CP1.1finalreport.ipynb

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    "colab": {
      "name": "CP.ipynb",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mswastik/optimization/blob/master/CP1.1finalreport.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcrQOnfJgYxd"
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      "source": [
        "**Indices**  \n",
        "j - job  \n",
        "m - machine \n",
        "\n",
        "**Decision Variable**  \n",
        "$s_{j,m}$ - start time of job j at machine m  \n",
        "$e_{j,m}$ - end time of job j at machine m  \n",
        "$d_{j,m}$ - duration of job j at machine m  \n",
        "$b_{j,m}$ - machine m is selected for job j     \n",
        "$a_{j,i,m}$ - job i precedes job j at machine m \n",
        "\n",
        "**Parameters**  \n",
        "H - Horizon  \n",
        "$d_j$ - delivery date of job j  \n",
        "$q_j$ - order qty of job j  \n",
        "$o_{j,m}$ - output per hour of job j at machine m  \n",
        "$set_{i,j}$ - setup time between job i & j   \n",
        "\n",
        "\n",
        "**Objective** (Not implemented right now)  \n",
        "$ \\sum_{j} d_j - max(e_{j,m})  \\qquad \\forall m \\in mc$    \n",
        "\n",
        "\n",
        "**Constraints**  \n",
        "$ \\sum_m e_{j,m} < s_{i,m} or s_{j,m} > e_{i,m} \\qquad \\forall j,i \\in jobs$  \n",
        "$ \\sum_j o_{j,m} \\times d_{j,m} >= q_j \\qquad \\forall m \\in ma $    \n",
        "$ \\sum_m s_{i,m} >= e_{j,m} + set_{i,j} \\qquad if \\quad b_{j,m}=1, b_{i,m}=1, a_{j,i,m}=1 $  "
      ]
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "d6057f14-bd97-4cdf-98fa-c0137c60ee28"
      },
      "source": [
        "!pip install ortools\n",
        "#!pip install dtale"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting ortools\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/63/94/2832edee6f4fb4e77e8585b6034f9506be24361fe6ead4e76de38ab0a666/ortools-8.1.8487-cp36-cp36m-manylinux1_x86_64.whl (14.0MB)\n",
            "\u001b[K     |████████████████████████████████| 14.0MB 312kB/s \n",
            "\u001b[?25hCollecting absl-py>=0.11\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bc/58/0aa6fb779dc69cfc811df3398fcbeaeefbf18561b6e36b185df0782781cc/absl_py-0.11.0-py3-none-any.whl (127kB)\n",
            "\u001b[K     |████████████████████████████████| 133kB 65.6MB/s \n",
            "\u001b[?25hCollecting protobuf>=3.14.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fe/fd/247ef25f5ec5f9acecfbc98ca3c6aaf66716cf52509aca9a93583d410493/protobuf-3.14.0-cp36-cp36m-manylinux1_x86_64.whl (1.0MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0MB 51.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from absl-py>=0.11->ortools) (1.15.0)\n",
            "\u001b[31mERROR: tensorflow-metadata 0.26.0 has requirement absl-py<0.11,>=0.9, but you'll have absl-py 0.11.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: absl-py, protobuf, ortools\n",
            "  Found existing installation: absl-py 0.10.0\n",
            "    Uninstalling absl-py-0.10.0:\n",
            "      Successfully uninstalled absl-py-0.10.0\n",
            "  Found existing installation: protobuf 3.12.4\n",
            "    Uninstalling protobuf-3.12.4:\n",
            "      Successfully uninstalled protobuf-3.12.4\n",
            "Successfully installed absl-py-0.11.0 ortools-8.1.8487 protobuf-3.14.0\n"
          ],
          "name": "stdout"
        },
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              "pip_warning": {
                "packages": [
                  "google"
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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sG_LW15bCHKH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b35a4ca4-801a-4239-f1f7-3f92e39921a9"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import collections\n",
        "from ortools.sat.python import cp_model\n",
        "# Clone the entire repo.\n",
        "!git clone -l -s https://github.com/mswastik/optimization.git cloned-repo\n",
