SCPlanning_Green_Apple.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/deepakawl/supplychain-analytics-teaching/blob/main/SCPlanning_Green_Apple.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWMocmUjlpEm"
      },
      "source": [
        "# Green Apple: Supply Planning case"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QN519G2FnVZ_"
      },
      "source": [
        "Green Apple (GA) products are sold through retailers nationwide. Their operations consist of assembling purchases parts into a multipurpose gardening tool. Because of the limited equipment and space required for its operations, GA’s capacity is mainly determined by the size of its workforce.\n",
        "\n",
        "Demand for GA’s products is highly seasonal peaking around April. In anticipation of the next season’s demand GA starts planning in the Fall for months of January through June. To meet the peak demand, GA can hire additional workers, subcontract some of the work or build up inventory during the slow months. It could also shift some demand in time by using promotions. Please determine the best use of these options by building an aggregate plan for: regular labor, overtime labor, number of units produced, inventory, subcontracting, and stockouts (backlogged demand).\n",
        "\n",
        "```\n",
        "+----------+-----------------+\n",
        "| Month    | Demand Forecast |\n",
        "+----------+-----------------+\n",
        "| January  | 1,600           |\n",
        "| February | 3,000           |\n",
        "| March    | 3,200           |\n",
        "| April    | 3,800           |\n",
        "| May      | 2,200           |\n",
        "| June     | 2,200           |\n",
        "+----------+-----------------+\n",
        "\n",
        "+-----------------------------+---------------+\n",
        "| Item                        | Cost          |\n",
        "+-----------------------------+---------------+\n",
        "| Materials                   | $10/unit      |\n",
        "| Inventory holding cost      | $2/unit/month |\n",
        "| Marginal cost of a stockout | $5/unit/month |\n",
        "| Hiring and training costs   | $300/worker   |\n",
        "| Layoff cost                 | $500/worker   |\n",
        "| Labor hours required        | 4/unit        |\n",
        "| Regular time cost           | $4/hour       |\n",
        "| Over time cost              | $6/hour       |\n",
        "| Cost of subcontracting      | $30/unit      |\n",
        "+-----------------------------+---------------+\n",
        "```\n",
        "\n",
        "* number of workers in the beginning of January: 80\n",
        "* Initial inventory: 1000\n",
        "* Minimum inventory at the end of June: 500 (business continuity) and no backlog\n",
        "* Maximum use of overtime: 10 hours per worker\n",
        "* Use of subcontracting: unlimited or limited to 500 per month\n",
        "* Regular unit sales price: \\$ 40\n",
        "* Promotion price: \\$ 39, results in an increase of consumption by 10% in the months of the promotion, and shifts 20% of demand from the future two months to the current month."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCqqg646v6r6"
      },
      "source": [
        "### Install and import packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AVeKmtpwefyk"
      },
      "outputs": [],
      "source": [
        "# Install and import packages\n",
        "# !pip install tabulate\n",
        "# !pip install gurobipy\n",
        "import numpy as np\n",
        "from gurobipy import Model, GRB, quicksum\n",
        "from tabulate import tabulate\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4A7JbN-Hv0Wg"
      },
      "source": [
        "### Raw Data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### User defined variables\n",
        "promote = 0\n",
        "month = 4\n",
        "base_price = 40\n",
        "promotion_price = base_price - 1\n",
        "consumption = 0.10\n",
        "forward_buy = 0.20\n",
        "\n",
        "F = np.array([0, 1600, 3000, 3200, 3800, 2200, 2200])\n",
        "\n",
        "### Variables based on input\n",
        "n_months = len(F)-1\n",
        "F = np.append(F, [0, 0])"
      ],
      "metadata": {
        "id": "w_KVttrQwBux"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "frM7khPFv_XZ"
      },
      "source": [
        "### Minimize cost using Gurobi integer optimizer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_inventory():\n",
        "  t_values = list(range(1, n_months + 1))\n",
        "\n",
        "  plt.plot(t_values, [I[t].x for t in t_values], marker='o', label='Inventory')\n",
        "  plt.plot(t_values, [P[t].x for t in t_values], marker='s', label='Production')\n",
        "  plt.plot(t_values, [D[t].x for t in t_values], marker='D', label='Demand')\n",
        "  plt.plot(t_values, [S[t].x for t in t_values], marker='^', label='Stockout')\n",
        "  plt.plot(t_values, [C[t].x for t in t_values], marker='x', label='Subcontracting')\n",
        "\n",
        "  # Adding labels and legend\n",
        "  plt.xlabel('Period')\n",
        "  plt.ylabel('')\n",
        "  plt.legend(loc='best')\n",
        "  plt.title('Aggregate Plan')\n",
        "  plt.grid(axis='y')\n",
        "\n",
        "  # Show the plot\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "exqxhVjpe457"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Optimization problem\n",
        "\n",
        "***Minimize Cost***\n",
        "\n",
        "***subject to:***\n",
        "  Workforce constraint\n",
        "\n",
        "  Inventory balance constraint\n",
        "\n",
        "  Production Capacity constraint\n",
        "\n",
        "  Overtime constraint\n",
        "  \n",
        "  Demand and Stockout constraint\n",
        "\n",
        "***Decision Variables***\n",
        "\n",
        "Ht  = Number of employees hired at the beginning of month t, t = 1, ..., 6\n",
        "\n",
        "Lt  = Number of employees laid off at the beginning of month t, t = 1, ..., 6\n",
        "\n",
        "Wt  = Workforce size for month t, t = 1, ..., 6\n",
        "\n",
        "Ot  = Number of overtime hours worked in month t, t = 1, ..., 6\n",
        "\n",
        "It  = Inventory at the end of month t, t = 1, ..., 6\n",
        "\n",
        "St  = Number of units stocked out at the end of month t, t = 1, ..., 6\n",
        "\n",
        "Ct  = Number of units subcontracted for month t, t = 1, ..., 6\n",
        "\n",
        "Pt  = Production in month t, t = 1, ..., 6\n",
        "\n",
        "Dt  = Computed Demand in month t, t = 1, ..., 6"
      ],
      "metadata": {
        "id": "9ftNavgbfeug"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "-VIxKBv4lmqw",
        "outputId": "21bd101f-0aca-4dcc-b986-8eac4de90a66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+-------+----------+-----------+----------+-----------+----------+-------------+------------+--------+-------+\n",
            "| Period | Hired | Laid Off | Workforce | Overtime | Inventory | Stockout | Subcontract | Production | Demand | Price |\n",
            "|    (t) |   (H) |      (L) |       (W) |      (O) |       (O) |      (S) |         (C) |        (P) |    (D) |  (Pr) |\n",
            "+--------+-------+----------+-----------+----------+-----------+----------+-------------+------------+--------+-------+\n",
            "|      0 |  -0.0 |     -0.0 |      80.0 |     -0.0 |    1000.0 |      0.0 |        -0.0 |       -0.0 |   -0.0 |   nan |\n",
            "|      1 |   0.0 |     16.0 |      64.0 |     -0.0 |    1960.0 |     -0.0 |        -0.0 |     2560.0 | 1600.0 |  40.0 |\n",
            "|      2 |   0.0 |     -0.0 |      64.0 |     -0.0 |    1520.0 |     -0.0 |         0.0 |     2560.0 | 3000.0 |  40.0 |\n",
            "|      3 |   0.0 |     -0.0 |      64.0 |     -0.0 |     880.0 |     -0.0 |         0.0 |     2560.0 | 3200.0 |  40.0 |\n",
            "|      4 |   0.0 |     -0.0 |      64.0 |     -0.0 |      -0.0 |    220.0 |       140.0 |     2560.0 | 3800.0 |  40.0 |\n",
            "|      5 |   0.0 |     -0.0 |      64.0 |     -0.0 |     140.0 |     -0.0 |         0.0 |     2560.0 | 2200.0 |  40.0 |\n",
            "|      6 |   0.0 |     -0.0 |      64.0 |     -0.0 |     500.0 |      0.0 |         0.0 |     2560.0 | 2200.0 |  40.0 |\n",
            "+--------+-------+----------+-----------+----------+-----------+----------+-------------+------------+--------+-------+\n",
            "Minimum Cost : $ 422,660.00\n",
            "Total Revenue: $ 640,000.00\n",
            "Profit       : $ 217,340.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a Gurobi model\n",
        "model = Model(\"MinimizeCost\")\n",
        "\n",
        "H = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"H_{t}\") for t in range(0, n_months+1)}\n",
        "L = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"L_{t}\") for t in range(0, n_months+1)}\n",
        "W = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"W_{t}\") for t in range(0, n_months+1)}\n",
        "O = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"O_{t}\") for t in range(0, n_months+1)}\n",
        "I = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"I_{t}\") for t in range(0, n_months+1)}\n",
        "S = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"S_{t}\") for t in range(0, n_months+1)}\n",
        "C = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"C_{t}\") for t in range(0, n_months+1)}\n",
        "P = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"P_{t}\") for t in range(0, n_months+1)}\n",
        "D = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"D_{t}\") for t in range(0, n_months+1)}\n",
        "\n",
        "# Update the model\n",
        "model.update()\n",
        "\n",
        "# Set objective function - Total cost = Production & Transportation cost across network + Fixed cost of low cap line + fixed cost of high cap line\n",
        "model.setObjective(\n",
        "    quicksum( 300*H[t] + 500*L[t] + 640*W[t] + 6*O[t] + 2*I[t] + 5*S[t] + 30*C[t] + 10*P[t] for t in range(1, n_months+1) ),\n",
        "    GRB.MINIMIZE)\n",
        "\n",
        "### Constraints\n",
        "for t in range(1, n_months+1):\n",
        "\n",
        "  # Workforce size for each month is based on hiring and layoffs, W_t = W_{t-1} + H_t - L_t\n",
        "  model.addConstr(W[0] == 80, name=f\"Workforce_Beg\")\n",
