n",
    "m.x = pyo.Var(m.CRUDES, domain=pyo.NonNegativeReals)\n",
    "m.y = pyo.Var(m.PRODUCTS, domain=pyo.NonNegativeReals)\n",
    "\n",
    "\n",
    "# objective\n",
    "@m.Expression()\n",
    "def revenue(m):\n",
    "    return sum(products.loc[p, \"price\"] * m.y[p] for p in m.PRODUCTS)\n",
    "\n",
    "\n",
    "@m.Expression()\n",
    "def feed_cost(m):\n",
    "    return sum(crudes.loc[c, \"price\"] * m.x[c] for c in m.CRUDES)\n",
    "\n",
    "\n",
    "@m.Expression()\n",
    "def process_cost(m):\n",
    "    return sum(crudes.loc[c, \"process cost\"] * m.x[c] for c in m.CRUDES)\n",
    "\n",
    "\n",
    "@m.Objective(sense=pyo.maximize)\n",
    "def profit(m):\n",
    "    return m.revenue - m.feed_cost - m.process_cost\n",
    "\n",
    "\n",
    "# constraints\n",
    "@m.Constraint(m.PRODUCTS)\n",
    "def balances(m, p):\n",
    "    return m.y[p] == sum(yields.loc[c, p] * m.x[c] for c in m.CRUDES) / 100\n",
    "\n",
    "\n",
    "@m.Constraint(m.CRUDES)\n",
    "def feeds(m, c):\n",
    "    return m.x[c] <= crudes.loc[c, \"available\"]\n",
    "\n",
    "\n",
    "@m.Constraint(m.PRODUCTS)\n",
    "def capacity(m, p):\n",
    "    return m.y[p] <= products.loc[p, \"capacity\"]\n",
    "\n",
    "\n",
    "SOLVER.solve(m)\n",
    "print(round(m.profit(), 3))"
   ]
  },
  {
   "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 `Pyomo` 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 `Pyomo` 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": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 286,
     "status": "ok",
     "timestamp": 1649686878638,
     "user": {
      "displayName": "Jeffrey Kantor",
      "userId": "09038942003589296665"
     },
     "user_tz": 240
    },
    "id": "ou4iqNTogpvd",
    "outputId": "f0374e13-a30c-4e2c-b55f-83ec25a84654"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "860275.8620663473"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "executionInfo": {
     "elapsed": 328,
     "status": "ok",
     "timestamp": 1649686883612,
     "user": {
      "displayName": "Jeffrey Kantor",
      "userId": "09038942003589296665"
     },
     "user_tz": 240
    },
    "id": "kgFALj_rK1b8",
    "outputId": "13077e14-2051-4cae-b3af-57be582e9425"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>available</th>\n",
       "      <th>price</th>\n",
       "      <th>process cost</th>\n",
       "      <th>consumption</th>\n",
       "      <th>shadow price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crude 1</th>\n",
       "      <td>28000.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>26206.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crude 2</th>\n",
       "      <td>15000.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6896.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_crudes = crudes\n",
    "results_crudes[\"consumption\"] = x.value\n",
    "results_crudes[\"shadow price\"] = feeds.dual_value\n",
    "\n",
    "display(results_crudes.round(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KbaNCZtRVQB5"
   },
   "source": [
    "## Refinery production results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1649679830703,
     "user": {
      "displayName": "Jeffrey Kantor",
      "userId": "09038942003589296665"
     },
     "user_tz": 240
    },
    "id": "lem9nBSThVoj",
    "outputId": "c66f2d1d-f5a4-4086-b0f9-a48ba976f048"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>capacity</th>\n",
       "      <th>price</th>\n",
       "      <th>production</th>\n",
       "      <th>unused capacity</th>\n",
       "      <th>shadow price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gasoline</th>\n",
       "      <td>24000</td>\n",
       "      <td>108</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kerosine</th>\n",
       "      <td>2000</td>\n",
       "      <td>72</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>262.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel oil</th>\n",
       "      <td>6000</td>\n",
       "      <td>63</td>\n",
       "      <td>5103.4</td>\n",
       "      <td>896.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residual</th>\n",
       "      <td>2500</td>\n",
       "      <td>30</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          capacity  price  production  unused capacity  shadow price\n",
       "gasoline     24000    108     24000.0              0.0          14.0\n",
       "kerosine      2000     72      2000.0              0.0         262.6\n",
       "fuel oil      6000     63      5103.4            896.6           0.0\n",
       "residual      2500     30      2000.0            500.0           0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "executionInfo": {
     "elapsed": 1303,
     "status": "ok",
     "timestamp": 1649679998701,
     "user": {
      "displayName": "Jeffrey Kantor",
      "userId": "09038942003589296665"
     },
     "user_tz": 240
    },
    "id": "ebwOwbkbYSRt",
    "outputId": "bb2d121c-9fc2-4e01-a59b-f83b9bf6d913"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 24000.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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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",
    "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?"
   ]
  }
 ],
 "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}