        "%cd cloned-repo"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'cloned-repo'...\n",
            "warning: --local is ignored\n",
            "remote: Enumerating objects: 21, done.\u001b[K\n",
            "remote: Counting objects: 100% (21/21), done.\u001b[K\n",
            "remote: Compressing objects: 100% (21/21), done.\u001b[K\n",
            "remote: Total 391 (delta 12), reused 0 (delta 0), pack-reused 370\u001b[K\n",
            "Receiving objects: 100% (391/391), 2.53 MiB | 6.73 MiB/s, done.\n",
            "Resolving deltas: 100% (218/218), done.\n",
            "/content/cloned-repo\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUhA6-OCCHKV"
      },
      "source": [
        "#Importing Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import collections\n",
        "from ortools.sat.python import cp_model\n",
        "#Importing Data\n",
        "df=pd.read_excel('orders1.xlsx')\n",
        "ma=pd.read_excel('item master.xlsx')\n",
        "bu=pd.read_excel('item master.xlsx',sheet_name='Sheet2')\n",
        "pa=pd.read_excel('item master.xlsx',sheet_name='packing')\n",
        "# Formatting Data\n",
        "df = df[df['Line']=='Tmmthpkhti Limi']\n",
        "df = df[df['Plant']==1024]\n",
        "del df['Line']\n",
        "df['due'] = df['Dispatch Date'] - df['SO Date'].min()\n",
        "df['due'] = df['due'].dt.days\n",
        "df['sodate'] = df['SO Date'] - df['SO Date'].min()\n",
        "df['sodate'] = df['sodate'].dt.days\n",
        "df.reset_index(inplace=True,drop=True)\n",
        "df['Key']=df['SO'].astype('str')+df['FG Code']\n",
        "df.drop(['Dispatch Date','SO Date','Description','Customer'],axis=1,inplace=True)\n",
        "df['Order Qty'] = df[df['Order Qty']!=0]['Order Qty']\n",
        "df['Order Qty'] = df['Order Qty']*1000\n",
        "df=df[df['Key']!='17023287gd39000800iX']\n",
        "# Creating Parameter Data\n",
        "jobs =df['Key']\n",
        "op =[1,2] # Ignore as of now\n",
        "mc = [1,2,3,4,5,6]\n",
        "due = df.set_index('Key')\n",
        "dur=df[['Key','FG Code','Order Qty']]\n",
        "dur=dur.merge(pa[[\"FG Code\",\"Line\",\"Output\"]], on=\"FG Code\")\n",
        "dur['Output']=dur['Output']*100\n",
        "dur['duration']=dur['Order Qty']/dur['Output']\n",
        "dur['duration'] = dur['duration'].astype('float64').round(decimals = 2)\n",
        "dur['operation'] = 2\n",
        "budf=df.merge(ma[[\"FG Code\",\"Bulk Code\",\"Case Size\",\"Pack Wt(g)\"]],on='FG Code',how='left')\n",
        "budf['Qty']=budf['Order Qty']*budf['Case Size']*budf['Pack Wt(g)']/1000000\n",
        "bdur=budf.merge(bu[[\"Bulk Code\", \"Line\",\"Output\"]],on=\"Bulk Code\",how=\"left\")\n",
        "bdur['Order Qty'] = bdur['Qty']*1000\n",
        "bdur['Output'] = bdur['Output']*100\n",
        "bdur['Output'] = bdur['Output'].replace(0,1)\n",
        "bdur['duration'] = bdur['Order Qty']/bdur['Output']\n",
        "bdur['duration'] = bdur['duration'].fillna(0).astype('int')\n",
        "bdur['operation'] = 1\n",
        "bdur.drop(['Plant','due','sodate','Case Size','Pack Wt(g)','SO','Qty','Bulk Code'],axis=1,inplace=True)\n",
        "dur=dur.append(bdur)\n",
        "dur.set_index(['Key','Line','operation'],inplace=True)\n",
        "del dur['FG Code']\n",
        "dur['Output'].replace(0,1,inplace=True)\n",
        "dur['Order Qty']=dur['Order Qty'].fillna(0).astype('int')\n",
        "dur['duration']=dur['duration'].fillna(0).astype('int')\n",
        "dur['Output']=dur['Output'].fillna(0).astype('int')\n",
        "#horizon = sum(dur['duration'])\n",
        "# Preparing Machine Setup Data\n",
        "setup = pd.DataFrame(columns=('Key1','Key2','setup'))\n",
        "for i in df['Key']:\n",
        "  for k in df['Key']:\n",
        "    if df[df['Key']==i][\"FG Code\"].values[0]==df[df['Key']==k][\"FG Code\"].values[0]:\n",
        "      f = {'Key1':[i],\"Key2\":[k],\"setup\":[0]}\n",
        "    else:\n",
        "      f = {'Key1':[i],\"Key2\":[k],\"setup\":[4]}\n",
        "    f = pd.DataFrame(data=f)\n",
        "    setup = pd.concat([setup,f])\n",