        "  model.addConstr(W[t] - W[t-1] - H[t] + L[t] == 0, name=f\"Workforce_size_constraint_{t}\")\n",
        "\n",
        "  # Demand constraints\n",
        "  if promote == 0:\n",
        "      model.addConstr(D[t] == F[t], name=f\"D_constraint_{t}\")\n",
        "  else:\n",
        "      if t == month:\n",
        "          model.addConstr(D[t] == F[t] * (1 + consumption) + forward_buy * (F[t+1] + F[t+2]), name=f\"D_constraint_{t}\")\n",
        "      else:\n",
        "          if t <= 2 + promote:\n",
        "              if t > promote:\n",
        "                  model.addConstr(D[t] == (1 - forward_buy) * F[t], name=f\"D_constraint_{t}\")\n",
        "              else:\n",
        "                  model.addConstr(D[t] == F[t], name=f\"D_constraint_{t}\")\n",
        "          else:\n",
        "              model.addConstr(D[t] == F[t], name=f\"D_constraint_{t}\")\n",
        "\n",
        "  # Inventory balance constraint: I_{t-1} + P_t + C_t = D_t + S_{t-1} + I_t - S_t\n",
        "  model.addConstr(I[0] == 1000, name=f\"Inventory_Beg\")\n",
        "  model.addConstr(I[t-1] + P[t] + C[t] - D[t] - S[t-1] - I[t] + S[t] == 0, name=f\"Inventory_constraint_{t}\")\n",
        "  model.addConstr(I[n_months] >= 500, name=f\"Inventory_End\")\n",
        "\n",
        "  # Stockout constraint: No stockout at the end\n",
        "  model.addConstr(S[n_months] == 0, name=f\"Stockout_End\")\n",
        "\n",
        "  # Production <= Capacity P_t <= 40 W_t + O_t/4\n",
        "  model.addConstr(40*W[t] + O[t]/4 - P[t] >= 0, name=f\"Capacity_constraint_{t}\")\n",
        "\n",
        "  # Overtime constraint O_t <= 10 W_t\n",
        "  model.addConstr(10*W[t] - O[t] >= 0, name=f\"Overtime_constraint_{t}\")\n",
        "\n",
        "\n",
        "# Suppress optimization output\n",
        "model.Params.OutputFlag = 0\n",
        "\n",
        "# Optimize the model\n",
        "model.optimize()\n",
        "\n",
        "### Calculate price\n",
        "Pr = np.zeros(n_months+1)\n",
        "for t in range(0, n_months + 1):\n",
        "  if promote == 1 and t == month:\n",
        "    Pr[t] = promotion_price\n",
        "  else:\n",
        "    Pr[t] = base_price\n",
        "  if t == 0:\n",
        "    Pr[t] = np.nan\n",
        "\n",
        "### Total revenue = sumproduct (demand*price)\n",
        "revenue = sum([D[t].x * Pr[t] for t in range(1, n_months + 1)])\n",
        "\n",
        "# Store result in table format and print\n",
        "opt_results = [[t] + [H[t].x] + [L[t].x] + [W[t].x] + [O[t].x] + [I[t].x] + [S[t].x] + [C[t].x] + [P[t].x] + [D[t].x] + [Pr[t]] for t in range(0, n_months + 1)]\n",
        "\n",
        "headers = [\"Period\\n(t)\"] + [\"Hired\\n(H)\"] + [\"Laid Off\\n(L)\"] + [\"Workforce\\n(W)\"] + [\"Overtime\\n(O)\"] + [\"Inventory\\n(O)\"] + [\"Stockout\\n(S)\"] + [\"Subcontract\\n(C)\"] + [\"Production\\n(P)\"] + [\"Demand\\n(D)\"] + [\"Price\\n(Pr)\"]\n",
        "print(tabulate(opt_results, headers=headers, tablefmt=\"pretty\", stralign=\"right\"))\n",
        "\n",
        "print(f\"Minimum Cost : $ {model.objVal:,.2f}\")\n",
        "print(f\"Total Revenue: $ {revenue:,.2f}\")\n",
        "print(f\"Profit       : $ {(revenue-model.objVal):,.2f}\")\n",
        "\n",
        "plot_inventory()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9Lv1s3usxay"
      },
      "source": [
        "### 2. What if plants must be built in every market? In this case?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hgdkcE0RssKK"
      },
      "source": [
        "## What-if analysis\n",
        "\n",
        "1.   Cost of adding/lowering capacity drops from \\$300/500 to \\$50/50 each\n",
        "2.   Increase in holding cost (from \\$2 to \\$6), and then consider subcontracting limited to 500 per month (add constraint)\n",
        "3.   Over time cost drops to from \\$6 to \\$4.1 per hour\n",
        "4.   Increased demand fluctuation (April peak to 4800)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZvgxQ44-vwF3"
      },
      "source": [
        "### 1. Cost of adding/lowering capacity drops from \\$300/500 to \\$50/50 each"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EiYn3aZaeMXN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Increase in holding cost (from \\$2 to \\$6), and then consider subcontracting limited to 500 per month (add constraint)"
      ],
      "metadata": {
        "id": "GaZXLuPwd5W_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Gurobi model\n",
        "model = Model(\"MinimizeCost\")\n",
        "\n",
        "H = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"H_{t}\") for t in range(0, n_months+1)}\n",
        "L = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"L_{t}\") for t in range(0, n_months+1)}\n",
        "W = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"W_{t}\") for t in range(0, n_months+1)}\n",
        "O = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"O_{t}\") for t in range(0, n_months+1)}\n",
        "I = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"I_{t}\") for t in range(0, n_months+1)}\n",
        "S = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"S_{t}\") for t in range(0, n_months+1)}\n",
        "C = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"C_{t}\") for t in range(0, n_months+1)}\n",
        "P = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"P_{t}\") for t in range(0, n_months+1)}\n",
        "D = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"D_{t}\") for t in range(0, n_months+1)}\n",
        "\n",
        "# Update the model\n",
        "model.update()\n",
        "\n",
        "# Set objective function - Total cost = Production & Transportation cost across network + Fixed cost of low cap line + fixed cost of high cap line\n",
        "model.setObjective(\n",
        "    quicksum( 300*H[t] + 500*L[t] + 640*W[t] + 6*O[t] + 6*I[t] + 5*S[t] + 30*C[t] + 10*P[t] for t in range(1, n_months+1) ),\n",
        "    GRB.MINIMIZE)\n",
        "\n",
        "### Constraints\n",
        "for t in range(1, n_months+1):\n",
        "\n",
        "  # Workforce size for each month is based on hiring and layoffs, W_t = W_{t-1} + H_t - L_t\n",
        "  model.addConstr(W[0] == 80, name=f\"Workforce_Beg\")\n",
        "  model.addConstr(W[t] - W[t-1] - H[t] + L[t] == 0, name=f\"Workforce_size_constraint_{t}\")\n",
        "\n",
        "  # Demand constraints\n",
        "  if promote == 0:\n",
        "      model.addConstr(D[t] == F[t], name=f\"D_constraint_{t}\")\n",
        "  else:\n",
        "      if t == month:\n",
        "          model.addConstr(D[t] == F[t] * (1 + consumption) + forward_buy * (F[t+1] + F[t+2]), name=f\"D_constraint_{t}\")\n",
        "      else:\n",
        "          if t <= 2 + promote:\n",
        "              if t > promote:\n",
        "                  model.addConstr(D[t] == (1 - forward_buy) * F[t], name=f\"D_constraint_{t}\")\n",
        "              else:\n",
        "                  model.addConstr(D[t] == F[t], name=f\"D_constraint_{t}\")\n",
        "          else:\n",
        "              model.addConstr(D[t] == F[t], name=f\"D_constraint_{t}\")\n",
        "\n",
        "  # Inventory balance constraint: I_{t-1} + P_t + C_t = D_t + S_{t-1} + I_t - S_t\n",
        "  model.addConstr(I[0] == 1000, name=f\"Inventory_Beg\")\n",
        "  model.addConstr(I[t-1] + P[t] + C[t] - D[t] - S[t-1] - I[t] + S[t] == 0, name=f\"Inventory_constraint_{t}\")\n",
        "  model.addConstr(I[n_months] >= 500, name=f\"Inventory_End\")\n",
        "\n",
        "  # Stockout constraint: No stockout at the end\n",
        "  model.addConstr(S[n_months] == 0, name=f\"Stockout_End\")\n",
        "\n",
        "  # Production <= Capacity P_t <= 40 W_t + O_t/4\n",
        "  model.addConstr(40*W[t] + O[t]/4 - P[t] >= 0, name=f\"Capacity_constraint_{t}\")\n",
        "\n",
        "  # Overtime constraint O_t <= 10 W_t\n",
        "  model.addConstr(10*W[t] - O[t] >= 0, name=f\"Overtime_constraint_{t}\")\n",
        "\n",
        "  # Subcontracting constraint C_t <= 500\n",
        "  model.addConstr(C[t] <= 500, name=f\"Subcontracting_constraint_{t}\")\n",
        "\n",
        "\n",
        "# Suppress optimization output\n",
        "model.Params.OutputFlag = 0\n",
        "\n",
        "# Optimize the model\n",
        "model.optimize()\n",
        "\n",
        "### Calculate price\n",
        "Pr = np.zeros(n_months+1)\n",
        "for t in range(0, n_months + 1):\n",
        "  if promote == 1 and t == month:\n",
        "    Pr[t] = promotion_price\n",
        "  else:\n",
        "    Pr[t] = base_price\n",
        "  if t == 0:\n",
        "    Pr[t] = np.nan\n",
        "\n",
        "### Total revenue = sumproduct (demand*price)\n",
        "revenue = sum([D[t].x * Pr[t] for t in range(1, n_months + 1)])\n",
        "\n",
        "# Store result in table format and print\n",
        "opt_results = [[t] + [H[t].x] + [L[t].x] + [W[t].x] + [O[t].x] + [I[t].x] + [S[t].x] + [C[t].x] + [P[t].x] + [D[t].x] + [Pr[t]] for t in range(0, n_months + 1)]\n",
        "\n",
        "headers = [\"Period\\n(t)\"] + [\"Hired\\n(H)\"] + [\"Laid Off\\n(L)\"] + [\"Workforce\\n(W)\"] + [\"Overtime\\n(O)\"] + [\"Inventory\\n(O)\"] + [\"Stockout\\n(S)\"] + [\"Subcontract\\n(C)\"] + [\"Production\\n(P)\"] + [\"Demand\\n(D)\"] + [\"Price\\n(Pr)\"]\n",
        "print(tabulate(opt_results, headers=headers, tablefmt=\"pretty\", stralign=\"right\"))\n",
        "\n",
        "print(f\"Minimum Cost : $ {model.objVal:,.2f}\")\n",
        "print(f\"Total Revenue: $ {revenue:,.2f}\")\n",
        "print(f\"Profit       : $ {(revenue-model.objVal):,.2f}\")\n",
        "\n",
        "plot_inventory()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "dM6ZN3uqeM11",
        "outputId": "1ced4471-60a4-4e1e-c7b7-70f1daa41495"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+-------+----------+-----------+----------+-----------+----------+-------------+------------+--------+-------+\n",
            "| Period | Hired | Laid Off | Workforce | Overtime | Inventory | Stockout | Subcontract | Production | Demand | Price |\n",
            "|    (t) |   (H) |      (L) |       (W) |      (O) |       (O) |      (S) |         (C) |        (P) |    (D) |  (Pr) |\n",
            "+--------+-------+----------+-----------+----------+-----------+----------+-------------+------------+--------+-------+\n",
