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   "metadata": {
    "id": "BgpJa9pthV3-"
   },
   "source": [
    "```{index} single: solver; cbc\n",
    "```\n",
    "```{index} single: solver; highs\n",
    "```\n",
    "\n",
    "# Extra material: Refinery production and shadow pricing\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "immZxWu_qaff",
    "outputId": "243f18b0-fa1d-4bb5-9311-cdb8390c3b3e"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Using 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://ampl.com/colab).\n"
     ]
    }
   ],
   "source": [
    "# install AMPL and solvers\n",
    "%pip install -q amplpy pandas numpy matplotlib\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": "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"
   ]
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    "id": "pogEPVlQgbuT",
    "outputId": "e3083218-5fa3-42e4-badf-5c194bd85757"
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   "outputs": [
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       "          capacity  price\n",
       "gasoline     24000    108\n",
       "kerosine      2000     72\n",
       "fuel oil      6000     63\n",
       "residual      2500     30"
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     "data": {
      "text/plain": [
       "         available  price  process_cost\n",
       "crude 1    28000.0   72.0           1.5\n",
       "crude 2    15000.0   45.0           3.0"
      ],
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      "text/plain": [
       "         gasoline  kerosine  fuel oil  residual\n",
       "crude 1        80         5        10         5\n",
       "crude 2        44        10        36        10"
      ],
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       "type": "dataframe",
       "variable_name": "yields",
       "summary": "{\n  \"name\": \"yields\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"gasoline\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 44,\n        \"max\": 80,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          44,\n          80\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kerosine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 5,\n        \"max\": 10,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          10,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fuel oil\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 10,\n        \"max\": 36,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          36,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 5,\n        \"max\": 10,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          10,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\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",
   "source": [
    "%%writefile refinery.mod\n",
    "\n",
    "set CRUDES;\n",
    "set PRODUCTS;\n",
    "\n",
    "param price {PRODUCTS union CRUDES};   # selling price for products, purchase price for crudes\n",
    "param process_cost {CRUDES};\n",
    "param yields {PRODUCTS, CRUDES};       # percent yields (0..100)\n",
    "param available {CRUDES};\n",
    "param capacity  {PRODUCTS};\n",
    "\n",
    "# decision variables\n",
    "var x {c in CRUDES}   >= 0;            # crude consumption\n",
    "var y {p in PRODUCTS} >= 0;            # product production\n",
    "\n",
    "# objective\n",
    "maximize profit:\n",
    "    sum {p in PRODUCTS} price[p] * y[p]\n",
    "  - sum {c in CRUDES}   price[c] * x[c]\n",
    "  - sum {c in CRUDES}   process_cost[c] * x[c];\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",
    "subject to feed_limit {c in CRUDES}:\n",
    "    x[c] <= available[c];\n",
    "\n",
    "subject to cap_limit {p in PRODUCTS}:\n",
    "    y[p] <= capacity[p];"
   ],
   "metadata": {
    "id": "R_5g3EfMDp9-",
    "outputId": "1c433a1a-3957-4fe1-cd32-ef0dd037956c",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Overwriting refinery.mod\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uhFeemwyqafn",
    "outputId": "e50ba0f7-08ba-42e0-a13b-c17b90370af3"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Profit: 860275.86\n",
      "\n"
     ]
    }
   ],
   "source": [
    "m = AMPL()\n",
    "\n",
    "m.read(\"refinery.mod\")\n",
    "\n",
    "m.set_data(crudes, \"CRUDES\")\n",
    "m.set_data(products, \"PRODUCTS\")\n",
    "m.param[\"yields\"] = yields.T\n",
    "\n",
    "# solution\n",
    "m.solve(solver=SOLVER, verbose=False)\n",
    "assert m.solve_result == \"solved\", m.solve_result\n",
    "print(f\"Profit: {m.obj['profit'].value():0.2f}\\n\")\n",
    "\n",
    "# -----------------------\n",
    "# Extract primals\n",
    "# -----------------------\n",
    "x = m.var[\"x\"].get_values().to_pandas()\n",
    "y = m.var[\"y\"].get_values().to_pandas()\n",
    "\n",
    "# -----------------------\n",
    "# Extract duals (shadow prices)\n",
    "# -----------------------\n",
    "feed_dual = m.con[\"feed_limit\"].get_values(\"dual\").to_pandas()  # column \"dual\"\n",
    "cap_dual = m.con[\"cap_limit\"].get_values(\"dual\").to_pandas()\n",
    "bal_dual = m.con[\"balances\"].get_values(\"dual\").to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rc7Bvxy8VCX7"
   },
   "source": [
    "## Crude oil feed results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "kgFALj_rK1b8",