        "setup.set_index(['Key1','Key2'],inplace=True)\n",
        "\n",
        "horizon = max(df[\"due\"])+50"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kluFz3V77yw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e57065d0-7b81-48d8-f505-e07e610d96df"
      },
      "source": [
        "# Initialize model\n",
        "model = cp_model.CpModel()\n",
        "# Creating variables to store data\n",
        "task_type = collections.namedtuple('task_type', 'start end dur run1')\n",
        "mtj = collections.defaultdict(list)\n",
        "all_tasks = {}\n",
        "for j in jobs:\n",
        "  for o in op:\n",
        "    for m in mc:\n",
        "      suffix = '_%s_%i_%i' % (j,m,o)\n",
        "      start = model.NewIntVar(0, horizon, \"start\"+suffix)\n",
        "      end = model.NewIntVar(0, horizon, \"end\"+suffix)\n",
        "      run1 = model.NewIntVar(0, dur['duration'].loc[(j,m,o)].item(),\"run\"+suffix)\n",
        "      duration = model.NewIntervalVar(start, run1, end, \"duration\"+suffix)\n",
        "      all_tasks[j, m,o] = task_type(start=start, end=end, dur= duration,run1=run1)\n",
        "      mtj[o,m].append(duration)\n",
        "\n",
        "\n",
        "# (New) No mixer 5,6\n",
        "for j in jobs:\n",
        "  model.Add(all_tasks[j, 5,1].run1==0)\n",
        "  model.Add(all_tasks[j, 6,1].run1==0)\n",
        "\n",
        "# Selection variable to store whether machine is selected to process job\n",
        "b={}\n",
        "for o in op:\n",
        "  for j in jobs:\n",
        "    for m in mc:\n",
        "      b[j,m,o] = model.NewBoolVar('selection')\n",
        "      model.Add(all_tasks[j, m,o].run1>0).OnlyEnforceIf(b[j,m,o])\n",
        "      model.Add(all_tasks[j, m,o].run1==0).OnlyEnforceIf(b[j,m,o].Not())\n",
        "\n",
        "# (New) Small run\n",
        "for o in op:\n",
        "  for m in mc:\n",
        "    for j in jobs:\n",
        "      if dur['duration'].loc[(j,m,o)].item()<12:\n",
        "        model.Add(all_tasks[j, m,o].run1==dur['duration'].loc[(j,m,o)].item()).OnlyEnforceIf(b[j,m,o])\n",
        "        model.Add(all_tasks[j, m,o].run1==0).OnlyEnforceIf(b[j,m,o].Not())\n",
        "\n",
        "# Variable to store sequence of jobs\n",
        "a={}\n",
        "for o in op:\n",
        "  for m in mc:\n",
        "    for j in jobs:\n",
        "      for i in jobs:\n",
        "        if j != i:\n",
        "          a[j,i,m,o] = model.NewBoolVar('sequence')\n",
        "          model.Add(all_tasks[i, m,o].start>all_tasks[j,m,o].start).OnlyEnforceIf(a[j,i,m,o])\n",
        "          model.Add(all_tasks[i, m,o].start<all_tasks[j,m,o].start).OnlyEnforceIf(a[j,i,m,o].Not())\n",
        "\n",
        "\n",
        "\n",
        "# Constraint: A machine can process only 1 job at a time\n",
        "for o in op:\n",
        "  for m in mc:\n",
        "    model.AddNoOverlap(mtj[o,m])\n",
        "\n",
        "# Constraint: Complete production of full order quantity\n",
        "com = {}\n",
        "for o in op:\n",
        "  for j in jobs:\n",
        "    tt= 0\n",
        "    for m in mc:\n",
        "      comp = all_tasks[j,m,o].run1*dur[\"Output\"].loc[(j,m,o)]\n",
        "      tt = tt + comp\n",
        "    com[j] = tt\n",
        "    #Change ORDER QTY FOR BULK\n",
        "    model.Add(com[j]*10 >= dur.loc[(j,slice(None),o)]['Order Qty'].values[0])\n",
        "\n",
        "# Constraint of Setup Time\n",
        "for o in op:\n",
        "  for m in mc:\n",
        "    for j in jobs:\n",
        "      for i in jobs:\n",
        "        if j != i:\n",
        "          model.Add(all_tasks[i, m,o].start >= all_tasks[j, m,o].end + setup.loc[(j,i)].values[0]).OnlyEnforceIf(a[j,i,m,o]).OnlyEnforceIf(b[j,m,o]).OnlyEnforceIf(b[i,m,o])\n",
        "# Gap between two operations\n",
        "for j in jobs:\n",
        "  for m in mc:\n",
        "    minst=model.NewIntVar(0, horizon, \"minst\"+j)\n",
        "    model.AddMinEquality(minst,[all_tasks[j,k,1].start for k in mc])\n",
        "    model.Add(all_tasks[j, m,2].start >= minst+4).OnlyEnforceIf(b[j,m,2])\n",