            "|      0 |  -0.0 |     -0.0 |      80.0 |     -0.0 |    1000.0 |      0.0 |        -0.0 |       -0.0 |   -0.0 |   nan |\n",
            "|      1 |   0.0 |     22.0 |      58.0 |     -0.0 |    1720.0 |     -0.0 |        -0.0 |     2320.0 | 1600.0 |  40.0 |\n",
            "|      2 |   0.0 |     -0.0 |      58.0 |     -0.0 |    1040.0 |     -0.0 |        -0.0 |     2320.0 | 3000.0 |  40.0 |\n",
            "|      3 |   0.0 |     -0.0 |      58.0 |     -0.0 |     160.0 |     -0.0 |        -0.0 |     2320.0 | 3200.0 |  40.0 |\n",
            "|      4 |   9.0 |     -0.0 |      67.0 |     -0.0 |      -0.0 |    480.0 |       480.0 |     2680.0 | 3800.0 |  40.0 |\n",
            "|      5 |   0.0 |     -0.0 |      67.0 |     -0.0 |      -0.0 |     -0.0 |        -0.0 |     2680.0 | 2200.0 |  40.0 |\n",
            "|      6 |   0.0 |     -0.0 |      67.0 |     -0.0 |     500.0 |      0.0 |        20.0 |     2680.0 | 2200.0 |  40.0 |\n",
            "+--------+-------+----------+-----------+----------+-----------+----------+-------------+------------+--------+-------+\n",
            "Minimum Cost : $ 441,620.00\n",
            "Total Revenue: $ 640,000.00\n",
            "Profit       : $ 198,380.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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dHXF1dWX69OmG1/v160efPn2yvUer1VKuXDlWrFgBgF6vZ9asWXh6emJpaUn9+vX5/fffDe0DAwNRqVTs3r2bxo0bY2VlRYsWLbh06RIA/v7+zJgxg1OnTqFSqVCpVPj7+wNw48YNunfvjo2NDXZ2drz66qvcuXPHsO+snpsff/wRT09PLCwsWLFiBWXLliUtLS1b3T169GDAgAH5efqEKDC5CS9ZpCdGiKcjAYZ/JhxLz8jVV0Kqlmkbz5HTzaKsbdM3nichVZur/SnKs912Wr58OdbW1hw5coS5c+fy8ccfExAQAED//v3ZtGkTiYmJhvY7duwgOTmZnj17AjBr1ixWrFjBkiVLOHfuHOPGjeO1115j37592Y7z4YcfMm/ePI4dO4apqSmvv/46AH369OHdd9/F29ubiIgIIiIi6NOnD3q9nu7duxMTE8O+ffsICAjg6tWrDwWqK1eu8Mcff/Dnn38SEhLCK6+8gk6nY+PGjYY2UVFRbNmyxXBMIYq6RSGLCrS9EEJuIQGQotXhNXVHvuxLASLjU6k7fWeu2p//2A8rs6f/a6hXrx7Tpk0DoHr16nz77bfs3r2bjh074ufnh7W1NevWrTP0XqxZs4aXXnoJW1tb0tLS+Oyzz9i1axe+vr4AVKlShQMHDvDdd9/RunVrw3Fmzpxp+P7999+na9eupKamYmlpiY2NDaampri6uhraBwQEcObMGcLCwvDw8ABgxYoVeHt7ExwcTJMmTYDM20YrVqzAycnJ8N5+/fqxbNkyXnnlFQBWrVpFxYoVadOmzVOfJyEK02u1X2PlhZW5bj/KZ1QBViNEySQ9MMVcvXr1sn3v5uZmmLre1NSUV199ldWrVwOQlJTEhg0b6N+/P5DZ+5GcnEzHjh2xsbExfK1YsYLQ0NBHHidrSvys4+TkwoULeHh4GMILgJeXFw4ODly4cMGwrVKlStnCC8CwYcPYuXMnt2/fBjJvUw0ePFjmUxFFXkpGCgtDFrL277W5fs9on9HyDIwQT0F6YABLjQnnP/bLVdujYTEMXhb8xHb+Q5rQ1NMxV8d+FhqNJtv3KpUKvV5v+L5///60bt2aqKgoAgICsLS0pHPnzgCGW0tbtmyhfPny2fZjbm7+yONkBYkHj/O0rK2tH9rWoEED6tevz4oVK+jUqRPnzp1jy5Ytz3wsIQqKoigEXA/gi2NfEJGU+QxcU9emVLGvwi+Xfnnk+yS8CPH0JMCQeUHO7W2c56s74WZvQWRcao7PwagAV3sLnq/uhIna+D0GLVq0wMPDg19//ZVt27bxyiuvGMKIl5cX5ubm3LhxI9vtorwyMzNDp9Nl21a7dm1u3rzJzZs3Db0w58+fJzY2Fi8vryfu84033mD+/Pncvn2bDh06ZOvJEaIouXL/CrOPzuZI5BEA3KzdmNB4Ah0rdUSlUlHWsmyOD/SaqkzpWqVrYZcrRIkht5DyyEStYlq3zAvwf+NJ1vfTunkVifCSpV+/fixZsoSAgADD7SMAW1tbJkyYwLhx41i+fDmhoaGcOHGCb775huXLl+d6/5UrVyYsLIyQkBDu3btHWloaHTp0oG7duvTv358TJ05w9OhRBg4cSOvWrWncuHGuar516xY//PCDPLwriqT49HjmHJ3Dy5te5kjkEczUZoyoP4INPTbQqXInQ0/liPojGO0zOtt7K9hUIEPJ4JOgT575QX4hSisJME+hcx03Fr/WEFd7i2zbXe0tWPxawyI3D0z//v05f/485cuXp2XLltle++STT5gyZQqzZs2idu3adO7cmS1btuDp6Znr/ffu3ZvOnTvTtm1bnJyc+Pnnn1GpVGzYsIEyZcrQqlUrOnToQJUqVfj1119ztU97e3t69+6NjY1NkZu4T5RuekXPn5f/pNu6bqy6sAqdoqN9xfZs6LGB0T6jsTS1fOg9WSFGhYrRPqP5ruN3mJuYExQRxKarm4zwKYQo/lRKCY3/8fHx2NvbExcXh52dXbbXUlNTCQsLM8w98rR0eoWjYTFEJaTibGtBU0/HItXzUty1b98eb29vvv76a2OX8kzy6+dNGN/pu6eZdWQWZ6PPAuBp78n7Td+nhXuLPO/rxzM/suDEAhzMHdjQYwOOFk9+Zk6I0uBx1+8HyTMwz8BErcK3alljl1Hi3L9/n8DAQAIDA1m0SObHEMZ3L+Ue84/PZ0PoBgCsNdaMrD+SfrX7oVFrnvDunA3yHsT2sO1cun+JucFzmf387PwsWYgSTwKMKHIaNGjA/fv3mTNnDjVr1jR2OaIU0+q1rLmwhiWnlpCozRy1171qd8Y2Gks5y3LPtG+NWsP0FtPpv7U/W65uoVuVbrQs3/LJbxRCABJgRBF07do1Y5cgBIfCDzHn6Byuxl0FwLusN5ObTaa+U/18O0adcnXoX7s/K8+v5JPDn/DnS39ipbHKt/0LUZJJgBFCiAfcTrzN58Gfs/vGbgAcLRx5p+E79KjWA7Uq/8c9jPEZw+7ru7mdeJuFIQt5r8l7+X4MIUoiGYUkhBD8O4tu9/Xd2X1jNyYqE16r/Rqbem6iV/VeBRJeAKw0VkzxnQLAqgurOHvvbIEcR4iSRgKMEKJUy5pFt/v67iw5tYQ0XRpNXZvyW7ffmNR0EnZmjx4FkV+eK/8cL3i+gF7RM/3QdLR6bYEfU4jiTm4hCSFKrSv3rzA7eDZHInKeRbcwTWwykYPhB7l0/xIrzq1gaN2hhXp8IYob6YERQpQ62WbRjXj0LLqFqaxlWSY2mQjA4lOLuRF/o9BrEKI4kQAjhCg1cppFt51Hu8fOoluYulXphq+bL2m6ND4O+liWGRDiMSTAiBwNHjy4UKbwr1y5MvPnzy/w4whx+u5p+m/pz7RD04hJjcHT3pPvOnzHgnYLqGBbwdjlAZkLy07xnYKFiQVHIo+w/sp6Y5ckRJElAeZpxN6E8JBHf8XeLLBDDx48GJVKhUqlwszMjGrVqvHxxx+TkZFRYMfMD/7+/jg4ODy0PTg4mOHDhxd+QaLUuJdyj48OfET/rf05G30Wa401ExpP4I+X/qBF+bwvAVDQPGw9GOUzCoAvjn3BvZR7Rq5IiKJJHuLNq9ib8G0jyEh7dBtTcxhzHBw8CqSEzp07s2zZMtLS0ti6dSujR49Go9EwefLkbO3S09MxMzMrkBryi5OTk7FLECWUVq/l5ws/s/jU4nyfRbegDfAawLawbVyIucDco3OZ23qusUsSosiRHpi8So5+fHiBzNeTowusBHNzc1xdXalUqRIjR46kQ4cObNy40XDbZ+bMmbi7uxum4T9z5gzt2rXD0tKSsmXLMnz4cBITEw370+l0jB8/HgcHB8qWLcvEiRMfuvee060eHx8fpk+fbvg+NjaWN998ExcXFywsLKhTpw6bN28mMDCQIUOGEBcXZ+g9ynrff/d748YNunfvjo2NDXZ2drz66qvcuXPH8Pr06dPx8fFh5cqVVK5cGXt7e/r27UtCQkL+nFxRIgSFB/Hyxpf5/NjnJGoT8S7rzaoXVvHpc58W+fACYKo2ZXqL6ZioTNh2bRv7b+03dklCFDkSYAAUBdKTcveVkZK7fWak5G5/+fCQnqWlJenp6QDs3r2bS5cuERAQwObNm0lKSsLPz48yZcoQHBzMb7/9xq5duxgzZozh/fPmzcPf35+lS5dy4MABYmJiWLduXZ5q0Ov1dOnShYMHD7Jq1SrOnz/P7NmzMTExoUWLFsyfPx87OzsiIiKIiIhgwoQJOe6je/fuxMTEsG/fPgICArh69Sp9+vTJ1i40NJT169ezefNmNm/ezL59+5g9WxbCE5mz6I7bO47hAcO5GncVRwtHZrSYwZqua/J1CYDC4FXWiwFeAwD45PAnJGmTjFyREEVLnm4hLV68mMWLFxvWqvH29mbq1Kl06dIFgDZt2rBv375s73nzzTdZsmSJ4fsbN24wcuRI9u7di42NDYMGDWLWrFmYmv5bSmBgIOPHj+fcuXN4eHjw0UcfMXjw4Kf8iLmgTYbP3PN3n0s7567dB+FgZv1Uh1AUhd27d7Njxw7eeust7t69i7W1NT/++KPh1tEPP/xAamoqK1aswNo68zjffvst3bp1Y86cObi4uDB//nwmT55Mr169AFiyZAk7duzIUy27du3i6NGjXLhwgRo1agBQpUoVw+v29vaoVCpcXV0fuY/du3dz5swZwsLC8PDIvP22YsUKvL29CQ4OpkmTJkBm0PH398fW1haAAQMGsHv3bmbOnJmnmkXJkZKRwrKzy1h6dilpujRMVCb0rdWXUT6jCmUiuoIyymcUAdcDuJ14m29OfsP7Td83dklCFBl56oGpUKECs2fP5vjx4xw7dox27drRvXt3zp07Z2gzbNgww7+yIyIimDv333u3Op2Orl27kp6ezqFDh1i+fDn+/v5MnTrV0CYsLIyuXbvStm1bQkJCGDt2LG+88UaeL6gl2ebNm7GxscHCwoIuXbrQp08fwy2ZunXrZnvu5cKFC9SvX98QXgBatmyJXq/n0qVLxMXFERERQbNmzQyvm5qa0rhx4zzVFBISQoUKFQzh5WlcuHABDw8PQ3gB8PLywsHBgQsXLhi2Va5c2RBeANzc3IiKinrq44ri68FZdBefWpxtFt33m75frMMLgKWpJVN9M38/rrmwhtN3Txu5IiGKjjz1wHTr1i3b9zNnzmTx4sUcPnwYb29vAKysrB75r+ydO3dy/vx5du3ahYuLCz4+PnzyySdMmjSJ6dOnY2ZmxpIlS/D09GTevHkA1K5dmwMHDvDVV1/h5+f3NJ/xyTRWmT0huRF5One9K69vB9d6uTt2HrVt25bFixdjZmaGu7t7tt6rB4NKflKr1Q89F6PV/jvduaVl4c2fodFosn2vUqnQ6/WFdnxRNBSlWXQLUgv3FnSr0o1NVzcxPWg6v774Kxq15slvFKKEe+pRSDqdjt9++42kpCR8fX0N21evXs2qVatwdXWlW7duTJkyBSurzIt0UFAQdevWxcXFxdDez8+PkSNHcu7cORo0aEBQUBAdOnTIdiw/Pz/Gjh372HrS0tJIS/v34dr4+Hgg8yL74IU2a5uiKOj1+n8vfLmdwMrEIlfdVnoTi9ztU1Hy9ByMoihYWVlluz2T9RkURTF8riw1a9bE39+fhIQEQ7j566+/UKvVVK9eHVtbW9zc3Dh8+DDPPfccABkZGRw/fpwGDRoY9uXk5ER4eLjh+/j4eMLCwgzHq1OnDrdu3eLixYs59sKYmpqi0+lyDBpZ+6hZsyY3b97k+vXrhl6Y8+fPExsbS61atdDr9YYQ9eB+ctpWlGTVrdVqMTExMXY5xV5CegLfnfmOX//+FZ2iw0xtxiCvQQz2GoylqWWRn1LgaYz1Gctft//i8v3L/HT6J4Z6yzIDouT67zX7UfIcYM6cOYOvry+pqanY2Niwbt06vLy8AOjXrx+VKlXC3d2d06dPM2nSJC5dusSff/4JQGRkZLbwAhi+j4yMfGyb+Ph4UlJSHvkv/VmzZjFjxoyHtu/cudMQoAwf2tQUV1dXEhMTDQ+/5pZJUiK2T25GUlIiun9CVH7SarVkZGQYAtqTXuvWrRvTp0/ntddeY9KkSURHR/P222/Tp08fLC0tiY+PZ/jw4cyePZvy5ctTvXp1Fi1aRGxsbLZ9tWjRgpUrV9K2bVvs7e2ZNWsWJiYmpKWlER8fT4MGDWjRogW9evVi5syZVKlShb///huVSkWHDh1wcnIiMTGRTZs2UadOHSwtLbGyskKv15Oamkp8fDxNmzbFy8uL//3vf8yaNYuMjAwmTJhAy5YtqVGjBvHx8aSlpaHT6bJ9xtTUVPR6fY7npChIT08nJSWF/fv3l8iLa2HRK3pOpp9kZ+pOkpTMB1pra2rTxaILjrcc2Xtrr5ErLFgdTDrwO7/z3anv0FzVUM6k6I+mEuJpJCcn56pdngNMzZo1CQkJIS4ujt9//51Bgwaxb98+vLy8sk1IVrduXdzc3Gjfvj2hoaFUrVo1r4fKk8mTJzN+/HjD9/Hx8Xh4eNCpUyfs7LLfB09NTeXmzZuG50jyRKmIYmqO6jFDqRVTc6ydKoJd/t9/12g0mJqaPvSZHvWanZ0d27dvZ9y4cbRv3x4rKyt69erFvHnzsLGxAeCDDz7g/v37jBo1CrVazZAhQ+jRowdxcXGGfU2bNo3w8HD+97//YW9vz4wZM7h16xbm5uaGNuvWreO9995j2LBhJCUlUa1aNT777DPs7Ozo2LEjb775JkOHDiU6OpqpU6cybdo01Go1FhYWhn1s3LiRt99+m65du6JWq/Hz8+Prr782vG5ubo6JiUm2z2hhYYFarc7xnBQFqampWFpa0qpVq7z/vAkAzt47y5xjcziXkvm8XWW7yrzX6D183Xyf8M6So4vShduBtwmKCOIvy7/4rv13qFUykFSUPLn9x6hKecbFNjp06EDVqlX57rvvHnotKSkJGxsbtm/fjp+fH1OnTmXjxo2EhIQY2oSFhVGlShVOnDhBgwYNaNWqFQ0bNsw2N8iyZcsYO3YscXFxua4rPj4ee3v7bBfhLKmpqYSFheHp6fl0F5TYm4+f58WqbIFNYieKn2f+eSvF7qXcY8GJBYYp9a011oysP5J+tfqhMSl9z4HcTrxNzw09SclIYbrvdHrX6G3skoTId4+7fj/omWfi1ev12Z49eVBWUHFzcwPA19eXmTNnEhUVhbOzMwABAQHY2dkZbkP5+vqydevWbPsJCAjI9pyN0Tl4SEARogAV51l0C1J5m/KM9hnNF8e+YN7xebT2aF2qz4co3fIUYCZPnkyXLl2oWLEiCQkJrFmzhsDAQHbs2EFoaChr1qzhhRdeoGzZspw+fZpx48bRqlUr6tXLHI3TqVMnvLy8GDBgAHPnziUyMpKPPvqI0aNHY25uDsCIESP49ttvmThxIq+//jp79uxh7dq1bNmyJf8/vRCiyAkKD2L20dlcjbsKgHdZbyY3m1zsJqIrKP1r92dr2FbOR59n1pFZzGszz9glCWEUeQowUVFRDBw4kIiICOzt7alXrx47duygY8eO3Lx5k127djF//nySkpLw8PCgd+/efPTRR4b3m5iYsHnzZkaOHImvry/W1tYMGjSIjz/+2NDG09OTLVu2MG7cOBYsWECFChX48ccfC24ItRCiSLideJsvgr9g141dADhaOPJOw3foUa2HPOvxAFO1KTNazKDv5r7svL6TwJuBtPFoY+yyhCh0z/wMTFFVoM/ACJEH8vP2eKkZqSw9u7TEzaJb0L46/hVLzy7FxcqF9d3XY2NmY+yShMgXuX0GRv5ZI4QwipI+i25BG1l/JB62HtxJvsOCEwuMXY4QhU4CjBCi0IXGhjIsYBjjA8cTnhSOq7UrX7T+gh87/Uj1MtWNXV6xYGFqYVhm4NdLvxISFWLcgoQoZBJghBCFJiE9gTlH59B7Y2+ORBzBTG3GiPoj2NhjI36V/UrUEgCFoblbc7pX7Y6CwoygGWh1uZvBVIiSQAKMEKLA6RU96y6v48V1L7Lqwip0io52Hu3Y0GMDo31GY5nbpTzEQyY0noCjhSNXYq/w09mfjF2OEIVGAowQokCduXuG/lv6M/XQVGJSY/C09+S7Dt+xoN0CKthWMHZ5xZ6DhQPvN30fgO9Pf28Yfi5ESScB5hktObWEesvrseTUEmOXUqwNHjyYHj16GLsMkY/updxjysEp9Nvaj7PRZ7HWWDOh8QT+6PYHLcq3MHZ5JUrnyp15vvzzaPVaZhyagV4pmgubCpGfJMA8gyWnlrAwZCEKCgtDFhZKiBk8eDAqlQqVSoVGo8HFxYWOHTuydOnSIrsasyhdtHotK86toNu6boYlAF6q+hKbe25mkPegUrkEQEFTqVRMaT4FS1NLTkSd4Pe/fzd2SUIUOAkwTykrvDyosEJM586diYiI4Nq1a2zbto22bdvyzjvv8OKLL8pqx8KoDkcc5pWNr/D5sc9J1CbiVdaLlV1WMvO5mTLlfQFzs3Hj7QZvA5lzxEQlRxm5IiEKlgSYp5BTeMlSGCHG3NwcV1dXypcvT8OGDfnggw/YsGED27Ztw9/fH4DY2FjeeOMNnJycsLOzo127dpw6dcqwj+nTp+Pj48PSpUupWLEiNjY2jBo1Cp1Ox9y5c3F1dcXZ2ZmZM2dmO/aXX35J3bp1sba2xsPDg1GjRpGYmGh43d/fHwcHB3bs2EHt2rWxsbExBK4sOp2O8ePH4+DgQNmyZZk4cSIldD7FUuN24m3G7R3HsJ3DCI0LxdHCkRktZvBz15/xcfYxdnmlxv9q/Y+65eqSqE1k1pFZxi5HiAIlAYbMCbWStcm5+vrmxDePDC9ZFoYs5JsT3+Rqf/l14W7Xrh3169fnzz//BOCVV14hKiqKbdu2cfz4cRo2bEj79u2JiYkxvCc0NJRt27axfft2fv75Z3766Se6du3KrVu32LdvH3PmzOGjjz7iyJEjhveo1Wq+/vprzp07x/Lly9mzZw8TJ07MVktycjJffPEFK1euZP/+/dy4cYMJEyYYXp83bx7+/v4sXbqUAwcOEBMTw7p16/LlPIjClZqRyuKQxXRf351dN3ZhojKhf+3+bOq5iV7Ve8kSAIXMRG3CNN9pmKpM2XVjF7tv7DZ2SUIUmGdejbokSMlIodmaZvm6z+/PfM/3Z75/Yrsj/Y5gpbHKl2PWqlWL06dPc+DAAY4ePUpUVJRhkcwvvviC9evX8/vvvzN8+HAgcyXxpUuXYmtri5eXF23btuXSpUts3boVtVpNzZo1mTNnDnv37qVZs8zzM3bsWMPxKleuzKeffsqIESNYtGiRYbtWq2XJkiVUrVoVgDFjxmRb72r+/PlMnjyZXr16AbBkyRJ27NiRL+dAFA5FUdh9YzefB39OeFI4AE1dm/J+0/dlIjojq+lYkyF1hvDDmR/47PBnNHVtiq2ZrbHLEiLfSYApQRRFQaVScerUKRITEylbtmy211NSUggNDTV8X7lyZWxt//3F5uLigomJCWq1Otu2qKh/76Xv2rWLWbNmcfHiReLj48nIyCA1NZXk5GSsrDKDmJWVlSG8ALi5uRn2ERcXR0REhCEQAZiamtK4cWO5jVRMhMaGMvvobA5HHAbA1dqVCY0n0KlSJ5mIroh4s/6b