    "outputId": "19c5b654-15bf-41e7-8164-03b0235a1cb9"
   },
   "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\n",
       "crude 2    15000.0   45.0           3.0       6896.6             0"
      ],
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      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "summary": "{\n  \"name\": \"display(results_crudes\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"available\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9192.388155425118,\n        \"min\": 15000.0,\n        \"max\": 28000.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          15000.0,\n          28000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.091883092036785,\n        \"min\": 45.0,\n        \"max\": 72.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          45.0,\n          72.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"process_cost\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0606601717798212,\n        \"min\": 1.5,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.0,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"consumption\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13654.44407674659,\n        \"min\": 6896.6,\n        \"max\": 26206.9,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          6896.6,\n          26206.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"shadow price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "results_crudes = crudes\n",
    "results_crudes[\"consumption\"] = x.values\n",
    "results_crudes[\"shadow price\"] = feed_dual\n",
    "\n",
    "display(results_crudes.round(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KbaNCZtRVQB5"
   },
   "source": [
    "## Refinery production results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "lem9nBSThVoj",
    "outputId": "03e6663d-7c91-4182-d7c7-4f972c8a99ac"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "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"
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-0e1db21d-d4e2-499c-bfde-2cd57d005c0f\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e1db21d-d4e2-499c-bfde-2cd57d005c0f')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "      display: none;\n",
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       "      padding: 0 0 0 0;\n",
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       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
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       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-0e1db21d-d4e2-499c-bfde-2cd57d005c0f button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-0e1db21d-d4e2-499c-bfde-2cd57d005c0f');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "summary": "{\n  \"name\": \"display(results_products\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"capacity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10403,\n        \"min\": 2000,\n        \"max\": 24000,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2000,\n          2500,\n          24000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32,\n        \"min\": 30,\n        \"max\": 108,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          72,\n          30,\n          108\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"production\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10584.358249637371,\n        \"min\": 2000.0,\n        \"max\": 24000.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          24000.0,\n          2000.0,\n          5103.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unused capacity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 434.46084211736894,\n        \"min\": 0.0,\n        \"max\": 896.6,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          896.6,\n          500.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"shadow price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 129.13541987644857,\n        \"min\": 0.0,\n        \"max\": 262.6,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          14.0,\n          262.6,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "results_products = products\n",
    "results_products[\"production\"] = y.reindex(results_products.index)\n",
    "results_products[\"unused capacity\"] = (\n",
    "    results_products[\"capacity\"] - results_products[\"production\"]\n",
    ")\n",
    "results_products[\"shadow price\"] = cap_dual\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": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 582
    },
    "id": "ebwOwbkbYSRt",
    "outputId": "c0d54b41-d1ff-4f37-a877-ee6d6304f5ab"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0.0, 24000.0)"
      ]
     },
     "metadata": {},
     "execution_count": 7
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ylim = 24000\n",
    "xlim = 32000\n",
    "\n",
    "ax.axvline(crudes[\"available\"].iloc[0], linestyle=\"--\", label=\"Crude 1\")\n",
    "ax.axhline(crudes[\"available\"].iloc[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].iloc[1]\n",
    "    m = -yields[product].iloc[0] / yields[product].iloc[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.values[0], x.values[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": 7,
   "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
}