        "    model.Add(all_tasks[j, m,2].start <= minst+24).OnlyEnforceIf(b[j,m,2])\n",
        "\n",
        "gg={}\n",
        "for j in jobs:\n",
        "  aa=model.NewIntVar(0, horizon, \"endrun\"+j)\n",
        "  model.AddMaxEquality(aa,[all_tasks[j,m,2].end for m in mc])\n",
        "  gg[j] = due['due'].loc[j]- aa\n",
        "\n",
        "#Dummy variable for minimizing absolute value in objective function\n",
        "mm={}\n",
        "for j in jobs:\n",
        "  kk = model.NewIntVar(0, horizon, 'dummy'+j)\n",
        "  mm[j] = kk\n",
        "  model.Add(gg[j]<=mm[j])\n",
        "  model.Add(-gg[j]<=mm[j])\n",
        "\n",
        "# Define Objective Function\n",
        "model.Minimize(sum([mm[j] for j in jobs]))\n",
        "# Calling Solver\n",
        "solver = cp_model.CpSolver()\n",
        "solver.parameters.num_search_workers = 16\n",
        "solver.Solve(model)\n",
        "print(solver.StatusName(),solver.ObjectiveValue())\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FEASIBLE 75.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niO4NG7l8_7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "outputId": "07cfc852-cde9-4947-dbb0-89b1bd21e3ad"
      },
      "source": [
        "ff=pd.DataFrame()\n",
        "for o in op:\n",
        "  for m in mc:\n",
        "    for j in jobs:\n",
        "      if solver.Value(all_tasks[j,m,o].run1)>0:\n",
        "        kk={'Job':j,'line':m,'operation':o,'start':solver.Value(all_tasks[j,m,o].start),\n",
        "            'run':solver.Value(all_tasks[j,m,o].run1),'end':solver.Value(all_tasks[j,m,o].end),'due':due['due'].loc[j],'delay':solver.Value(gg[j])}\n",
        "        ff=ff.append(kk,ignore_index=True)\n",
        "\n",
        "!pip install -U plotly\n",
        "import plotly.express as px\n",
        "import datetime\n",
        "#fig=px.timeline(data_frame=ff, x_start=ff['start'].astype('datetime64[h]'),x_end=ff['end'].astype('datetime64[h]'),\n",
        "#                facet_row='line',facet_col='operation',y='Job',height=1200,width=1500,color='Job')\n",
        "#Replace with above code for final report\n",
        "fig=px.timeline(data_frame=ff, x_start=ff['start'].astype('datetime64[h]'),x_end=ff['end'].astype('datetime64[h]'),y=(ff['line']).astype('str'),facet_col='operation',color='Job',range_y=(1,6))\n",
        "fig.update_xaxes(dtick=14400000,tickformat=\"%d-%H\")\n",
        "fig.update_yaxes(autorange=\"reversed\")\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: plotly in /usr/local/lib/python3.6/dist-packages (4.14.1)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from plotly) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly) (1.3.3)\n"
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          "name": "stdout"
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\"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, 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\"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.49], \"dtick\": 14400000, \"tickformat\": \"%d-%H\", \"type\": \"date\"}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.51, 1.0], \"dtick\": 14400000, \"matches\": \"x\", \"tickformat\": \"%d-%H\", \"type\": \"date\"}, \"yaxis\": {\"anchor\": \"x\", \"autorange\": \"reversed\", \"domain\": [0.0, 1.0], \"range\": [1, 6], \"title\": {\"text\": \"y\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"autorange\": \"reversed\", \"domain\": [0.0, 1.0], \"matches\": \"y\", \"showticklabels\": false}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('fb8903d0-1cff-4104-939e-de7e62a69f1a');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpGEwEStZh6i"
      },
      "source": [
        "px.timeline?"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTrYTZe2u6FW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "523e20e4-0ce6-4d17-9ce3-2ab07998ab7b"
      },
      "source": [
        "#Exploring Result (click on last link to view result)\n",
        "!pip install dtale\n",
        "from dtale import show\n",