7Ly+k+vx15l/fD5TfKcYuyQh8p0EGMDS1JIj/Y48sd1PZ37KVa9KluF1hzO07tAnHju/XLhwAU9PTxITE3FzcyMwMPChNg4ODoY/azTZR4NkjWz677as0U3Xrl3jxRdfZOTIkcycORNHR0cOHDjA0KFDSU9PNwSYnPYh4aT4S0hPYFHIIn6++DM6RYeZ2owhdYYwtO5QmYiuiDE3MWea7zRe3/E6a/9eS9cqXWno0tDYZQmRryTAkHmBzc1tnLcavoXGRPPEZ2AARvuMZkT9EflRXq7s2bOHM2fOMG7cOCpUqEBkZCSmpqZUrlw5345x/Phx9Ho98+bNM/TSrF27Nk/7sLe3x83NjSNHjtCqVSsAMjIyDM/piKJHr+jZcGUD80/MJyY18xmqdh7tmNBkAh62HkauTjxKE9cm9Kreiz8v/8n0oOn83u13zEzMjF2WEPlGnrDLoxH1RzDaZ/Rj2xR0eElLSyMyMpLbt29z4sQJPvvsM7p3786LL77IwIED6dChA76+vvTo0YOdO3dy7do1Dh06xIcffsixY8ee+rjVqlVDq9XyzTffcPXqVVauXMmSJXkfcfXOO+8we/Zs1q9fz8WLFxk1ahSxsbFPXZd4No+bjPHM3TO8tvU1wyy6le0qG2bRlfBS9I1vNJ6yFmUJiwvjxzM/GrscIfKVBJin8LgQUxg9L9u3b8fNzY3KlSvTuXNn9u7dy9dff82GDRswMTFBpVKxdetWWrVqxZAhQ6hRowZ9+/bl+vXruLi4PPVx69evz5dffsmcOXOoU6cOq1evZtasvA/VfPfddxkwYACDBg3C19cXW1tbevbs+dR1iaf3qMkY76XcY+rBqfTb2o8z984YZtH986U/ZRbdYsTe3J7JzSYD8MOZHwiNDX3CO4QoPlRKCX04IT4+Hnt7e+Li4rCzs8v2WmpqKmFhYXh6emJhYfHUx/jvfDCFfdtIFA/59fOW3x41n9Fz5Z8jJCqERG3m/D4vVX2JcY3GyUR0xZSiKLy9520CbwXi4+TD8i7LZXi7KNIed/1+kPwUP4OsnhgVKgkvolh53GSMB24fkFl0SxCVSsWHzT/EytSKkLshrL2Ut+fWhCiqJMA8oxH1R3B60GkJL6LYeFx4eVAbjzYyi24J4WrtyjsN3wFg/on5RCZFGrkiIZ6dBBghSpEFJxbkKrwALApZJKuslyB9avahnlM9krRJfHbkM5naQBR7MoxaiBLqfup9LkRf4HzMeS5EX+BCzAVuJtzM0z4WhSyS3sUSwkRtwgzfGbyy+RX23tzLrhu76Fipo7HLEuKpSYARogS4m3yXCzEXOB/9b1iJSIp48hufYJTPqHyoThQV1cpUY2idoXx3+js+O/IZzdyaYWf26IckhSjKJMAIUYwoikJkUmS2XpXz0ee5l3Ivx/aV7SpT27E2tcv+8+VYm58v/lwkJ2MUhWNYvWHsuLaDa/HX+PLYl0xvMd3YJQnxVCTACFFEKYrCrYRb2cLKhegL3E+7/1BbtUpNFfsq/4YVx9rUcqyFjZnNQ22zQsnjQoyEl5LL3MSc6S2mM3j7YP64/Addq3SliWsTY5clRJ5JgBGiCNAreq7FX8sMKg+ElQRtwkNtTVWmVCtTLVtYqelYM0/rET0uxEh4KfkauTTi5Rov8/vfv/Nx0Mf8/tLvmJuYG7ssIfJEAowQhSxDn8HVuKvZgsrFmIskZyQ/1NZMbUaNMjUMt4C8HL2oXqZ6vqxpk1OIkfBSeoxrNI59N/dxLf4a35/+nrcavGXskoTIEwkw4on8/f0ZO3asrFf0FPSKntSMVJK1ySwOWcyx6GNcun+JNF3aQ20tTS2pWaamoVfFq6wXVRyqoFFrcthz/sgKK4tCFjHKZ5SEl1LEzsyOD5p9wLjAcSw9sxS/yn7UKFPD2GUJkWsSYIqZu3fvMnXqVLZs2cKdO3coU6YM9evXZ+rUqbRs2RKVSsW6devo0aOHsUvNk+nTp7N+/XpCQkKMXcpTyworqRmppOpSSclIIU2Xhi5dR2xaLNvCthGRnjkyyFpjne0WkFdZLyrbVcZEbVLodY+oP0KCSynVoVIH2nm0Y8/NPcw4NIMVXVYY5WdQiKchAeYZJR06ROTMz3D98AOsWxT8Ine9e/cmPT2d5cuXU6VKFe7cucPu3buJjo4u8GOLf+n0OlJ1qYbAkqJLIS3j4V4VABNMMDcxp2eNnng6elK7bG08bD1kPRpRJHzQ7AOORh7l9L3T/HLpF/rX7m/skoTIFfkN+gwURSHqy69IDw0l6suvCnxmy9jYWP766y/mzJlD27ZtqVSpEk2bNmXy5Mm89NJLVK5cGYCePXuiUqkM3wMsXryYqlWrYmZmRs2aNVm5cuVD+37zzTdxcXHBwsKCOnXqsHnz5hzruHv3Lo0bN6Znz56kpaWRlpbG22+/jbOzMxYWFjz33HMEBwcb2vv7++Pg4JBtH+vXr0elUhlenzFjBqdOnUKlUqFSqfD393/m85VfdHodSelJ3Eu5x62EW1y+f5mLMRe5FneNyKRIYtNiDeHFRG2CjZkN5SzL4WHrQfUy1fF08KSsZVmGeA+hs2dnKtlVkvAiigwXaxfGNhwLwNcnvpZlBkSxIT0wZAYRJSUlz+9LOnSI1LNnAUg9e5bE3bvz3AujsrQ0XMifxMbGBhsbG9avX0/z5s0xN88+aiA4OBhnZ2eWLVtG586dMTHJ7Apet24d77zzDvPnz6dDhw5s3ryZIUOGUKFCBdq2bYter6dLly4kJCSwatUqqlatyvnz5w3vf9DNmzfp2LEjzZs356effsLExIR33nmHP/74g+XLl1OpUiXmzp2Ln58fV65cwdHR8Ymfq0+fPpw9e5bt27eza9cuAOzt7XN1TvJbhj4js0clI8VwG0ir0+bY1lRtiqWpJRamFpn/NbHAVG360N9nqiq1MEoX4qm9UvMVtoRt4WTUST49/CnftPsm17+XhDAWCTCAkpLCpYaNnnk/t8bk/Sn+mieOo7KyylVbU1NT/P39GTZsGEuWLKFhw4a0bt2avn37Uq9ePZycnABwcHDA1dXV8L4vvviCwYMHM2pU5qyq48eP5/Dhw3zxxRe0bduWXbt2cfToUS5cuECNGpkP8VWpUuWh41+6dImOHTvSs2dP5s+fj0qlIikpicWLF+Pv70+XLl0A+OGHHwgICOCnn37ivffee+LnsrS0xMbGBlNT02x1FzStTmsIKVm3grT6nMOKxkRjCCkWpplfBflwrRCFSa1SM813Gq9seoV9t/ax4/oOOlfubOyyhHgs6ccuZnr37k14eDgbN26kc+fOBAYG0rBhw8fecrlw4QItW7bMtq1ly5ZcuHABgJCQECpUqGAILzlJSUnh+eefp1evXixYsMDwr7PQ0FC0Wm22/Ws0Gpo2bWrY/9OKSo7i3L1zRCVHPdN+FEUhXZdOfFo8UclRXI+/zqWYS/x9/29uxN/gbvJdEtITDOHFzMQMe3N7XKxdqGRXiZqONalRpgYeth44WTlha2Yr4UWUOFUdqjKs7jAAZh2ZRVxanJErEuLx8tQDs3jxYhYvXsy1a9cA8Pb2ZurUqYZ/eaempvLuu+/yyy+/kJaWhp+fH4sWLcLFxcWwjxs3bjBy5Ej27t2LjY0NgwYNYtasWZia/ltKYGAg48eP59y5c3h4ePDRRx8xePDgZ/+0j6CytKTmieO5bq8oCtcHDCTt4kXQ6/99Qa3GvFYtKq1ckevuV5Vl7icfy2JhYUHHjh3p2LEjU6ZM4Y033mDatGlPfY4sc1GDubm54fbTe++9R/ny5XO9f7Va/dDzQVptzj0dWaKSo7ibfBfA8F9nK+cnHktRFLR67b+9Kv/0sOj0uhzbm5uaY2FiYbgVZGFiIaMwRKk1tO5Qtl/bztW4q8w7No+PW35s7JKEeKQ89cBUqFCB2bNnc/z4cY4dO0a7du3o3r07586dA2DcuHFs2rSJ3377jX379hEeHk6vXr0M79fpdHTt2pX09HQOHTrE8uXL8ff3Z+rUqYY2YWFhdO3albZt2xISEsLYsWN544032LFjRz595IepVCrUVla5/ko5cZK08+ezhxcAvZ608+dJOXEy1/vKj/vMXl5eJCUlAZm9Hzpd9ot17dq1OXjwYLZtBw8exMvLC4B69epx69Yt/v7770ceQ61Ws3LlSho1akTbtm0JDw8HMDwY/OD+tVotwcHBhv07OTmRkJBgqBF4aLi0mZmZoe4Hw0uWu8l3H+qJURSFtIw0YtNiiUyK5FrcNS7GXOTy/cvcSrjFvZR7JKYnotPrUKHCwtQCBwsHXK1d8bT3pJZjLao5VKOCbQXKWpbFWmMt4UWUamYmZoa1kdZdWcfRiKPGLUiIx1GeUZkyZZQff/xRiY2NVTQajfLbb78ZXrtw4YICKEFBQYqiKMrWrVsVtVqtREZGGtosXrxYsbOzU9LS0hRFUZSJEycq3t7e2Y7Rp08fxc/PL091xcXFKYASFxf30GspKSnK+fPnlZSUlDztU1EURa/XK1d7v6ycr1VbOV+z1sNftWorV3u/rOj1+jzv+0nu3buntG3bVlm5cqVy6tQp5erVq8ratWsVFxcX5fXXX1cURVGqV6+ujBw5UomIiFBiYmIURVGUdevWKRqNRlm0aJHy999/K/PmzVNMTEyUvXv3Gvbdpk0bpU6dOsrOnTuVq1evKlu3blW2bdumKIqiLFu2TLG3t1cURVG0Wq3y8ssvKzVr1lQiIiIURVGUd955R3F3d1e2bdumnDt3Thk0aJBSpkwZw/Gjo6MVa2tr5e2331auXLmirF69WnF3d1ce/PFbvXq1Ym1trew+tFv56+JfyolbJ5Szd88+9HU97roSnhiuXI29qpy/dz7HNufunVNC74cqtxNuK9Ep0UpyerKi0+vy/e8jt57l500IY/gk6BOljn8d5YU/XlBStPJzKwrX467fD3rqZ2B0Oh2//PILSUlJ+Pr6cvz4cbRaLR06dDC0qVWrFhUrViQoKAiAoKAg6tatm+2Wkp+fH/Hx8YZenKCgoGz7yGqTtQ9jU7RatBER8Kgh04qCNjIS5Qm3SJ6GjY0NzZo146uvvqJVq1bUqVOHKVOmMGzYML799lsA5s2bR0BAAB4eHjRo0ACAHj16sGDBAr744gu8vb357rvvWLZsGW3atDHs+48//qBJkyb873//w8vLi4kTJz7UkwOZDxL//PPPeHt7065dO6Kiopg9eza9e/dmwIABNGzYkCtXrrBjxw7KlCkDgKOjI6tWrWLr1q3UrVuXn3/+menTp2fbb+/evWnbsS09u/Tk+VrPs/XPrTmeg4T0BGJSYkjWJqNX9KhVaiw1ljhaOOJu405Vh6rUcqxFFYcquNu442jhiKXGUoYtC5EH7zR8B2dLZ24k3OC7098ZuxwhcqRSlLxNXnLmzBl8fX1JTU3FxsaGNWvW8MILL7BmzRqGDBlCWlr2ybyaNm1K27ZtmTNnDsOHD+f69evZbgclJydjbW3N1q1b6dKlCzVq1GDIkCFMnjzZ0Gbr1q107dqV5OTkRz6vkTUfSZb4+Hg8PDy4d+8ednZ22dqmpqZy8+ZNKleujIWFRV4+PgDaiAh09x9eETiLiaMjmkIcTVMS3Eu5x92Uu09u+A9bM1ucLZ3zZU2ggpaamsq1a9fw8PB4qp83IYwh8FYg4/ePx1RlyqrOq2SZAVFo4uPjKVeuHHFxcQ9dvx+U52HUNWvWJCQkhLi4OH7//XcGDRrEvn37nqnY/DBr1ixmzJjx0PadO3di9Z9hylnDdRMTE0lPT8/7waytM78eIyU+Pu/7LcXuZuQ+vEBmT4yd3o5Uiv4cK+np6aSkpLB//34yMjKMXY4Queat8eac9hzv7nyXN23elJ5MUSiSkx9e2DYneQ4wZmZmVKtWDYBGjRoRHBzMggUL6NOnD+np6cTGxmabdfXOnTuGuT1cXV05ejT7Q2F37twxvJb136xtD7axs7N77GiZyZMnM378eMP3WT0wnTp1emQPjI2NjfyLuIhIT0nPUw+Mk6UTdpaPTuZFSWpqKpaWlrRq1Up+3kSx0iSlCb039+a29jZxVePoX0uWGRAFLz6XHQDPPJGdXq8nLS2NRo0aodFo2L17N7179wYyJz67ceMGvr6+APj6+jJz5kyioqJwds4cEhsQEICdnZ1hxIqvry9bt2Z//iEgIMCwj0cxNzd/aGZayByVo9Fkn7NDp9NljjxSq1Gr5V8URYGztTOoeGj0UU6crJxyNaS6qFCr1ahUqhx/FoUoytw17oxvPJ6Pgz5m0elFdPLshLuNu7HLEiVcbn9P5unqPXnyZPbv38+1a9c4c+YMkydPJjAwkP79+2Nvb8/QoUMZP348e/fu5fjx4wwZMgRfX1+aN28OQKdOnfDy8mLAgAGcOnWKHTt28NFHHzF69GhD+BgxYgRXr15l4sSJXLx4kUWLFrF27VrGjRuXx1MgihuFJz+OVdzCixDFXe/qvWno3JCUjBQ+OfxJga/5JkRu5SnAREVFMXDgQGrWrEn79u0JDg5mx44ddOzYEYCvvvqKF198kd69e9OqVStcXV35888/De83MTFh8+bNmJiY4Ovry2uvvcbAgQP5+ON/J0vy9PRky5YtBAQEUL9+febNm8ePP/6In59fPn1kURQlpSdxL/keAHbmOd8akvAiROFTq9RMbzEdjVrDgdsH2Ba2zdglCQE8xSik4iI+Ph57e/scn2JOTU0lLCwMT09PeSahCMjQZxAaG0qGPoMyFmVwt3F/aDK74hxe5OdNlATfnfqOb0O+xdHCkQ3dN+Bg4WDskkQJ9bjr94PkARBhVIqiEJ4YToY+AzMTM1ysMucIcrZyxskqc3HK4hxehCgpXq/zOtUcqhGTGsPnxz43djlCSIARxhWTGkNCegIqlQoPW49sU/k7WznjXc5bwosQRYDGRMP0FtNRoWJj6EaCwovG5KKi9JIAI4wmJSOFO8mZQ+ZdrFywMJXbK0IUZfWd6tO3Vl8APg76mJSMFCNXJEozCTClTGBgICqVitjYWKPWodPruJVwC0VRsDWzxdHCsdBrmD59Oj4+PoV+XCGKs3cavoOLlQu3Em+x+NRiY5cjSjEJMMXM3bt3GTlyJBUrVsTc3BxXV1f8/PweWm26qHvvo/fo9nw3TNWmuNu458uq3I+jUqlYv359tm0TJkxg9+7dBXpcIUoaa401U5pPAWDFuRVciL5g5IpEaSUB5ikc3XSV4C1hOb4WvCWMo5uuFtixe/fuzcmTJ1m+fDl///03GzdupE2bNkRHRxfYMfNbXFocqRmZSwBUsK2AqfrR8ylqC2BRzCw2NjaULVu2wPYvREnV2qM1fpX90Ck6ph2aRoZelsgQhU8CzFNQqVUc3RT2UIjJDC9hqNQF05sQGxvLX3/9xZw5c2jbti2VKlWiadOmTJ48mZdeeolr166hUqkICQnJ9h6VSkVgYGC2fR08eJB69ephYWFB8+bNOXv27EOvt2nTBisrK8qUKYOfnx/3/1nAMi0tjbfffhtnZ2csLCx47rnnCA4ONrw36zbV7t27ady4MVZWVrRo0YJLly6Rrktn4Q8LWfz5Yi6du4SNmQ0qlQp/f38gs6dk8eLFvPTSS1hbWzNz5kx0Oh1Dhw7F09MTS0tLatasyYIFCx46P0uXLsXb2xtzc3Pc3NwYM2YMAJUrVwagZ8+eqFQqw/f/vYU0ePBgevTowRdffIGbmxtly5Zl9OjR2UJUREQEXbt2xdLSEk9PT9asWUPlypWZP39+Hv4mhSj+3m/6PrZmtlyIucDqC6uNXY4ohSTAkDmUV5umy/WXT4eKNH6hMkc3hXFk41W0aTqObLzK0U1hNH6hMj4dKuZ6X3mZhsfGxgYbGxvWr1//0KrfefXee+8xb948goODcXJyolu3boYLdUhICO3bt8fLy4ugoCAOHDhAt27d0Ol0AEycOJE//viD5cuXc+LECapVq4afnx8xMTHZjvHhhx8yb948jh07hqmpKa+//jq3Em7h192PN8a8gbe3NxEREURERNCnTx/D+6ZPn07Pnj05c+YMr7/+Onq9ngoVKvDbb79x/vx5pk6dygcffMDatWsN71m8eDGjR49m+PDhnDlzho0bNxrW7MoKV8uWLSMiIiJb2PqvvXv3Ehoayt69e1m+fDn+/v6GcAUwcOBAwsPDCQwM5I8//uD7778nKirqmf4uhCiOylmWY0LjCQAsDFnIrYRbRq5IlDbPvBZSSZCRruf7d55uRe1jW69xbOu1R37/JMMXtEZjbvLkhmSuou3v78+wYcNYsmQJDRs2pHXr1vTt25d69erlqe5p06YZZlBevnw5FSpUYN26dbz66qvMnTuXxo0bs2jRIkN7b29vAJKSkli8eDH+/v506dIFgB9++IGAgAB++ukn3nvvPcN7Zs6cSevWrQF4//336dq1K7GJsVhZWeFaxtWwKvh/9evXjyFDhmTb9uBK456engQFBbF27VpeffVVAD799FPeffdd3nnnHUO7Jk2aAODklDmfjIODQ47He1CZMmX49ttvMTExoVatWnTt2pXdu3czbNgwLl68yK5duwgODqZx48YA/Pjjj1SvXv2x+xSipOpZrSebr24mODKYTw5/wpIOSwr8eTYhskgPTDHTu3dvwsPD2bhxI507dyYwMJCGDRtm6yXIjQcXx3R0dKRmzZpcuJD5MF5WD0xOQkND0Wq1tGzZ0rBNo9HQtGlTw/uzPBiq7MvZAxBzL4byNuWzzffyX1nh4EELFy6kUaNGODk5YWNjw/fff8+NGzeAzCUuwsPDH1lzXnh7e2Ni8m9tbm5uhh6WS5cuYWpqSsOGDQ2vV6tWjTJlyjzzcYUojlQqFdN8p2GmNuNQ+CE2X91s7JJEKSI9MICpmZrhC1rn+X0ndlzn2NZrqE1U6HUKjV+oTEO/Snk+dl5ZWFjQsWNHOnbsyJQpU3jjjTeYNm0af/31F0C221JP8xCspaVlnt+Tk6wVRTP0GdxNyVwWwFZj+8i1jrJYW1tn+/6XX35hwoQJzJs3D19fX2xtbfn88885cuRIvtb7YM1ZVCoVer0+3/YvRElTya4SI31GsuDEAuYGz6Vl+ZZGmRZBlD7SA0PmRUpjbpKnr5BdNzi29RpNu3kycmFbmnbz5NjWa4TsupGn/eRHd6uXlxdJSUmGWyURERGG1x58oPdBhw8fNvz5/v37/P3339SuXRvI7Dl51PDiqlWrYmZmlm3YtlarJTg4GC8vr4faK4rC7cTb6PSZz89kLQ9gZmZmeKbmSQ4ePEiLFi0YNWoUDRo0oFq1aoSGhhpet7W1pXLlyo8dEq3RaHJ9vEepWbMmGRkZnDx50rDtypUrhoebhSitBnkPokaZGsSmxfJ5sCwzIAqHBJinkDXaqGk3T5p09QSgSVdPmnbzzHF0Un6Jjo6mXbt2rFq1itOnTxMWFsZvv/3G3Llz6d69O5aWljRv3pzZs2dz4cIF9u3bx0cffZTjvj7++GN2797N2bNnGTx4MOXKlaNHjx4ATJ48meDgYEaNGsXp06e5ePEiixcv5t69e1hbWzNy5Ejee+89tm/fzvnz5xk2bBjJyckMHTr04ZpTo0lMT4R/cppalfkjV7lyZcLCwggJCeHevXuPfSi5evXqHDt2jB07dvD3338zZcqUhx7EnT59OvPmzePrr7/m8uXLnDhxgm+++cbwelbAiYyMfOrAUatWLTp06MDw4cM5evQoJ0+eZPjw4VhaWsp9f1GqadQapvtmLjOw+epmDt4uXvNSieJJAsxTUPRKtvCSJSvEKPqCWeDbxsaGZs2a8dVXX9GqVSvq1KnDlClTGDZsGN9++y2QOZQ4IyODRo0aMXbsWD799NMc9zV79mzeeecdGjVqRGRkJJs2bcLMzAyAGjVqsHPnTk6dOkXTpk3x9fVlw4YNmJqaGt7bu3dvBgwYQMOGDbly5Qo7dux46FmQlIwUopIznx8pZ1ku22u9e/emc+fOtG3bFicnJ37++edHfu4333yTXr160adPH5o1a0Z0dDSjRo3K1mbQoEHMnz+fRYsW4e3tzYsvvsjly5cNr8+bN4+AgAA8PDxo0KBBbk53jlasWIGLiwutWrWiZ8+eDBs2DFtbW1llWpR6dZ3q0r92fwA+OfwJydpkI1ckSjqVkpdxvMXI45bjTk1NJSwsDE9PT7nwFBCdXsfVuKuk69KxNbPFw9ajRPZS3Lp1Cw8PD3bt2vXIh4jl502UFsnaZHps6EFEUgSDvAYxockEY5ckiqHHXb8fJD0wokBEJkWSrktHo9YUylIBhWXPnj1s3LiRsLAwDh06RN++falcuTKtWrUydmlCGJ2VxsqwzMDKCys5d++ckSsSJZkEGJHvYtNiiU2LBaC8bfnHLhVQ3Gi1Wj744AO8vb3p2bMnTk5OBAYGPjR6SYjS6vkKz9PFswt6Rc/0oOlo9QW3HIgo3UrOlUUUCem6dCISM0dBOVk5Ya2xfsI7ihc/Pz/8/PyMXYYoLmJvQvJj1imzKgsOHoVXTyGZ1GQSh8IPcTHmIivPr+T1Oq8X7AFL6XkudEXsPEuAEflGr+i5mXATvaLHSmOFk6WTsUsSwnhib8K3jSDjMct+mJrDmOMl7uJa1rIsExpPYMrBKSwKWUTHih3xsCugz1iKz3OhKoLnuVTfQiqhzy8bTVRyFKkZqZioTahgU6HEPPfyrOTnrJRKjn78L3vIfP1x/6ItxrpX7U4zt2ak6dKYcXhGwf1/UMrPc6Epgue5VPbAZD2vkJycnK+zuJZmiemJRKdk/uC6W7ujMZFnQrIkJ2cOJ5XnZESOUu5D4l1jV5HvVMC0emPoeeckRyKOsPH8arpXKoDbrym5nNephJ7nQpPb81yISmWAMTExwcHBwbDGjZWVlfQWPAOtXsvN+MxbR/Zm9pgpZqSmphq7LKNTFIXk5GSioqJwcHDItsaSEAYrexi7ggLjAYyyt+UrxzJ8fuQznvvjLcoaa2mOEnyeS6tSGWAAw6rEWSFGPB0FhZiUGNJ0aWjUGkwsTUhSJRm7rCIlN6tgixJEr4ebR+DwYmNXUiQMjEtgm7U1F83NmFO2DHPvyq0ckT9KbYBRqVS4ubnh7Oz8VAseikzr/l7HskvLMDMx48s2X1LRrqKxSypSNBqN9LyUFnfOwZnf4MwfEHcj9+8bvg/cfQqsLGMzBaZHn6Pfln5ss7GmW/flPF/h+fw7QHgIfJ+LxXhL+HkucLk9z4Wo1AaYLCYmJnKBeUpn751lbshcMpQMpvlOo4ZzDWOXJEThir0BZ37P/Ip6YNI2M1uo1BIubzdebUWId1lvXqv9GivOr+CTw5+wvvt6rDRWxi5LFHOlPsCIp5OYnsjE/RPJUDLoVKkTvav3NnZJQhSOpGg4vw5O/wY3/13VHRMzqN4J6r4CNfzg7iUJMA8Y7TOa3Td2czvxNt+c/IZJTScZuyRRzEmAEXmmKAqfHvmUmwk3cbd2Z1qLafIQtCjZ0pPg4tbMW0Shu0Gf8c8LKqj8XGZo8XoJLB9Y0NSqbOa8GE+aN8OqbIGWXlRkLTMwYtcIVl9YzQueL1DXqW4+7FjOc6Eogue5VC7mKJ7NxtCNfHjgQ0xUJvh39sfH2cfYJQmR/3RaCN2TGVouboEHV1d2q58ZWur0Bjv3R++jiM1cWhRM/msym69upnqZ6vz64q9o1PkwvYCc58JRSOc5t9dv6YEReXIt7hqfHv4UgFE+oyS8iJIlawTRmd/g3DpIifn3tTKemaGl7ivglMvnvRw85ML5H+81eY8Dtw9w+f5llp9bzht133j2ncp5LhxF7DxLgBG5lq5LZ+L+iaRkpNDUtSlD6ww1dklC5I9HjSCydsrsZan7KpRvCHKr9Jk5WjgysclEPjjwAYtDFtOxUkcq2VUydlmiGJIAI3Jt/on5XIi5gIO5A5899xkmahm9JYqxx40gqt0N6r4Mnq3BRH5N5rcXq7zIptBNBEUEMSNoBj91+kmeoxN5Jv9nilzZf2s/K8+vBODTlp/iYu1i5IqEeApPHEH0MtToDBpZYqQgqVQqpvpOpdfGXgRHBrPuyjp6Ve9l7LJEMSMBRjzR3eS7TDk4BYD+tfvT2qNoTWYkxGM9zQgiUeAq2FZgtM9ovjj2BV8c+4JWFVpRzrKcscsSxYgEGPFYekXP5AOTiUmNoZZjLcY3Gm/skoR4svwYQSQKXP/a/dkatpXz0eeZfXQ2X7T+wtgliWJEnZfGs2bNokmTJtja2uLs7EyPHj24dOlStjZt2rRBpVJl+xoxYkS2Njdu3KBr165YWVnh7OzMe++9R0ZGRrY2gYGBNGzYEHNzc6pVq4a/v//TfULxTJaeXcqRiCNYmloyt9VczEzMjF2SEDnT6+F6EGweD1/UgDWvZgYYbXLmCKJWE2H0UXhzP7R4S8JLEWCqNmW673RMVCbsuLaDfTf3GbskUYzkqQdm3759jB49miZNmpCRkcEHH3xAp06dOH/+PNbW1oZ2w4YN4+OPPzZ8b2X175TROp2Orl274urqyqFDh4iIiGDgwIFoNBo+++wzAMLCwujatSsjRoxg9erV7N69mzfeeAM3Nzf8/ApgOXaRo1N3T/HtyW8BmNx0Mp72nkauSIgcPHEE0StQvpGMICqiapetzUCvgSw7t4xPDn9CY9fGWGusn/xGUeo900R2d+/exdnZmX379tGqVSsgswfGx8eH+fPn5/iebdu28eKLLxIeHo6LS+aDoEuWLGHSpEncvXsXMzMzJk2axJYtWzh79qzhfX379iU2Npbt23M3NbdMZPdsEtITeGXTK9xOvE2Xyl2Y02qOjBIQRYeMICpRUjJS6LWhF7cSb9GvVj8mN5ts7JKEERXKRHZxcXEAODo6Ztu+evVqVq1ahaurK926dWPKlCmGXpigoCDq1q1rCC8Afn5+jBw5knPnztGgQQOCgoLo0KFDtn36+fkxduzYR9aSlpZGWtq/UxzHx8cDoNVqZbXpPFIUhekHp3M78TblrcvzfuP3H7rFJ0ShS45GfWEDqnN/on5gBJFiYoZStQP6Or1RqnX6dwSRXgG9/L9fHJhiyodNP2TknpH8fPFnOnp0pL5TfWOXJYwkt9fspw4wer2esWPH0rJlS+rUqWPY3q9fPypVqoS7uzunT59m0qRJXLp0iT///BOAyMjIbOEFMHwfGRn52Dbx8fGkpKRgafnwEMdZs2YxY8aMh7bv3Lkz2y0s8WTH046zM2UnatS8qHqRv3b9ZeySRCllokvDNe44Fe4H4Rx/FjU6ABRU3LOpxa0yvkQ4NEFrag1hQNhe4xYsnkkDTQNOak8yafckRtmOwlQlPWilUXJy8pMb8QwBZvTo0Zw9e5YDBw5k2z58+HDDn+vWrYubmxvt27cnNDSUqlWrPu3hnmjy5MmMH//vCJn4+Hg8PDzo1KmT3ELKg6txV/l0e+ZSAWN8xjDYa7BxCxKlj06L6upe1Of+QPX3NlQPjCBSXOuh9+6N3qsnDnbuOAB1HrkjUdy0SGtB7829iUqLIqpSFG/UyYdlBkSxk3UH5UmeKsCMGTOGzZs3s3//fipUqPDYts2aNQPgypUrVK1aFVdXV44ePZqtzZ07dwBwdXU1/Ddr24Nt7Ozscux9ATA3N8fc3Pyh7RqNBo0mHxYLKwXSdGl8eOhDUnWpNHdrztB6Q1Gr8jRQTYink6s1iF5G5VQTE0DmgC6ZnDROTGo6iff/ep8fzv5A5yqdZfBAKZTba3aeAoyiKLz11lusW7eOwMBAPD2f/IMVEhICgJubGwC+vr7MnDmTqKgonJ2dAQgICMDOzg4vLy9Dm61bt2bbT0BAAL6+vnkpV+TRl8e+5NL9SzhaOPLZc59JeBEFT0YQif94wfMFNl3dxMHbB5kRNIOlfkvld5HIUZ5GIY0aNYo1a9awYcMGatasadhub2+PpaUloaGhrFmzhhdeeIGyZcty+vRpxo0bR4UKFdi3L3N8v06nw8fHB3d3d+bOnUtkZCQDBgzgjTfeyDaMuk6dOowePZrXX3+dPXv28Pbbb7Nly5ZcD6OWUUh5E3gzkLf2vAXAovaLeL7C88YtSJRcMoJIPEF4Yjg9NvQgJSOFqb5TeaXGK8YuSRSi3F6/8xRgHjWMdtmyZQwePJibN2/y2muvcfbsWZKSkvDw8KBnz5589NFH2Yq4fv06I0eOJDAwEGtrawYNGsTs2bMxNf33F1ZgYCDjxo3j/PnzVKhQgSlTpjB48ODclioBJg/uJN3h5U0vE5sWy0CvgbzX5D1jlyRKmqw1iM78DjeC/t0uaxCJR1h5fiVzg+diq7FlQ48NOFk5GbskUUgKJMAUJxJgcken1zEsYBjBkcHUdqzNqhdWyWy7In/IGkTiGej0Ol7b+hpno8/SsVJHvmzzpbFLEoWkUOaBEcXfj2d+JDgyGEtTSz5v/bmEF/FsHrcGkWs9qPcqePcC+/LGq1EUCyZqE6a3mE6fzX0IuB7Anht7aFexnbHLEkWIBJhS7GTUSRafWgzAR80/opJdJSNXJIqlXI4gwqnmo/chRA5qOtZksPdgfjr7EzMPz6SJaxNszWyNXZYoIiTAlFJxaXFM2j8JnaLjxSov8lLVl4xdkihuZASRKAQj6o8g4HoANxJusODEAj5q/pGxSxJFhASYUkhRFGYEzSAiKQIPWw/5hSByT0YQiUJmYWrBNN9pDN05lF8v/UrXKl1p4NzA2GWJIkB+y+RG7E1Ijn7061ZlwcGj8Op5Rr9f/p2A6wGYqkz5vNXnRWvl1xJ2rousvJznR40gUmsyRxDVe0VGEIkC1dStKT2r9WTdlXVMPzSd37r9Js/rCQkwTxR7E75tBBlpj25jag5jjheLC+uV+1eYc3QOAO80fAfvct5GrugBJexcF1m5Oc8m5tDx48wHcmUEkSgC3m38Lvtu7eNq3FV+OvMTI31GGrskYWQyveGTJEc//hc9ZL7+uH/NFhGpGam8t/890nRptHRvyUDvgcYuKbsSdK6LtNycZ10abJ8El3dkhhfXetDpUxh3DgZvhkaDJLyIQmVvbs/kppMB+OHMD1yNvWrkioSxSQ9Mfrl9AlLjjF3FY31x5VeuxF6hrMaWTz26og4rYqtMR4fmrl0xONdFWm7Ps60bNBggI4hEkeFX2Y9NVzex/9Z+pgdNx7+zvywzUIrJRHZPEh4C37fOt7qMZbeVJWNdMmey/C4yihYpqUauSBR5wwPBXR6WFEVLRGIEPTb0IDkjmY+afUSfWn2MXZLIZzKRXWFzqFxkH2KMVOmZapUCwJB0DS1sPMHGyEXlRJsCsdee3K4In+tiIbfnGRn+LIoeNxs33m74NrOPzuarE1/RxqMNLtYuxi5LGIEEmPzy6nJw9zF2FQ/J0GcwacdQ4qNOUKdsHd7qsgJMcrdUeaHLbW9XET3XxUYJ6VUUpVffmn3ZenUrp++d5rMjn7Gg3QJjlySMQG4elnA/nP6BE1EnsNZYM7fVXDRFNbwIIUQumahNmNZiGqYqU/bc3MOu67uMXZIwAgkwJdixyGMsOb0EgCnNp+BhJ0OPhRAlQ40yNRhSZwgAnx35jPj0eCNXJAqbBJgnsSqbOffI45iaZ7YrQuLS4nj/r/fRK3peqvoSXat0NXZJT1ZMz3WxI+dZlBBv1n+TynaVuZtyl6+OfwXAklNLqLe8HktOLTFydSVbUTjPMgopN4rZ7LCKojB271j23NxDJbtKrH1xLVYaK2OXlTvF7FwXW3KeRQkRHBnM6zteBzDM1ptltM9oRtQfYazSSqwlp5awMGSh4fv8Ps8yCik/OXgUq1/may+tZc/NPZiqTZnbam7xCS9Q7M51sSXnWZQQTVyb0Lt6b/64/Ee28AIYLrISYvLPf8MLGO88yy2kEubv+38zN3guAOMbjcerrJeRKxJCiIJVxuLRs0IvDFkot5PySU7hJYsxzrMEmBIkJSOFifsmkq5P5/nyz/Na7deMXZIQQhSoJaeW8OOZHx/bRkLMs3tceMlS2OdZbiGVIHOD5xIaF0o5y3J8+tynqFQyEZkQouTKzUU1y8KQhey8tpPqZaoXcFUlz+X7l7kcezlXbQvzdpI8xFtC7Ly2k3f3vYsKFd93+p7mbs2NXZIQQhSoesvroVAiL2HFmgoVpwedfur3y0O8pUh4YjjTg6YDMLTuUAkvQohSYZTPqFz3wAA8V/45Wri3KMCKSqZD4Yc4cPtArtuP8hlVgNX8SwJMMZehz2DS/kkkpCdQz6leof3gCCGEsWXdpshNiJEh1U9vgNeAXN+uK8zzLA/xFnOLTy0m5G4INhob5jw/B41algoQQpQeI+qPYLTP6Me2kfDy7IrieZYAU4wFRwbzw+kfAJjmO40KthWMXJEQQhS+x11cJbzkn6J2niXAFFP3U+/z/v73UVDoVb0XnT07G7skIYQwmpwurhJe8l9ROs/yDEwxpCgKUw9OJSolCk97TyY1mWTskoQQwuiyLqKLQhYxymeUhJcCUlTOswyjLoZWX1jN7KOz0ag1/Nz1Z2o61jR2SUIIIUS+yO31W24hFTOXYi4x79g8AN5t/K6EFyGEEKWSBJhiJFmbzIR9E9DqtbSp0IZ+tfoZuyQhhBDCKCTAFCNzgudwLf4azpbOfNzyY1kqQAghRKklAaaY2B62nT8v/4kKFbNbzX7s6qtCCCFESScBphi4lXCLGUEzABhWbxhNXJsYuSIhhBDCuCTAFHFavZZJ+yeRqE3Ex8mHkfVHGrskIYQQwujyFGBmzZpFkyZNsLW1xdnZmR49enDp0qVsbVJTUxk9ejRly5bFxsaG3r17c+fOnWxtbty4QdeuXbGyssLZ2Zn33nuPjIyMbG0CAwNp2LAh5ubmVKtWDX9//6f7hMXcopBFnL53GlszW+a0moOpWqbuEUIIIfIUYPbt28fo0aM5fPgwAQEBaLVaOnXqRFJSkqHNuHHj2LRpE7/99hv79u0jPDycXr16GV7X6XR07dqV9PR0Dh06xPLly/H392fq1KmGNmFhYXTt2pW2bdsSEhLC2LFjeeONN9ixY0c+fOTi43DEYX468xMA032n427jbuSKhBBCiKLhmSayu3v3Ls7Ozuzbt49WrVoRFxeHk5MTa9as4eWXXwbg4sWL1K5dm6CgIJo3b862bdt48cUXCQ8Px8XFBYAlS5YwadIk7t69i5mZGZMmTWLLli2cPXvWcKy+ffsSGxvL9u3bc1VbcZ/ILiY1ht4be3Mv5R4v13iZab7TjF2SEEIIUeBye/1+pvsRcXFxADg6OgJw/PhxtFotHTp0MLSpVasWFStWNASYoKAg6tatawgvAH5+fowcOZJz587RoEEDgoKCsu0jq83YsWMfWUtaWhppaWmG7+Pj4wHQarVotdpn+ZiFTq/o+fCvD7mXco8q9lUY5zOu2H0GIYQQ4mnk9nr31AFGr9czduxYWrZsSZ06dQCIjIzEzMwMBweHbG1dXFyIjIw0tHkwvGS9nvXa49rEx8eTkpKCpaXlQ/XMmjWLGTNmPLR9586dWFlZPd2HNJJDqYc4kHoAU0x5Qf8Ce3fuNXZJQgghRKFITk7OVbunDjCjR4/m7NmzHDhw4Gl3ka8mT57M+PHjDd/Hx8fj4eFBp06ditUtpAsxF5i+czoA7zV5j1eqv2LcgoQQQohClHUH5UmeKsCMGTOGzZs3s3//fipUqGDY7urqSnp6OrGxsdl6Ye7cuYOrq6uhzdGjR7PtL2uU0oNt/jty6c6dO9jZ2eXY+wJgbm6Oubn5Q9s1Gg0ajSbvH9IIkrXJfHDoAzL0GbSv2J7/1f6fzLYrhBCiVMntNTtPo5AURWHMmDGsW7eOPXv24Onpme31Ro0aodFo2L17t2HbpUuXuHHjBr6+vgD4+vpy5swZoqKiDG0CAgKws7PDy8vL0ObBfWS1ydpHSTXzyEyux1/HxcqFGS1mSHgRQgghHiFPPTCjR49mzZo1bNiwAVtbW8MzK/b29lhaWmJvb8/QoUMZP348jo6O2NnZ8dZbb+Hr60vz5s0B6NSpE15eXgwYMIC5c+cSGRnJRx99xOjRow09KCNGjODbb79l4sSJvP766+zZs4e1a9eyZcuWfP74Rcfmq5vZGLoRtUrNnFZzsDe3N3ZJQgghRNGl5AGQ49eyZcsMbVJSUpRRo0YpZcqUUaysrJSePXsqERER2fZz7do1pUuXLoqlpaVSrlw55d1331W0Wm22Nnv37lV8fHwUMzMzpUqVKtmOkRtxcXEKoMTFxeXpfcZwI+6G0mx1M6WOfx1l0clFxi5HCCGEMJrcXr+faR6Yoqy4zAOj1WkZuG0gZ6PP0tC5IT/5/SSz7QohhCi1cnv9lrWQjOybk99wNvosdmZ2slSAEEIIkUsSYIzo0O1DLDu3DICPW36Mq7WrkSsSQgghigcJMEZyL+UeHxz4AIA+NfvQvmJ7I1ckhBBCFB8SYIxAr+j56MBHRKdGU71MdSY0nmDskoQQQohiRQKMEaw4t4KD4QexMLHg81afY2FqYeyShBBCiGJFAkwhO3vvLAtOLABgYtOJVHWoauSKhBBCiOJHAkwhSkxPZOL+iWQoGXSs1JGXq79s7JKEEEKIYkkCTCGaeWQmNxNu4mbtxjTfabJUgBBCCPGUJMAUko2hG9l8dTMmKhPmtporSwUIIYQQz0ACTCG4FneNTw9/CsDI+iPxcfYxbkFCCCFEMScBpoCl69KZuH8iKRkpNHFtwht13zB2SUIIIUSxJwGmgC04sYALMRdwMHdg1nOzMFGbGLskIYQQotiTAFOA/rr1FyvOrwDgk5af4GLtYuSKhBBCiJJBVg7MA51e4WhYDFEJqTjbWtDU0xETdc4jie4m3+Wjgx8B0K9WP9p4tCnESoUQQoiSTQJMLm0/G8GMTeeJiEs1bHOzt2BaNy8613HL1lav6PngwAfEpMZQs0xNxjceX9jlCiGEECWa3ELKhe1nIxi56kS28AIQGZfKyFUn2H42Itv2ZWeXcTjiMJamlsxtPRdzE/PCLFcIIYQo8STAPIFOrzBj03mUHF7L2jZj03l0+szvTt89zbcnvwVgctPJVLGvUjiFCiGEEKWIBJgnOBoW81DPy4MUICIulaNhMSSkJxiWCuhcuTM9qvUotDqFEEKI0kSegXmCqIRHh5cH3YlPYV3QHG4n3qa8TXmm+k6VpQKEEEKIAiIB5gmcbS1y1e5M3C62Xd+GicqEOa3mYGtmW8CVCSGEEKWX3EJ6gqaejrjZW/C4vhS1WRRrw74GYIzPGOo71S+c4oQQQohSSgLME5ioVUzr5gXwUIhRAai02Fb8FdRaMpKqERxSn7sJaYVdphBCCFGqSIDJhc513Fj8WkNc7S0wK7cbm1rvY1ZuN672FnRpdRy95jYWaju0EX3YcT6KTl/tY+OpcBQlp7FLQgghhHhWEmByqXMdNwZ2uYy5UwAqFZg7BdCqxU4ORK0HYF7bWWwc+QJebnbcT9by9s8nGbnqhPTGCCGEEAVAAkwuLTm1hEWnFmbbtvXaVgAGeA2gVYVWeLnbsWFMS8Z2qI6pWsX2c5HSGyOEEEIUAAkwubDk1BIWhix85OvWptaGP2tM1IztUIONY56T3hghhBCigEiAeYInhReAJaeXsOTUkmzbpDdGCCGEKDgSYB4jN+Ely8KQhQ+FGOmNEUIIIQqGSimh3QHx8fHY29sTFxeHnZ3dU+2j3vJ6KDmugpQzFSpODzqd42tanZ6Fe6/w7Z4rZOgVylhpmNG9Dt3qucmMvUIIIcQ/cnv9lh6YxxjlMyrf2ktvjBBCCJF/JMA8xoj6IxjtMzpXbUf7jGZE/RFPbCfPxgghhBDPTgLME+QmxOQ2vGSR3hghhBDi2UiAyYXHhZi8hpcHPao3ZpP0xgghhBCPlecAs3//frp164a7uzsqlYr169dne33w4MGoVKpsX507d87WJiYmhv79+2NnZ4eDgwNDhw4lMTExW5vTp0/z/PPPY2FhgYeHB3Pnzs37p8tHOYWYZwkvWXLqjXlLemOEEEKIx8pzgElKSqJ+/fosXPjo4cWdO3cmIiLC8PXzzz9ne71///6cO3eOgIAANm/ezP79+xk+fLjh9fj4eDp16kSlSpU4fvw4n3/+OdOnT+f777/Pa7n5KivEqFDlS3h5kJe7HetHS2+MEEIIkRvPNIxapVKxbt06evToYdg2ePBgYmNjH+qZyXLhwgW8vLwIDg6mcePGAGzfvp0XXniBW7du4e7uzuLFi/nwww+JjIzEzMwMgPfff5/169dz8eLFXNWWH8OojeVceBwTfjvNhYh4ADp7u/JJjzo42ZobuTIhhBCiYOX2+m1aEAcPDAzE2dmZMmXK0K5dOz799FPKli0LQFBQEA4ODobwAtChQwfUajVHjhyhZ8+eBAUF0apVK0N4AfDz82POnDncv3+fMmXKPHTMtLQ00tL+veUSH5958ddqtWi12oL4mAWmhpMVvw9vynf7w1i07yrbz0VyJCyaaS/W5oU6LjJvjBBCiBIrt9fsfA8wnTt3plevXnh6ehIaGsoHH3xAly5dCAoKwsTEhMjISJydnbMXYWqKo6MjkZGRAERGRuLp6ZmtjYuLi+G1nALMrFmzmDFjxkPbd+7ciZWVVX59vEJVFRhXB9ZcMeF2spaxa0+zdJeeVzz12Jk98e1CCCFEsZOcnJyrdvkeYPr27Wv4c926dalXrx5Vq1YlMDCQ9u3b5/fhDCZPnsz48eMN38fHx+Ph4UGnTp2K3S2k/xqcoTf0xpyOUXMz1Vx6Y4QQQpRIWXdQnqRAbiE9qEqVKpQrV44rV67Qvn17XF1diYqKytYmIyODmJgYXF1dAXB1deXOnTvZ2mR9n9Xmv8zNzTE3f/gZEY1Gg0ajyY+PYjQaDYz3q4VfXTfDszFj155m+zl5NkYIIUTJkttrdoHPA3Pr1i2io6Nxc3MDwNfXl9jYWI4fP25os2fPHvR6Pc2aNTO02