        "import dtale.app as dtale_app\n",
        "dtale_app.USE_COLAB = True\n",
        "show(ff,ignore_duplicate=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting dtale\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/26/93/6ddef64868f4ef65683ce1997ff8e00283836b8dda41848deaf36c203b4f/dtale-1.15.2-py2.py3-none-any.whl (7.6MB)\n",
            "\u001b[K     |████████████████████████████████| 7.6MB 2.8MB/s \n",
            "\u001b[?25hCollecting squarify\n",
            "  Downloading https://files.pythonhosted.org/packages/0b/2b/2e77c35326efec19819cd1d729540d4d235e6c2a3f37658288a363a67da5/squarify-0.4.3-py3-none-any.whl\n",
            "Requirement already satisfied: itsdangerous in /usr/local/lib/python3.6/dist-packages (from dtale) (1.1.0)\n",
            "Collecting dash>=1.5.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d1/c2/0eb000c0a2f9e594c7f94c3234838e8f840b209dc8c5b2bc1ed0a9bc0f3c/dash-1.16.1.tar.gz (72kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 8.4MB/s \n",
            "\u001b[?25hCollecting dash-bootstrap-components; python_version > \"3.0\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/61/01/5da09fab6018f065d70237ffb429afe2a4c10ef6986662c62eedcc76d6e6/dash_bootstrap_components-0.10.6-py2.py3-none-any.whl (185kB)\n",
            "\u001b[K     |████████████████████████████████| 194kB 48.0MB/s \n",
            "\u001b[?25hCollecting flask-ngrok; python_version > \"3.0\"\n",
            "  Downloading https://files.pythonhosted.org/packages/af/6c/f54cb686ad1129e27d125d182f90f52b32f284e6c8df58c1bae54fa1adbc/flask_ngrok-0.0.25-py3-none-any.whl\n",
            "Collecting dash-daq\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/16/c1/9c6adfc3646990965a5b2ca073f579cc9c09d15553fcfcd83aefd1255494/dash_daq-0.5.0.tar.gz (642kB)\n",
            "\u001b[K     |████████████████████████████████| 645kB 50.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: xarray in /usr/local/lib/python3.6/dist-packages (from dtale) (0.15.1)\n",
            "Collecting dash-colorscales\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d5/24/de9318acd52989d77135a8497c8be536442b725473684ed4e1ab999b2051/dash_colorscales-0.0.4.tar.gz (62kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 8.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from dtale) (2.23.0)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.6/dist-packages (from dtale) (0.10.2)\n",
            "Requirement already satisfied: future>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from dtale) (0.16.0)\n",
            "Requirement already satisfied: scikit-learn>='0.21.0 in /usr/local/lib/python3.6/dist-packages (from dtale) (0.22.2.post1)\n",
            "Collecting Flask-Compress\n",
            "  Downloading https://files.pythonhosted.org/packages/a0/96/cd684c1ffe97b513303b5bfd4bbfb4114c5f4a5ea8a737af6fd813273df8/Flask-Compress-1.5.0.tar.gz\n",
            "Requirement already satisfied: Flask>=1.0 in /usr/local/lib/python3.6/dist-packages (from dtale) (1.1.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from dtale) (1.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from dtale) (1.15.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from dtale) (1.0.5)\n",
            "Collecting lz4; python_version > \"3.0\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/15/6a/ea95dd9a9957143636cfad5037637abec91016b9bde519d3edf4708e3d83/lz4-3.1.0-cp36-cp36m-manylinux2010_x86_64.whl (1.8MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8MB 42.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from dash>=1.5.0->dtale) (4.10.0)\n",
            "Collecting dash_renderer==1.8.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/12/22/82e15ff57fd91bedd52583ee7e5968380a09b5a3003dd63361bfb67fdb54/dash_renderer-1.8.1.tar.gz (951kB)\n",
            "\u001b[K     |████████████████████████████████| 952kB 53.5MB/s \n",
            "\u001b[?25hCollecting dash-core-components==1.12.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bb/9e/979263a03963d7ac70229ae9a9a118640dd3ef9b57c5d0c89d3d18f073e7/dash_core_components-1.12.1.tar.gz (3.4MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4MB 29.5MB/s \n",