b9/f7b7YAEBAdSsWTPH20elhbe7PRtkpJIQQgiR9wCTmJhISEgIISEhAISFhRESEsKNGzdITEzkvffe4/Dhw1y7do3du3fTvXt3qlWrhp+fHwC1a9emc+fODBs2jKNHj3Lw4EHGjBlD3759cXd3B6Bfv36YmZkxdOhQzp07x6+//sqCBQuy3SIqrcxMM+eN2TCmJbVl3hghhBClVJ6HUQcGBtK2bduHtg8aNIjFixfTo0cPTp48SWxsLO7u7nTq1IlPPvnE8BAuZE5kN2bMGDZt2oRaraZ37958/fXX2NjYGNqcPn2a0aNHExwcTLly5XjrrbeYNGlSrusszsOocys9Q8+iwOwrXH/cvQ4vygrXQgghiqncXr+faR6Yoqw0BJgsMm+MEEKIkiK3129ZC6kEkGdjhBBClDYSYEoIeTZGCCFEaSIBpoSR3hghhBClgQSYEkh6Y4QQQpR0EmBKsKzemHfaS2+MEEKIkkUCTAlnZqpmXEfpjRFCCFGySIApJaQ3RgghREkiAaYUkd4YIYQQJYUEmFJIemOEEEIUdxJgSinpjRFCCFGcSYAp5aQ3RgghRHEkAUZIb4wQQohiRwKMMJDeGCGEEMWFBBiRjfTGCCGEKA4kwIgcSW+MEEKIokwCjHgk6Y0RQghRVEmAEU8kvTFCCCGKGgkwIlce1xtzL1F6Y4QQQhQuCTAiT3Lqjen4pfTGCCGEKFwSYESePdgbU8vV1tAbM2q19MYIIYQoHBJgxFPzdrdn45jnePuf3phtZ6U3RgghROGQACOeiZmpmvEda7B+tPTGCCGEKDwSYES+qFNeemOEEEIUHgkwIt9Ib4wQQojCIgFG5DvpjRFCCFHQJMCIAiG9MUIIIQqSBBhRoKQ3RgghREGQACMKnPTGCCGEyG8SYEShkd4YIYQQ+UWllNArR3x8PPb29sTFxWFnZ2fscsR/nL0dx4TfTnExMgGALnVc+aRHHcrZmAOg0yscDYshKiEVZ1sLmno6YqJWGbNkIYQQhSC3128JMMJo0jP0fLv3Cov2XiFDr1DGSsPH3etgqlbx8ebzRMSlGtq62VswrZsXneu4GbFiIYQQBU0CjASYYuO/vTE5yep7WfxaQwkxQghRguX2+i3PwAijy3o2Zky7ao9sk5WyZ2w6j05fIjO3EEKIPMhzgNm/fz/dunXD3d0dlUrF+vXrs72uKApTp07Fzc0NS0tLOnTowOXLl7O1iYmJoX///tjZ2eHg4MDQoUNJTEzM1ub06dM8//zzWFhY4OHhwdy5c/P+6USxYWaqpmXVco9towARcakcDYspnKKEEEIUWXkOMElJSdSvX5+FCxfm+PrcuXP5+uuvWbJkCUeOHMHa2ho/Pz9SU/99nqF///6cO3eOgIAANm/ezP79+xk+fLjh9fj4eDp16kSlSpU4fvw4n3/+OdOnT+f7779/io8oiouohNQnN8pDOyGEECWXaV7f0KVLF7p06ZLja4qiMH/+fD766CO6d+8OwIoVK3BxcWH9+vX07duXCxcusH37doKDg2ncuDEA33zzDS+88AJffPEF7u7urF69mvT0dJYuXYqZmRne3t6EhITw5ZdfZgs6omRxtrXI13ZCCCFKrnx9BiYsLIzIyEg6dOhg2GZvb0+zZs0ICgoCICgoCAcHB0N4AejQoQNqtZojR44Y2rRq1QozMzNDGz8/Py5dusT9+/fzs2RRhDT1dMTN3oLHDZYuZ2NGU0/HQqtJCCFE0ZTnHpjHiYyMBMDFxSXbdhcXF8NrkZGRODs7Zy/C1BRHR8dsbTw9PR/aR9ZrZcqUeejYaWlppKX9O6trfHw8AFqtFq1W+ywfSxSiD7vU5K1fTqHi3wd3HxSXomXn2XA61HbO4VUhhBDFXW6v2fkaYIxp1qxZzJgx46HtO3fuxMrKyggViac1pIaKP6+piU3/ty/G3kzBxlThdrKaUWtO0qOyntauCiqZ204IIUqU5OTkXLXL1wDj6uoKwJ07d3Bz+3eujjt37uDj42NoExUVle19GRkZxMTEGN7v6urKnTt3srXJ+j6rzX9NnjyZ8ePHG76Pj4/Hw8ODTp06yTwwxcwLwES9wrHr94lKSMPZ1pzGlcqgKAoztlzkl+BbrLtmgo2LBx92qSUz9AohRAmSdQflSfI1wHh6euLq6sru3bsNgSU+Pp4jR44wcuRIAHx9fYmNjeX48eM0atQIgD179qDX62nWrJmhzYcffohWq0Wj0QAQEBBAzZo1c7x9BGBubo65uflD2zUajWEfovjQAM/VcHlo+6xe9fAsZ8OsbRdZeeQm4XFpfP2/Blibl5jORCGEKNVye83O80O8iYmJhISEEBISAmQ+uBsSEsKNGzdQqVSMHTuWTz/9lI0bN3LmzBkGDhyIu7s7PXr0AKB27dp07tyZYcOGcfToUQ4ePMiYMWPo27cv7u7uAPTr1w8zMzOGDh3KuXPn+PXXX1mwYEG2HhZROqlUKt5sXZVF/Rtibqpm98UoXv0uiDvxMrRaCCFKFSWP9u7dq5D5fGW2r0GDBimKoih6vV6ZMmWK4uLiopibmyvt27dXLl26lG0f0dHRyv/+9z/FxsZGsbOzU4YMGaIkJCRka3Pq1CnlueeeU8zNzZXy5csrs2fPzlOdcXFxCqDExcXl9SOKYuL49Ril4cc7lUqTNivNP9ulXIiQv2shhCjucnv9lrWQRLF2IzqZwf5HuXo3CRtzUxb2b0jrGk7GLksIIcRTkrWQRKlQsawVf45sQTNPRxLTMnjdP5g1R24YuywhhBAFTAKMKPYcrMxYObQZvRqUR6dX+GDdGWZtu4BeFn0UQogSSwKMKBHMTNXMe7U+YztUB+C7fVd56+eTpGp1Rq5MCCFEQZAAI0oMlUrF2A41+PLV+mhMVGw5E0G/Hw4TnZj25DcLIYQoViTAiBKnV8MKrHi9GXYWppy4EUvPRYe4EpVo7LKEEELkIwkwokTyrVqWP0e1xMPRkhsxyfRefIjDV6ONXZYQQoh8IgFGlFjVnG1YN6olDSo6EJeiZcBPR1h38paxyxJCCJEPJMCIEq2cjTk/D2vOC3Vd0eoUxv16ivm7/qaETn8khBClhgQYUeJZaEz49n8NebN1FQDm77rMu7+dIj1Db+TKhBBCPC0JMKJUUKtVTO5Sm8961sVEreLPE7cZuPQIcclaY5cmhBDiKUiAEaVKv2YV+WlQY2zMTTl8NYZeiw9yIzrZ2GUJIYTIIwkwotRpU9OZ30b44mZvQejdJHouOsjJG/eNXZYQQog8kAAjSqXabnasH90Sb3c7opPS6fv9YbadiTB2WUIIIXJJAowotVzsLFj7pi/tajmTlqFn1JoTfL8/VEYoCSFEMSABRpRq1uamfD+gEQN9K6Eo8NnWi3y4/iwZOhmhJIQQRZkEGFHqmZqomfGSN1Ne9EKlgjVHbjB0+TES0zKMXZoQQohHkAAjBJkLQQ59zpMlrzXCQqNm3993eXnxISLiUoxdmhBCiBxIgBHiAX7ervw63JdyNuZcjEygx8KDnL0dZ+yyhBBC/IcEGCH+o76HA+tGtaC6sw134tN49bsg9l6MMnZZQgghHiABRogceDha8fvIFrSsVpbkdB1DlwezMuiascsSQgjxDwkwQjyCvaWGZYOb8kqjCugVmLLhHJ9uPo9OL8OshRDC2CTACPEYZqZq5r5cj/f8agLw44EwRq0+Tkq6zsiVCSFE6SYBRognUKlUjG5bjQV9fTAzUbPj3B36/nCYuwlpxi5NCCFKLQkwQuRSd5/yrB7WDAcrDaduxtJj4UEu30kwdllCCFEqSYARIg+aVHZk3aiWVC5rxe3YFHotPsTBK/eMXZYQQpQ6EmCEyCPPctb8OaoljSuVISE1g0FLj/LbsZvGLksIIUoVCTBCPAVHazNWvdGMbvXdydArvPf7aebtvCQLQQohRCGRACPEU7LQmLCgjw+j21YF4Js9Vxj7awhpGTJCSQghCpoEGCGegVqt4j2/WsztXQ9TtYoNIeEM+PEo95PSjV2aEEKUaBJghMgHrzbxwH9IU2zNTTl6LYZeiw9x7V6SscsSQogSSwKMEPnkuerl+GNUC8o7WBJ2L4meiw5y7FqMscsSQogSSQKMEPmohost60a3oF4Fe+4na+n34xE2nQo3dllCCFHiSIARIp8521rwy/DmdPRyIT1Dz1s/n2Th3isyQkkIIfJRvgeY6dOno1Kpsn3VqlXL8HpqaiqjR4+mbNmy2NjY0Lt3b+7cuZNtHzdu3KBr165YWVnh7OzMe++9R0ZGRn6XKkSBsTIzZclrjXi9pScAn++4xPt/nEGr0xu5MiGEeDY6vUJQaDQbQm4TFBpttAVuTQtip97e3uzatevfg5j+e5hx48axZcsWfvvtN+zt7RkzZgy9evXi4MGDAOh0Orp27YqrqyuHDh0iIiKCgQMHotFo+OyzzwqiXCEKhIlaxdRuXlQqa8WMTef49dhNbsemsOi1hthZaIxdnhBC5Nn2sxHM2HSeiLhUwzY3ewumdfOicx23Qq2lQG4hmZqa4urqavgqV64cAHFxcfz00098+eWXtGvXjkaNGrFs2TIOHTrE4cOHAdi5cyfnz59n1apV+Pj40KVLFz755BMWLlxIeroMTRXFz6AWlflhYGOszEw4cOUeLy8+xK37ycYuSxSCo5uuErwlDICkQ4cI7foiSYcOARC8JYyjm64aszwh8mT72QhGrjqRLbwARMalMnLVCbafjSjUegokwFy+fBl3d3eqVKlC//79uXHjBgDHjx9Hq9XSoUMHQ9tatWpRsWJFgoKCAAgKCqJu3bq4uLgY2vj5+REfH8+5c+cKolwhClz72i6sfdMXZ1tz/r6TSM9FhzhzK87YZYkCplKrOLopjOAtYUR9+RXpoaFEffnVP+ElDJVaZewShcgVnV5hxqbz5HSzKGvbjE3nC/V2Ur7fQmrWrBn+/v7UrFmTiIgIZsyYwfPPP8/Zs2eJjIzEzMwMBweHbO9xcXEhMjISgMjIyGzhJev1rNceJS0tjbS0NMP38fHxAGi1WrRabX58NCGeSU1nK35/sxnDVp7g0p1EXv3uEF++Uo8OtZ2NXZooID6dKqDX6zm6KQzPhAp4cpYLCRUI2xRG466V8OlUQX4/iWLhSFjMQz0vD1KAiLhUgq5E0czT8ZmOldv/J/I9wHTp0sXw53r16tGsWTMqVarE2rVrsbS0zO/DGcyaNYsZM2Y8tH3nzp1YWVkV2HGFyKvXK8KyVDUX42DUmpP0qKynjZuMUCqxFIXy0acJ8+zGtUovoKhN8IjeT6TOh61bzxq7OiFyZV+ECjB5Yrudfx0h+sKz/T5LTs7dLfYCeYj3QQ4ODtSoUYMrV67QsWNH0tPTiY2NzdYLc+fOHVxdXQFwdXXl6NGj2faRNUopq01OJk+ezPjx4w3fx8fH4+HhQadOnbCzs8vHTyTEs+um0zNj80V+PXaLdddMsHGtyIddamIitxRKlIRrdzg2fzNX7RsBoKhNQFEwi43BR2VO2Rc6PGEPQhjX9Zhkvtsfxvrrt3PVvtPzzZ65BybrDsqTFHiASUxMJDQ0lAEDBtCoUSM0Gg27d++md+/eAFy6dIkbN27g6+sLgK+vLzNnziQqKgpn58yu9YCAAOzs7PDy8nrkcczNzTE3N39ou0ajQaORER+iaNFoYHbvelRxsmHWtousPHyDiLhUFvRtgLV5gf9vKQrYvTNhBC87yLWEcuhNavz7m1ZRQKUitGoPrm1Kodrh32n6ZhvsKhfu6A0hniT0biIL915hQ0i44bkWMxM16Y+YCkIFuNpb4FvN+Zn/IZbba7ZKyefZtSZMmEC3bt2oVKkS4eHhTJs2jZCQEM6fP4+TkxMjR45k69at+Pv7Y2dnx1tvvQXAoX+ezNfpdPj4+ODu7s7cuXOJjIxkwIABvPHGG3kaRh0fH4+9vT1xcXHSAyOKtC2nIxi3NoT0DD11ytuxdFATnO0sjF2WeArhB88R/PNJbmtdUFSZ3e3mqfdJsyiDZ9hmKt3YwVmv17nn1MDwHrVeS2WbuzQZ0pJydT2NVboQAPx9J4Fv91xh8+lwsp7HbV3DibfbV+NuQhojV50AyPYwb1ZcWfxaw3wZSp3b63e+B5i+ffuyf/9+oqOjcXJy4rnnnmPmzJlUrVoVyJzI7t133+Xnn38mLS0NPz8/Fi1alO320PXr1xk5ciSBgYFYW1szaNAgZs+enW0+mSeRACOKk+PX7zN8xTGik9Jxt7dg6ZAm1HKVn9viImzrUY5v+Js7KnfDNid9OGZJ0dy2rYtn2CY8r2//t32lLoR5voh5Wixp5g4AqBQd5TV3aPK/Bri39C7sjyBKufPh8Xy79zLbzkaSlQo61HbmrXbVqe/hYGhXGPPAGC3AFBUSYERxcyM6mcH+R7l6Nwkbc1MW9W9IqxpOxi5LPIJer+fvX/dzYnc4903/+QeYosfdNILGL9elfEsvdvT5BCU5MVt4yRJWqTNY21CjXydO7LzBXfW/v/xdlHAada+B5wtNC+vjiFLqzK04vt5zmYDz/86I39nblTHtqlGnvH2O79HpFY6GxRCVkIqzrQVNPR3z9fk9CTASYEQxFJuczpsrj3MkLAYTtYpPe9Thf00rGrss8QBdmpYz/rs5fTSOBE1mwFTpM6hkFUXTgc1waljd0FYbEUFGzKNXJDctWxbNP73PN/eGcOy3s4TrXEGVOUVXmYxIGrZ3p0afVqjVsnSdyD8nbtznm92X2XvpLgAqFbxYz50xbatR09XWqLVJgJEAI4qptAwdk/84w58nM5/6H9G6KhP9aqKWEUpGlZ6YQsh3Ozh7XkeKpgwAJrpUqjrG0uSN1jhUL58vx7l74jJHVxzherIzijrztrmt9i71mtpTd3B7TMxlUIJ4ekfDYvhmz2X+unwPALUKuvuUZ3TbalRztjFydZkkwEiAEcWYoigs2H2Z+bsuA9C1nhvzXqmPhebJ8zCI/JUcdZ/j3+3i4jUz0jWZ/zLVZCRRs0IyTd7sgJVb2QI5buzlWxz9YT9X7zugM8l8qNtSe586Xib4vOmHmU3BzaslShZFUQi6Gs3Xuy9z+Gpmj6CpWkXPBpnBpXI5ayNXmJ0EGAkwogT44/gt3v/zNFqdQsOKDvwwsDFlbR6eLkDkv/hrEQR/H8jlKHt0ppkBwkIbh3cNaDCiI+b2hfOv1aTwexz7fjeXblmhNc280JhpE6hVOZ1Gb3bAyrlModQhih9FUdh/+R7f7L7Msev3AdCYqHi5kQej2lTFw7FoTvIqAUYCjCghgkKjeXPlMeJTM6joaMWyIU2o6lQ0unpLIsMcLolO6NWZt2tstPeo29CaekM7YGphnACZdj+Bk9/v4tzfkKrJfLjSJCOV6s5xNBkuc8mIfymKwp6LUXy95wqnbsYCYGaqpm8TD0a0roq7Q9HuvZMAIwFGlCBXohIZ4n+UmzEp2Ftq+H5AI5pVKZhbF6VVTnO4OGTcwaeVM7X7tUFtWjRu32WkpnH6p12cPpFMkibzZ0DmkhEAer3CzvN3+GbPZc6FZ85ma6FR069pJd5sXQWXYjK/lAQYCTCihLmXmMYby48RcjMWMxM1c1+uR48G+fPgaGmW8xwuETR8wZMqLzUvsqN/9Bk6LqwJJGR/FLGmmQveylwypZNOr7DtbATf7rnCxcgEAKzMTBjQvBJvPF8FJ9viddtZAowEGFECpWp1jPs1hG1nM1dmH9+xBm+1q4ZKJSOU8uJJc7h4tPUxan15odfrubrxMCe2hslcMqWMTq+w+XQ43+y5wpWoRABszE0Z1KISQ5+rgqO1mZErfDoSYCTAiBJKr1eYs/0i3+2/CkDvhhWY1asuZqZFs6egKMnLHC7FkcwlUzpodXo2hISzcO8Vwu4lAWBnYcqQlp683tITe6viPdReAowEGFHCrT5ynakbzqHTK/hWKcuS1xoV+19cBaWw5nApKmQumZIpPUPPHydusSjwCjdjUgBwsNLwxnOeDGxRGTuLkvH3KgFGAowoBQIvRTF69QmS0nVUdbLGf0jTIjs00hiMNYdLUSFzyZQMqVodvx27yeLAUML/WYOorLUZw1pV4bXmlbApYSvYS4CRACNKifPh8bzuH0xkfCrlbMz4YWBjGlQs3XODFJU5XIoKmUumeErV6lhz5Abf7Q/lTnwaAE625rzZqgr9mlXEyqxkBZcsEmAkwIhSJDIulaHLgzkXHo+5qZoFfX3ybWXY4qSozuFSVMhcMsVDcnoGqw/f4Lv9V7mXmBlc3OwtGNG6Kn2aeJT4GbklwEiAEaVMUloGb/18kj0Xo1Cp4IMutXnjec9SMUKpuMzhUlTIXDJFU2JaBiuCrvHjX2HEJKUDUN7BklFtq/JyowqYl5KfYwkwEmBEKZSh0/Px5vOsCLoOwGvNKzK9mzemJiVz9ElxncOlqJC5ZIqGuBQt/gevsfRgGHEpWgAqlbVidJtq9GxYHk0J/f/3USTASIARpZSiKPx0IIyZWy+gKNCmphPf9mtYYh70K0lzuBQVMpeMcdxPSmfpwTD8D14jIS0DgCpO1oxpW42X6ruX2H94PIkEGAkwopTbcS6Sd345SapWT203O5YOboybffEddVLS53ApKmQumYIXnZjGD3+FsTLoGknpOgCqO9vwVvvqdK3rhom65N/2fRwJMBJghODUzViGLg/mXmI6LnbmLB3cBG93e2OXlSelbQ6XokLmksl/UQmp/LD/KqsO3yBFmxlcarvZ8Xa7avh5u6Iu5cEliwQYCTBCAHAzJpnX/YO5HJWItZkJ3/ZrSNtazsYu64lK+xwuRYXMJfPsIuNSWbIvlJ+P3iAtQw9AvQr2vNWuOh1qO5eKB+3zQgKMBBghDOJStIxafZyDV6JRq2BG9zoMaF4JnV7haFgMUQmpONta0NTT0ejd1zKHS9Ekc8nk3a37ySwODOW3Y7dI12UGlwYVHXi7fXXa1HCS4PIIEmAkwAiRTXqGng/XneG347cA6FDbmbO344mMTzW0cbO3YFo3L6PMISNzuBQPMpfMk12PTmLR3lD+OHGLDH3mJbZpZUfebl+dltXKSnB5AgkwEmCEeIiiKCzce4Uvdv6d4+tZv1YXv9aw0EKMzOFSPMlcMg+7ejeRb/deYUNIOLp/gkuLqmV5u311mleRW565JQFGAowQOdLpFRp9EkDsP/NN/JcKcLW34MCkdgV6O0nmcCkZZC4ZuHwngW/2XGHz6XD+yS20quHE2+2q0biyo3GLK4YkwEiAESJHQaHR/O+Hw09s92arKnTydqWak02+rXItc7iUXKVxLpkLEfF8u+cKW89GkHUlbV/LmbfaV8fHw8GotRVnEmAkwAiRow0ht3nnl5A8vaecjRlVnGyo5mxDVScbqjpZU9XJhvIOlrka+ilzuJQuJX0umbO34/h692V2nr9j2Obn7cJb7apTp3zxmqagKJIAIwFGiBzltgemjrsd0UnpRMSlPrKNhUaNZ7msYGP9T7ixoYqTNRYaE5nDpZQraXPJnLxxn2/2XGHPxSgAVCroWteNMe2qUctVrjP5RQKMBBghcqTTKzw3Zw+Rcank9D//f5+BSUzLIOxuElfuJhAalUTo3URC7yZy7V6yYWjof5XRptIv5i72aje0WXO4aJOo4paE76gOWLuXK7gPKIqc4j6XTPC1GL7efZm/Lt8DQK2Cl+q7M6ZdNao52xq5upJHAowEGCEeafvZCEauOgGQLcTkZRRShk7PrfsphN5N5EpUZqi5dyWcOueuYWVeEZ1p5kVJk3afWxl3+MPZjSSNGfaWGqo6WT9wO8qGqs42eJSxLLVrv5QWxWkuGUVRCLoazde7L3P4agwAJmoVPRuUZ3TbaniWszZyhSWXBBgJMEI81vazEczYdD7bLaKnnQcmpzlcrNLvYeOhcMmnFldi0wi9m8it+yk86jeOxkRF5bKZt6GqOdtQ1dn6n9tRNiVmIUqRqSjPJaMoCn9dvsc3ey4TfO0+kPmz+XKjCoxsXY2KZa2MVltpIQFGAowQT/SsM/HmdQ6XVK2OsHtJhh6b0LtJhEYlcvVeIqnanG9HAbjaWRgCzYM9Ny525jIpWDFWlOaSURSFvZei+Hr3FUJuxgJgZqKmTxMPRrSpSnmHon2bqySRACMBRogCk99zuOj1CuFxKf8Em3+es/nnz/cS0x75PmszE6r+E2gefJC4UllrzEzldlRxYcy5ZPR6hYALd/hmz2XO3o4HwNxUTb9mFXmzVVVc7S0K7NgiZxJgJMAIka+MNYdLXLKWK3cTDQ8Ph0YlcfVuItdjkg2znf6XiVpFRUerf0dGZYWcfJzTRuS/R84lQziNXsrfuWT0eoVtZyP5Zs9lLkYmAGCpMWGAbyXeeN4TZ1sJLsYiAUYCjBD5oqjO4ZKeoedGTNK/vTYP3JZKTMt45Puy5rT5b69Nbue0EYUjx7lkdJE0bPdsc8no9AqbT4fzzZ4rXIlKBMDG3JSBvpUY+pwnZW1kza1HObrpKiq1iiZdH761F/z/9u4+Kqpy3wP4dw/MDK+BYqO8SxAgGoQvcBGpY3pUVrFkVeYtUnype3XhW6SlnT/MVoker/eoaZjWhc5tebQ0Fc3XXELHSkU83HwXcQxU0BDlZdBhmL3vHwqKiIIMezO772etWbL37P3Mb35rYH4+z7Of/b0RkighJumpDr8OCxgWMEQdYq9ruEiShCvV5nt6bO4OS7VlTZvGgqZxrk3jmjYd0RXv+m0v2rOWzMPy3GAVsaXwMj7bfw7nK0y323FyxMT4IEyK7w1PF538b87O5H9vxOFtRsQkBTUrYlrb/7hUUcCsWrUKS5YsQXl5OaKiovDpp58iJqZtXYi2LGDurTpNP/+M8k8WotdfPoDr4ME2rTqJuZbLw/L88zencCm/GDcqrahvXMOlwYQwvzoM+s/hcPG235vS1ZobcP6eoajGIsdYYYLF+uA/hYIA+Ho633PJtytC7gxLebnqHjmJ2JZXe/2RPWwtGWvvCBirb+HT8t/Rs+hXTPl1C1ZHJuPK05FIMzyJeosV2dU3UFJZBwDwdNFicnwQUuN74wknDim2R2OxEv2sI3pu+yuuJL2HfxU22Kx4Adr+/d1lr03csGED0tPTsXr1asTGxmLZsmUYOXIkzpw5A4PBIGssgkbA4W1GAECPrL+hvrgYV//7b6i47t1UdZJtMNfyeFCeS/5rJf4voALXbrkBcAG0gJOlCn1Dgegpf4bew03ZoG3ATe+ISD9PRPp5NtvfYBVRev3mPcNQt3ttzl2tRdVNCy5ev4mL128i7+zvzc5rXNOmcZ5NyH1r2jSut3N/aVRedQtTvz4q612/7Z3n034Y8dc3mq0lc1PbDflFgOZ0MUQHPcIdb2H0yZ0IrL2KCSd3YoN7H1T9VoEDThaUODXAy1WHtxKewri4wC53ab4oShCtIkSrdM/j7rb1Yc813PlZbP0Y0SrC+pDnHnSetZXn9C6O+FdhA+D3DmDj4qU9umwPTGxsLAYNGoSVK1cCuD25y9/fH9OnT8fcuXMfeb6th5Aaq86A3/bA9/I/cdH3TygNGIZnYruhX0zXWXxJDY4fvo5jh67Dv2Qf/C7lMtedpDHPvhfzAAG47B3f1EXvZqnAM/1dETl5OByd/rhzAiRJwjVTfbNhqMbHo9a0CezugtLrN2FuaP3ycIO7Ht9OieNw0mOwVNXC+HUejEYH3NJ6Nu33uFGE8LP/wCXvBFz0H4rTmloUO9Th1Wg/DAnuAa1Gc+fLXoJobSwc7myLuOe5+7YfdHybjsE9z7X+GvZKEK0YP84ZbglDbNamXQ8h1dfXw8XFBRs3bkRycnLT/tTUVNy4cQNbt25tcY7ZbIbZfPdyy+rqavj7+6OiosImBYwkScidvBRFrrEdbouoq9KbbyDmOTeE/ftzLdZwoeZu1ltx4VodzleYcP53E4rv/Gu8ZnromjZkW1qrFclXL+FpdIfF+Umlw7EpQWyAIFmhkawQRCsEyQpBEptta6Q7+x+wrZHEO+c0blubtjXi3Z9vb9899m77DbfbuK/tcsMglPnEQxAbIGkc8bTpEP705bs2W5OpuroaPXr0sM8hpIqKClitVvTs2bPZ/p49e+L06dMPPCcjIwMLFixosX/Pnj1wcen4yokuZ87CP//vKHp+0O0Z8ZIEjWjpcLvUOlGjvT0BgbnuVI15FsQGDP7lL7jUZxKMe24qHZbdEAAEAwh2A+AGiL2B62bgpysa7Lv86CtlHCCBHTCPT3TQ4FvvAAy4cgozj32HY/3+o+nvhtZSC0G674u5cfu+L/T7C4MOHS9aoZEamr+22JZz754vQERX/Fhc8PszynziEWTchqDfdsEYOApFQUmonL8Bzv9mmwtm6urq2nRclyxgHse8efOQnp7etN3YAzNixIgO98BIkoSLf/9fnO6dCAgaCKIFkkaLwNI9CHctgd8/1nE1UBuRJAkXX38Dp00BMPZ+ibnuJK3l+ULvRIQfOgS/WTOZ5w4KNVZi3/8ceeRxX00ahNig7jJEpE6HjJV488t8jDu1BzUe/e4U47c/z76X8mCpOo5Zz8/A15OZ544q2PkbLuwoQdCF7Qj6bRcA3P5XEGDESxggBGBAYmCHX6e6urpNx3XJAqZHjx5wcHDAlStXmu2/cuUKevXq9cBz9Ho99PqWY/VarRZabcdmmdf+8wBO1/rDGPRSs6rTGJQEGLfBcOiwTcf//siYa3kwz50vLsQAbw+nR971Oy7EwDkwHRAXYsBwkxE6j74P/DwHGSUMNxkRF/Ii89xBDaUXm/J7r6ALOwFJQkPpaGi1IR1+nbZ+Z3fJtbZ1Oh0GDBiAffv2Ne0TRRH79u1DXFycrLFIkoSf1xy484uwrVnVGWTcBmNQEn5ecwBdcCqR3WGu5cE8y8NBI2B+UgQAtBgKaNyenxTBL9UO0gjA6GvVD/08j75WzWG6DpIkCb77VyGoZPcDnw8q2Q3f/atk/bvRJXtgACA9PR2pqakYOHAgYmJisGzZMphMJkycOFHWOCSLBdZaE4KuPqDqvLNtdXWDZLFA0HEhpI5gruXBPMtnVD9vZL7Zv8U6ML24DozNSBYLHG/dQlBl659ngZ/nDpMsFljKytDqpXeSBEt5uax57pJXITVauXJl00J2zz77LFasWIHY2LZdBWTLy6gtZWVoqKxs9XlHLy9oWxnaovZhruXBPMuLK/F2rsbPsygBJy5VobKuHt1ddOjr6wGNwM+zrcj1d8OuL6O2Bd5KgIiIyP609fu7S86BISIiInoYFjBERERkd1jAEBERkd1hAUNERER2hwUMERER2R0WMERERGR3WMAQERGR3WEBQ0RERHaHBQwRERHZnS57L6SOalxguK235SYiIiLlNX5vP+pGAaotYGpqagAA/v7+CkdCRERE7VVTUwMPD49Wn1ftvZBEUcTly5fh7u4OQbDdTdOqq6vh7++P0tJS3mOpkzHX8mCe5cE8y4N5lkdn5lmSJNTU1MDHxwcaTeszXVTbA6PRaODn59dp7T/xxBP85ZAJcy0P5lkezLM8mGd5dFaeH9bz0oiTeImIiMjusIAhIiIiu8MCpp30ej3mz58PvV6vdCiqx1zLg3mWB/MsD+ZZHl0hz6qdxEtERETqxR4YIiIisjssYIiIiMjusIAhIiIiu8MChoiIiOwOC5h2+PHHH5GUlAQfHx8IgoAtW7YoHZLqZGRkYNCgQXB3d4fBYEBycjLOnDmjdFiqk5mZicjIyKZFqOLi4rBz506lw1K9RYsWQRAEzJo1S+lQVOfDDz+EIAjNHuHh4UqHpUqXLl3Cm2++CS8vLzg7O+OZZ57BkSNHZI+DBUw7mEwmREVFYdWqVUqHolp5eXlIS0vDwYMHsXfvXlgsFowYMQImk0np0FTFz88PixYtQkFBAY4cOYIXXngBo0ePxokTJ5QOTbXy8/Px+eefIzIyUulQVKtv374oKytrehw4cEDpkFTn+vXriI+Ph1arxc6dO3Hy5EksXboU3bp1kz0W1d5KoDMkJiYiMTFR6TBUbdeuXc22s7OzYTAYUFBQgOeee06hqNQnKSmp2fYnn3yCzMxMHDx4EH379lUoKvWqra1FSkoK1q5di48//ljpcFTL0dERvXr1UjoMVVu8eDH8/f2RlZXVtC8oKEiRWNgDQ11aVVUVAKB79+4KR6JeVqsV69evh8lkQlxcnNLhqFJaWhpefPFFDB8+XOlQVK2oqAg+Pj546qmnkJKSgpKSEqVDUp2cnBwMHDgQY8aMgcFgQHR0NNauXatILOyBoS5LFEXMmjUL8fHx6Nevn9LhqM6xY8cQFxeHW7duwc3NDZs3b0ZERITSYanO+vXrcfToUeTn5ysdiqrFxsYiOzsbYWFhKCsrw4IFC5CQkIDjx4/D3d1d6fBU4/z588jMzER6ejo++OAD5OfnY8aMGdDpdEhNTZU1FhYw1GWlpaXh+PHjHMfuJGFhYSgsLERVVRU2btyI1NRU5OXlsYixodLSUsycORN79+6Fk5OT0uGo2r3D+5GRkYiNjUVgYCC++eYbTJ48WcHI1EUURQwcOBALFy4EAERHR+P48eNYvXq17AUMh5CoS5o2bRq2b9+O/fv3w8/PT+lwVEmn0yEkJAQDBgxARkYGoqKisHz5cqXDUpWCggJcvXoV/fv3h6OjIxwdHZGXl4cVK1bA0dERVqtV6RBVy9PTE6GhoTh37pzSoaiKt7d3i//k9OnTR5HhOvbAUJciSRKmT5+OzZs3Izc3V7HJYX9EoijCbDYrHYaqDBs2DMeOHWu2b+LEiQgPD8f7778PBwcHhSJTv9raWhQXF2PcuHFKh6Iq8fHxLZa2OHv2LAIDA2WPhQVMO9TW1jar5o1GIwoLC9G9e3cEBAQoGJl6pKWlYd26ddi6dSvc3d1RXl4OAPDw8ICzs7PC0anHvHnzkJiYiICAANTU1GDdunXIzc3F7t27lQ5NVdzd3VvM33J1dYWXlxfnddnY7NmzkZSUhMDAQFy+fBnz58+Hg4MDXn/9daVDU5V33nkHgwcPxsKFC/Haa6/h8OHDWLNmDdasWSN/MBK12f79+yUALR6pqalKh6YaD8ovACkrK0vp0FRl0qRJUmBgoKTT6aQnn3xSGjZsmLRnzx6lw/pDeP7556WZM2cqHYbqjB07VvL29pZ0Op3k6+srjR07Vjp37pzSYanStm3bpH79+kl6vV4KDw+X1qxZo0gcgiRJkvxlExEREdHj4yReIiIisjssYIiIiMjusIAhIiIiu8MChoiIiOwOCxgiIiKyOyxgiIiIyO6wgCEiIiK7wwKGiOzWhAkTkJyc3KE2cnNzIQgCbty4YZOYiEgeLGCISBYTJkyAIAgQBKHpRpIfffQRGhoaHrvN5cuXIzs723ZBEpHd4L2QiEg2o0aNQlZWFsxmM3bs2IG0tDRotVrMmzevXe1YrVYIggAPD49OipSIujr2wBCRbPR6PXr16oXAwEBMnToVw4cPR05ODsxmM2bPng1fX1+4uroiNjYWubm5TedlZ2fD09MTOTk5iIiIgF6vR0lJSYshJLPZjBkzZsBgMMDJyQlDhgxBfn5+sxh27NiB0NBQODs7Y+jQobhw4YI8b56IbIoFDBEpxtnZGfX19Zg2bRp++eUXrF+/Hr/++ivGjBmDUaNGoaioqOnYuro6LF68GF988QVOnDgBg8HQor333nsPmzZtwldffYWjR48iJCQEI0eORGVlJQCgtLQUL7/8MpKSklBYWIi33noLc+fOle39EpHtsIAhItlJkoQffvgBu3fvRmRkJLKysvDtt98iISEBwcHBmD17NoYMGYKsrKymcywWCz777DMMHjwYYWFhcHFxadamyWRCZmYmlixZgsTERERERGDt2rVwdnbGl19+CQDIzMxEcHAwli5dirCwMKSkpGDChAlyvnUishHOgSEi2Wzfvh1ubm6wWCwQRRFvvPEGXn31VWRnZyM0NLTZsWazGV5eXk3bOp0OkZGRrbZdXFwMi8WC+Pj4pn1arRYxMTE4deoUAODUqVOIjY1tdl5cXJwt3hoRyYwFDBHJZujQocjMzIROp4OPjw8cHR2xYcMGODg4oKCgAA4ODs2Od3Nza/rZ2dkZgiDIHTIRdVEsYIhINq6urggJCWm2Lzo6GlarFVevXkVCQsJjtx0cHAydToeffvoJgYGBAG4PO+Xn52PWrFkAgD59+iAnJ6fZeQcPHnzs1yQi5XAODBEpKjQ0FCkpKRg/fjy+++47GI1GHD58GBkZGfj+++/b3I6rqyumTp2KOXPmYNeuXTh58iTefvtt1NXVYfLkyQCAKVOmoKioCHPmzMGZM2ewbt06riNDZKdYwBCR4rKysjB+/Hi8++67CAsLQ3JyMvLz8xEQENCudhYtWoRXXnkF48aNQ//+/XHu3Dns3r0b3bp1AwAEBARg06ZN2LJlC6KiorB69WosXLiwM94SEXUyQZIkSekgiIiIiNqDPTBERERkd1jAEBERkd1hAUNERER2hwUMERER2R0WMERERGR3WMAQERGR3WEBQ0RERHaHBQwRERHZHRYwREREZHdYwBAREZHdYQFDREREdocFDBEREdmd/weNRS/S7RB5DQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Over time cost drops to from \\$6 to \\$4.1 per hour"
      ],
      "metadata": {
        "id": "TxUqdEykd5iI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Gurobi model\n",
        "model = Model(\"MinimizeCost\")\n",
        "\n",
        "H = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"H_{t}\") for t in range(0, n_months+1)}\n",
        "L = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"L_{t}\") for t in range(0, n_months+1)}\n",
        "W = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"W_{t}\") for t in range(0, n_months+1)}\n",
        "O = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"O_{t}\") for t in range(0, n_months+1)}\n",
        "I = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"I_{t}\") for t in range(0, n_months+1)}\n",
        "S = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"S_{t}\") for t in range(0, n_months+1)}\n",
        "C = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"C_{t}\") for t in range(0, n_months+1)}\n",
        "P = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"P_{t}\") for t in range(0, n_months+1)}\n",
        "D = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"D_{t}\") for t in range(0, n_months+1)}\n",
        "\n",
        "# Update the model\n",
        "model.update()\n",
        "\n",
        "# Set objective function - Total cost = Production & Transportation cost across network + Fixed cost of low cap line + fixed cost of high cap line\n",
        "model.setObjective(\n",
        "    quicksum( 300*H[t] + 500*L[t] + 640*W[t] + 4.1*O[t] + 2*I[t] + 5*S[t] + 30*C[t] + 10*P[t] for t in range(1, n_months+1) ),\n",
        "    GRB.MINIMIZE)\n",
        "\n",
        "### Constraints\n",
        "for t in range(1, n_months+1):\n",
        "\n",
        "  # Workforce size for each month is based on hiring and layoffs, W_t = W_{t-1} + H_t - L_t\n",
        "  model.addConstr(W[0] == 80, name=f\"Workforce_Beg\")\n",
        "  model.addConstr(W[t] - W[t-1] - H[t] + L[t] == 0, name=f\"Workforce_size_constraint_{t}\")\n",
        "\n",
        "  # Demand constraints\n",
        "  if promote == 0:\n",
        "      model.addConstr(D[t] == F[t], name=f\"D_constraint_{t}\")\n",
        "  else:\n",
        "      if t == month:\n",
        "          model.addConstr(D[t] == F[t] * (1 + consumption) + forward_buy * (F[t+1] + F[t+2]), name=f\"D_constraint_{t}\")\n",
        "      else:\n",
        "          if t <= 2 + promote:\n",
        "              if t > promote:\n",
        "                  model.addConstr(D[t] == (1 - forward_buy) * F[t], name=f\"D_constraint_{t}\")\n",
        "              else:\n",
        "                  model.addConstr(D[t] == F[t], name=f\"D_constraint_{t}\")\n",
        "          else:\n",
        "              model.addConstr(D[t] == F[t], name=f\"D_constraint_{t}\")\n",
        "\n",
        "  # Inventory balance constraint: I_{t-1} + P_t + C_t = D_t + S_{t-1} + I_t - S_t\n",
        "  model.addConstr(I[0] == 1000, name=f\"Inventory_Beg\")\n",
        "  model.addConstr(I[t-1] + P[t] + C[t] - D[t] - S[t-1] - I[t] + S[t] == 0, name=f\"Inventory_constraint_{t}\")\n",
        "  model.addConstr(I[n_months] >= 500, name=f\"Inventory_End\")\n",
        "\n",
        "  # Stockout constraint: No stockout at the end\n",
        "  model.addConstr(S[n_months] == 0, name=f\"Stockout_End\")\n",
        "\n",
        "  # Production <= Capacity P_t <= 40 W_t + O_t/4\n",
        "  model.addConstr(40*W[t] + O[t]/4 - P[t] >= 0, name=f\"Capacity_constraint_{t}\")\n",
        "\n",
        "  # Overtime constraint O_t <= 10 W_t\n",
        "  model.addConstr(10*W[t] - O[t] >= 0, name=f\"Overtime_constraint_{t}\")\n",
        "\n",
        "\n",
        "# Suppress optimization output\n",
        "model.Params.OutputFlag = 0\n",
        "\n",
        "# Optimize the model\n",
        "model.optimize()\n",
        "\n",
        "### Calculate price\n",
        "Pr = np.zeros(n_months+1)\n",
        "for t in range(0, n_months + 1):\n",
        "  if promote == 1 and t == month:\n",
        "    Pr[t] = promotion_price\n",
        "  else:\n",
        "    Pr[t] = base_price\n",
        "  if t == 0:\n",
        "    Pr[t] = np.nan\n",
        "\n",
        "### Total revenue = sumproduct (demand*price)\n",