            "\u001b[?25hCollecting dash-html-components==1.1.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/02/ba/bb9427c62feb25bfbaf243894eeeb4e7c67a92b426ed0575a167100e436e/dash_html_components-1.1.1.tar.gz (188kB)\n",
            "\u001b[K     |████████████████████████████████| 194kB 48.1MB/s \n",
            "\u001b[?25hCollecting dash-table==4.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a9/85/2408091d71af9af8c5ce6855730ff2e3e443ba8a667864e22f0881f4d785/dash_table-4.10.1.tar.gz (1.8MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8MB 48.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from xarray->dtale) (1.18.5)\n",
            "Requirement already satisfied: setuptools>=41.2 in /usr/local/lib/python3.6/dist-packages (from xarray->dtale) (50.3.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->dtale) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->dtale) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->dtale) (2020.6.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->dtale) (2.10)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from statsmodels->dtale) (0.5.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>='0.21.0->dtale) (0.16.0)\n",
            "Collecting brotli\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b4/d3/7c98f05b7b9103e2f3a112ba42f269c798155b3e5404fb80bb8f823aaebe/Brotli-1.0.9-cp36-cp36m-manylinux1_x86_64.whl (357kB)\n",
            "\u001b[K     |████████████████████████████████| 358kB 41.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: Werkzeug>=0.15 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0->dtale) (1.0.1)\n",
            "Requirement already satisfied: Jinja2>=2.10.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0->dtale) (2.11.2)\n",
            "Requirement already satisfied: click>=5.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0->dtale) (7.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->dtale) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->dtale) (2018.9)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->dash>=1.5.0->dtale) (1.3.3)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.10.1->Flask>=1.0->dtale) (1.1.1)\n",
            "Building wheels for collected packages: dash, dash-daq, dash-colorscales, Flask-Compress, dash-renderer, dash-core-components, dash-html-components, dash-table\n",
            "  Building wheel for dash (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash: filename=dash-1.16.1-cp36-none-any.whl size=82320 sha256=bed2870b52b9843405193172647c98a763406af898a4f6be83895c3d3f6dc719\n",
            "  Stored in directory: /root/.cache/pip/wheels/52/0c/98/a3842c450867b2aec8577ee1051bb94ad0f8fe62ed3f80c175\n",
            "  Building wheel for dash-daq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-daq: filename=dash_daq-0.5.0-cp36-none-any.whl size=669704 sha256=3274070d497271b84f001102b45a2c17e19ecb21401fb7dfbc60aab1e804dfce\n",
            "  Stored in directory: /root/.cache/pip/wheels/1c/9c/f7/84731716eaa9fc952cdbdd1f87df3ca9b8805c317c609efcd6\n",
            "  Building wheel for dash-colorscales (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-colorscales: filename=dash_colorscales-0.0.4-cp36-none-any.whl size=62599 sha256=0b50a1a657797a2a92904cbc62c2493021df1257c2606ec3c1e5ac0b3493f33d\n",
            "  Stored in directory: /root/.cache/pip/wheels/0e/4a/1c/3490cfe6a59299b06917af6626a969c8fb7caead39b2d74cf2\n",
            "  Building wheel for Flask-Compress (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for Flask-Compress: filename=Flask_Compress-1.5.0-cp36-none-any.whl size=5272 sha256=2aa981fdb58f4e6ed208884f52c9992668a76265f4759c8d3b67b58cec5a2111\n",
            "  Stored in directory: /root/.cache/pip/wheels/f7/e9/e4/5afc286be7c87461375e33152558415dfeb0c8f5af3b50e742\n",
            "  Building wheel for dash-renderer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-renderer: filename=dash_renderer-1.8.1-cp36-none-any.whl size=960253 sha256=582a0f90ea191c53dbf6177c09db5338af9d8ea7ece19cf5abdb43647bfc070c\n",