        "revenue = sum([D[t].x * Pr[t] for t in range(1, n_months + 1)])\n",
        "\n",
        "# Store result in table format and print\n",
        "opt_results = [[t] + [H[t].x] + [L[t].x] + [W[t].x] + [O[t].x] + [I[t].x] + [S[t].x] + [C[t].x] + [P[t].x] + [D[t].x] + [Pr[t]] for t in range(0, n_months + 1)]\n",
        "\n",
        "headers = [\"Period\\n(t)\"] + [\"Hired\\n(H)\"] + [\"Laid Off\\n(L)\"] + [\"Workforce\\n(W)\"] + [\"Overtime\\n(O)\"] + [\"Inventory\\n(O)\"] + [\"Stockout\\n(S)\"] + [\"Subcontract\\n(C)\"] + [\"Production\\n(P)\"] + [\"Demand\\n(D)\"] + [\"Price\\n(Pr)\"]\n",
        "print(tabulate(opt_results, headers=headers, tablefmt=\"pretty\", stralign=\"right\"))\n",
        "\n",
        "print(f\"Minimum Cost : $ {model.objVal:,.2f}\")\n",
        "print(f\"Total Revenue: $ {revenue:,.2f}\")\n",
        "print(f\"Profit       : $ {(revenue-model.objVal):,.2f}\")\n",
        "\n",
        "plot_inventory()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "SCGA7kbweNVK",
        "outputId": "a48b368e-b326-4f8e-f74a-4a2dd210222c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+-------+----------+-----------+----------+-----------+----------+-------------+------------+--------+-------+\n",
            "| Period | Hired | Laid Off | Workforce | Overtime | Inventory | Stockout | Subcontract | Production | Demand | Price |\n",
            "|    (t) |   (H) |      (L) |       (W) |      (O) |       (O) |      (S) |         (C) |        (P) |    (D) |  (Pr) |\n",
            "+--------+-------+----------+-----------+----------+-----------+----------+-------------+------------+--------+-------+\n",
            "|      0 |  -0.0 |     -0.0 |      80.0 |     -0.0 |    1000.0 |      0.0 |        -0.0 |       -0.0 |   -0.0 |   nan |\n",
            "|      1 |   0.0 |     16.0 |      64.0 |     -0.0 |    1960.0 |     -0.0 |        -0.0 |     2560.0 | 1600.0 |  40.0 |\n",
            "|      2 |   0.0 |     -0.0 |      64.0 |     -0.0 |    1520.0 |     -0.0 |         0.0 |     2560.0 | 3000.0 |  40.0 |\n",
            "|      3 |   0.0 |     -0.0 |      64.0 |     -0.0 |     880.0 |     -0.0 |         0.0 |     2560.0 | 3200.0 |  40.0 |\n",
            "|      4 |   0.0 |     -0.0 |      64.0 |    560.0 |      -0.0 |    220.0 |         0.0 |     2700.0 | 3800.0 |  40.0 |\n",
            "|      5 |   0.0 |     -0.0 |      64.0 |     -0.0 |     140.0 |     -0.0 |         0.0 |     2560.0 | 2200.0 |  40.0 |\n",
            "|      6 |   0.0 |     -0.0 |      64.0 |     -0.0 |     500.0 |      0.0 |         0.0 |     2560.0 | 2200.0 |  40.0 |\n",
            "+--------+-------+----------+-----------+----------+-----------+----------+-------------+------------+--------+-------+\n",
            "Minimum Cost : $ 422,156.00\n",
            "Total Revenue: $ 640,000.00\n",
            "Profit       : $ 217,844.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Increased demand fluctuation (April peak to 4800)"
      ],
      "metadata": {
        "id": "7MjQUs7ld5qY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "F = np.array([0, 1600, 3000, 3200, 4800, 2200, 2200])\n",
        "\n",
        "### Variables based on input\n",
        "n_months = len(F)-1\n",
        "F = np.append(F, [0, 0])\n",
        "\n",
        "# Create a Gurobi model\n",
        "model = Model(\"MinimizeCost\")\n",
        "\n",
        "H = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"H_{t}\") for t in range(0, n_months+1)}\n",
        "L = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"L_{t}\") for t in range(0, n_months+1)}\n",
        "W = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"W_{t}\") for t in range(0, n_months+1)}\n",
        "O = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"O_{t}\") for t in range(0, n_months+1)}\n",
        "I = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"I_{t}\") for t in range(0, n_months+1)}\n",
        "S = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"S_{t}\") for t in range(0, n_months+1)}\n",
        "C = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"C_{t}\") for t in range(0, n_months+1)}\n",
        "P = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"P_{t}\") for t in range(0, n_months+1)}\n",
        "D = {(t): model.addVar(vtype=GRB.INTEGER, lb=0, name=f\"D_{t}\") for t in range(0, n_months+1)}\n",
        "\n",
        "# Update the model\n",
        "model.update()\n",
        "\n",
        "# Set objective function - Total cost = Production & Transportation cost across network + Fixed cost of low cap line + fixed cost of high cap line\n",
        "model.setObjective(\n",
        "    quicksum( 300*H[t] + 500*L[t] + 640*W[t] + 6*O[t] + 2*I[t] + 5*S[t] + 30*C[t] + 10*P[t] for t in range(1, n_months+1) ),\n",
        "    GRB.MINIMIZE)\n",
        "\n",
        "### Constraints\n",
        "for t in range(1, n_months+1):\n",
        "\n",
        "  # Workforce size for each month is based on hiring and layoffs, W_t = W_{t-1} + H_t - L_t\n",
        "  model.addConstr(W[0] == 80, name=f\"Workforce_Beg\")\n",
        "  model.addConstr(W[t] - W[t-1] - H[t] + L[t] == 0, name=f\"Workforce_size_constraint_{t}\")\n",
        "\n",
        "  # Demand constraints\n",
        "  if promote == 0:\n",
        "      model.addConstr(D[t] == F[t], name=f\"D_constraint_{t}\")\n",
        "  else:\n",
        "      if t == month:\n",
        "          model.addConstr(D[t] == F[t] * (1 + consumption) + forward_buy * (F[t+1] + F[t+2]), name=f\"D_constraint_{t}\")\n",
        "      else:\n",
        "          if t <= 2 + promote:\n",
        "              if t > promote:\n",
        "                  model.addConstr(D[t] == (1 - forward_buy) * F[t], name=f\"D_constraint_{t}\")\n",
        "              else:\n",
        "                  model.addConstr(D[t] == F[t], name=f\"D_constraint_{t}\")\n",
        "          else:\n",
        "              model.addConstr(D[t] == F[t], name=f\"D_constraint_{t}\")\n",
        "\n",
        "  # Inventory balance constraint: I_{t-1} + P_t + C_t = D_t + S_{t-1} + I_t - S_t\n",
        "  model.addConstr(I[0] == 1000, name=f\"Inventory_Beg\")\n",
        "  model.addConstr(I[t-1] + P[t] + C[t] - D[t] - S[t-1] - I[t] + S[t] == 0, name=f\"Inventory_constraint_{t}\")\n",
        "  model.addConstr(I[n_months] >= 500, name=f\"Inventory_End\")\n",
        "\n",
        "  # Stockout constraint: No stockout at the end\n",
        "  model.addConstr(S[n_months] == 0, name=f\"Stockout_End\")\n",
        "\n",
        "  # Production <= Capacity P_t <= 40 W_t + O_t/4\n",
        "  model.addConstr(40*W[t] + O[t]/4 - P[t] >= 0, name=f\"Capacity_constraint_{t}\")\n",
        "\n",
        "  # Overtime constraint O_t <= 10 W_t\n",
        "  model.addConstr(10*W[t] - O[t] >= 0, name=f\"Overtime_constraint_{t}\")\n",
        "\n",
        "\n",
        "# Suppress optimization output\n",
        "model.Params.OutputFlag = 0\n",
        "\n",
        "# Optimize the model\n",
        "model.optimize()\n",
        "\n",
        "### Calculate price\n",
        "Pr = np.zeros(n_months+1)\n",
        "for t in range(0, n_months + 1):\n",
        "  if promote == 1 and t == month:\n",
        "    Pr[t] = promotion_price\n",
        "  else:\n",
        "    Pr[t] = base_price\n",
        "  if t == 0:\n",
        "    Pr[t] = np.nan\n",
        "\n",
        "### Total revenue = sumproduct (demand*price)\n",
        "revenue = sum([D[t].x * Pr[t] for t in range(1, n_months + 1)])\n",
        "\n",
        "# Store result in table format and print\n",
        "opt_results = [[t] + [H[t].x] + [L[t].x] + [W[t].x] + [O[t].x] + [I[t].x] + [S[t].x] + [C[t].x] + [P[t].x] + [D[t].x] + [Pr[t]] for t in range(0, n_months + 1)]\n",
        "\n",
        "headers = [\"Period\\n(t)\"] + [\"Hired\\n(H)\"] + [\"Laid Off\\n(L)\"] + [\"Workforce\\n(W)\"] + [\"Overtime\\n(O)\"] + [\"Inventory\\n(O)\"] + [\"Stockout\\n(S)\"] + [\"Subcontract\\n(C)\"] + [\"Production\\n(P)\"] + [\"Demand\\n(D)\"] + [\"Price\\n(Pr)\"]\n",
        "print(tabulate(opt_results, headers=headers, tablefmt=\"pretty\", stralign=\"right\"))\n",
        "\n",
        "print(f\"Minimum Cost : $ {model.objVal:,.2f}\")\n",
        "print(f\"Total Revenue: $ {revenue:,.2f}\")\n",
        "print(f\"Profit       : $ {(revenue-model.objVal):,.2f}\")\n",
        "\n",
        "plot_inventory()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "R_T00aUUgzML",
        "outputId": "da214817-8fc9-4a52-f42d-d96b70c584b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+-------+----------+-----------+----------+-----------+----------+-------------+------------+--------+-------+\n",
            "| Period | Hired | Laid Off | Workforce | Overtime | Inventory | Stockout | Subcontract | Production | Demand | Price |\n",
            "|    (t) |   (H) |      (L) |       (W) |      (O) |       (O) |      (S) |         (C) |        (P) |    (D) |  (Pr) |\n",
            "+--------+-------+----------+-----------+----------+-----------+----------+-------------+------------+--------+-------+\n",
            "|      0 |   0.0 |      0.0 |      80.0 |      0.0 |    1000.0 |      0.0 |         0.0 |        0.0 |    0.0 |   nan |\n",
            "|      1 |   0.0 |     11.0 |      69.0 |      0.0 |    2160.0 |      0.0 |         0.0 |     2760.0 | 1600.0 |  40.0 |\n",
            "|      2 |   0.0 |      0.0 |      69.0 |      0.0 |    1920.0 |      0.0 |         0.0 |     2760.0 | 3000.0 |  40.0 |\n",
            "|      3 |   0.0 |      0.0 |      69.0 |      0.0 |    1480.0 |      0.0 |         0.0 |     2760.0 | 3200.0 |  40.0 |\n",
            "|      4 |   0.0 |      0.0 |      69.0 |      0.0 |       0.0 |    540.0 |        20.0 |     2760.0 | 4800.0 |  40.0 |\n",
            "|      5 |   0.0 |      1.0 |      68.0 |      0.0 |       0.0 |     20.0 |         0.0 |     2720.0 | 2200.0 |  40.0 |\n",
            "|      6 |   0.0 |      0.0 |      68.0 |      0.0 |     500.0 |      0.0 |         0.0 |     2720.0 | 2200.0 |  40.0 |\n",
            "+--------+-------+----------+-----------+----------+-----------+----------+-------------+------------+--------+-------+\n",
            "Minimum Cost : $ 450,000.00\n",
            "Total Revenue: $ 680,000.00\n",
            "Profit       : $ 230,000.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO4jJjWwb6nvwnhiDGuhavO",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}