            "  Stored in directory: /root/.cache/pip/wheels/5a/e6/52/22190c9e35ff7c8fa8ce90bd76ea22a94903ca84b95b1d6db2\n",
            "  Building wheel for dash-core-components (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-core-components: filename=dash_core_components-1.12.1-cp36-none-any.whl size=3475396 sha256=89e79cb3dba528b062487c32b6bfde90fee12f8865a53491eb85d195d27453b4\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/11/a0/0cf8183b6f7df4716bec25b0e089043cd161bb58accc6633a6\n",
            "  Building wheel for dash-html-components (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-html-components: filename=dash_html_components-1.1.1-cp36-none-any.whl size=427894 sha256=6a534f8cf84b90a540e16f6c489f044fcbbc9aab0dc51a068192e84284bb215b\n",
            "  Stored in directory: /root/.cache/pip/wheels/8d/01/46/78e4de185a8a4a2da8ba31da16c52170f036d4cebeeb6e07a2\n",
            "  Building wheel for dash-table (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-table: filename=dash_table-4.10.1-cp36-none-any.whl size=1824995 sha256=0f81f886b0512c5ec44a02325e4ccdf69a1bcc919632b7f027389aeb92771fe7\n",
            "  Stored in directory: /root/.cache/pip/wheels/3d/c2/fd/50a7ea0b9aef63cf3bf3c5fb22799b8ba9a97ea8f9b14b7124\n",
            "Successfully built dash dash-daq dash-colorscales Flask-Compress dash-renderer dash-core-components dash-html-components dash-table\n",
            "Installing collected packages: squarify, brotli, Flask-Compress, dash-renderer, dash-core-components, dash-html-components, dash-table, dash, dash-bootstrap-components, flask-ngrok, dash-daq, dash-colorscales, lz4, dtale\n",
            "Successfully installed Flask-Compress-1.5.0 brotli-1.0.9 dash-1.16.1 dash-bootstrap-components-0.10.6 dash-colorscales-0.0.4 dash-core-components-1.12.1 dash-daq-0.5.0 dash-html-components-1.1.1 dash-renderer-1.8.1 dash-table-4.10.1 dtale-1.15.2 flask-ngrok-0.0.25 lz4-3.1.0 squarify-0.4.3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "https://g94vhw19i5q-496ff2e9c6d22116-40000-colab.googleusercontent.com/dtale/main/1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2dwvTF7GebQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "7d1bfcaf-de05-4de6-dc98-31ae3c306e7b"
      },
      "source": [
        "print(model.ModelStats())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimization model '':\n",
            "#Variables: 2040 (17 in objective)\n",
            " - 1734 in [0,1]\n",
            " - 5 in [0,15]\n",
            " - 5 in [0,36]\n",
            " - 1 in [0,66]\n",
            " - 5 in [0,75]\n",
            " - 5 in [0,93]\n",
            " - 5 in [0,108]\n",
            " - 5 in [0,133]\n",
            " - 1 in [0,162]\n",
            " - 2 in [0,168]\n",
            " - 3 in [0,189]\n",
            " - 1 in [0,213]\n",
            " - 2 in [0,216]\n",
            " - 5 in [0,233]\n",
            " - 3 in [0,243]\n",
            " - 1 in [0,300]\n",
            " - 2 in [0,320]\n",
            " - 2 in [0,400]\n",
            " - 8 in [0,450]\n",
            " - 3 in [0,451]\n",
            " - 1 in [0,462]\n",
            " - 1 in [0,497]\n",
            " - 2 in [0,693]\n",
            " - 2 in [0,746]\n",
            " - 1 in [0,940]\n",
            " - 3 in [0,978]\n",
            " - 1 in [0,995]\n",
            " - 3 in [0,1054]\n",
            " - 6 in [0,1066]\n",
            " - 2 in [0,1493]\n",
            " - 5 in [0,2000]\n",
            " - 3 in [0,2108]\n",
            " - 1 in [0,2160]\n",
            " - 1 in [0,4500]\n",
            " - 1 in [0,5600]\n",
            " - 1 in [0,8000]\n",
            " - 1 in [0,8640]\n",
            " - 1 in [0,14000]\n",
            " - 1 in [0,15120]\n",
            " - 1 in [0,36000]\n",
            " - 204 in [0,1442570]\n",
            "#kInterval: 102\n",
            "#kLinear1: 204 (#enforced: 204)\n",
            "#kLinear2: 4896 (#enforced: 4896)\n",
            "#kLinearN: 17\n",
            "#kNoOverlap: 6\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}