CP.ipynb

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      "name": "CP.ipynb",
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      "metadata": {
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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 $  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAegf4MTD07x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "outputId": "4502ff65-d585-4666-ee74-99ce0cdc5ec7"
      },
      "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/6c/e9/57ee68e41e02b00836dbe61a4f9679c953623168dcca3a84e2cd16a3e9b2/ortools-7.8.7959-cp36-cp36m-manylinux1_x86_64.whl (32.7MB)\n",
            "\u001b[K     |████████████████████████████████| 32.7MB 118kB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from ortools) (1.15.0)\n",
            "Requirement already satisfied: protobuf>=3.12.2 in /usr/local/lib/python3.6/dist-packages (from ortools) (3.12.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.12.2->ortools) (50.3.0)\n",
            "Installing collected packages: ortools\n",
            "Successfully installed ortools-7.8.7959\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sG_LW15bCHKH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "985e69de-a521-4bb2-83ac-a716ff53dd10"
      },
      "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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'cloned-repo' already exists and is not an empty directory.\n",
            "/content/cloned-repo\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUhA6-OCCHKV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#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",
        " \n",
        "# Formating 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",
        " \n",
        " \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",
        "#horizon = max(df[\"due\"])+900\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['Output'] = bdur['Output'].replace(0,1)\n",
        "bdur['duration'] = bdur['Qty']/bdur['Output']\n",
        "bdur['duration'] = bdur['duration'].fillna(0).astype('int')\n",
        "bdur['operation'] = 1\n",
        "bdur['Order Qty'] = bdur['Qty']\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",
        " \n",
        "#Setup\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)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kluFz3V77yw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "77fb03a9-b9de-404e-a87c-6cc27d3efddf"
      },
      "source": [
        "# Initialise model\n",
        "model = cp_model.CpModel()\n",
        "\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",
        " \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",
        "# 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",
        "# Variable to store sequence of jobs\n",
        "a={}\n",
        "for o in op:\n",
        "  for j in jobs:\n",
        "    for m in mc:\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",
        "# 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",
        "\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 j in jobs:\n",
        "    for m in mc:\n",
        "      for i in jobs:\n",
        "        if j != i:\n",
        "          #model.AddBoolOr([a[j,i,m],a[i,j,m]])\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",
        "          #model.Add(all_tasks[j, m].start >= all_tasks[i, m].end + setup.loc[(j,i)].values[0]).OnlyEnforceIf(a[,i,m].Not()).OnlyEnforceIf(b[j,m]).OnlyEnforceIf(b[i,m])\n",
        "\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+8).OnlyEnforceIf(b[j,m,2])\n",
        "gg={}\n",
        "for j in jobs:\n",
        "  aa=model.NewIntVar(0, horizon, \"endrun\"+j)\n",
        "  #aa[m]=all_tasks[j,m].end\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",
        "model.Minimize(sum([mm[j] for j in jobs]))\n",
        "solver = cp_model.CpSolver()\n",
        "solver.parameters.num_search_workers = 16\n",
        "solver.Solve(model)\n",
        "print(solver.StatusName(),solver.ObjectiveValue())"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FEASIBLE 112.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niO4NG7l8_7d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "5f689004-1372-4b30-edf2-0a6fd0873725"
      },
      "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',y='Job',height=1200,width=1500)\n",
        "fig.update_xaxes(dtick=14400000,tickformat=\"%d-%H\")\n",
        "fig.update_yaxes(autorange=\"reversed\")\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "' \\n!pip install -U plotly\\nimport plotly.express as px\\nimport datetime\\nfig=px.timeline(data_frame=ff, x_start=ff[\\'start\\'].astype(\\'datetime64[h]\\'),x_end=ff[\\'end\\'].astype(\\'datetime64[h]\\'),\\n                facet_row=\\'line\\',y=\\'Job\\',height=1200,width=1500)\\nfig.update_xaxes(dtick=14400000,tickformat=\"%d-%H\")\\nfig.update_yaxes(autorange=\"reversed\")\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uW8JljVIFVvg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "bd66efb9-2155-4b97-9407-7c84063d86e5"
      },
      "source": [
        "#!pip install -U bokeh\n",
        "from bokeh.plotting import figure, show\n",
        "from bokeh.io import output_notebook,push_notebook\n",
        "from bokeh.models import ColumnDataSource, HoverTool,MultiChoice\n",
        "from ipywidgets import interact\n",
        "from bokeh.layouts import row\n",
        "output_notebook()\n",
        "\n",
        "tooltips = [\n",
        "            ('Start','@start'),\n",
        "            ('End', '@end'),\n",
        "            ('Job', '@Job'),\n",
        "           ]\n",
        "\n",
        "p = figure(y_range=ff['Job'].unique(),x_range=(0,300),plot_width=900,tooltips=tooltips, plot_height=550, title=\"Schedule\")\n",
        "r=p.hbar(y='Job', left='start', right='end', height=0.4, source=ff)\n",
        "#p.tools(HoverTool(tooltips=tooltips))\n",
        "\n",
        "def update(attr, old, new):\n",
        "    new=text_input.value\n",
        "    ffx = ff[ff['Job'] ==new]\n",
        "    Currnew= ColumnDataSource(ffx)\n",
        "    r.data_source.data = Currnew.data\n",
        "    #push_notebook()\n",
        "text_input = MultiChoice(options=ff['Job'].unique().tolist(), title=\"Job:\")\n",
        "#text_input.on_change(\"value\",update)\n",
        "text_input.js_link('value', r.data_source, 'data.y')\n",
        "show(row(p,text_input))\n",
        "#interact(update,new=text_input.value)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-70-a303019867fc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0mtext_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMultiChoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mff\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Job'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Job:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;31m#text_input.on_change(\"value\",update)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mtext_input\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjs_link\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'value'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_source\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'data.y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtext_input\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;31m#interact(update,new=text_input.value)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bokeh/model.py\u001b[0m in \u001b[0;36mjs_link\u001b[0;34m(self, attr, other, other_attr, attr_selector)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mother_attr\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproperties\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%r is not a property of other (%r)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mother_attr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mbokeh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCustomJS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: 'data.y' is not a property of other (ColumnDataSource(id='9503', ...))"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpGEwEStZh6i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import plotly.graph_objects as go\n",
        "from ipywidgets import widgets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6homsfcc8KG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "92434f60cf3844e6988cf71db2178194",
            "b5778b1f1ede47c0bb0745f6f50dbe62",
            "2134abe811f8407e89e5da48cc67c64f"
          ]
        },
        "outputId": "abb481e4-82f3-4ea9-c9ec-e151f4835389"
      },
      "source": [
        "jo=widgets.SelectMultiple(options=ff['Job'].unique())\n",
        "display(jo)\n",
        "def gin(change):\n",
        "  kjk=ff.loc[ff['Job']==jo.value[0]]\n",
        "  #print(type(kjk))\n",
        "  fig=px.timeline(data_frame=kjk, x_start=kjk['start'].astype('datetime64[h]'),x_end=kjk['end'].astype('datetime64[h]'),facet_row='line')\n",
        "  fig.update_xaxes(dtick=14400000,tickformat=\"%d-%H\")\n",
        "  f=go.FigureWidget(data=fig)\n",
        "  f.show()\n",
        "jo.observe(gin,names='value')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "92434f60cf3844e6988cf71db2178194",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "SelectMultiple(options=('17023287gd29904300iX', '17023335gd28013000iX', '17023335gd28001700iX', '17023637gd280…"
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          },
          "metadata": {
            "tags": []
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          },
          "metadata": {
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dv-JSdMyoAKH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "8cb901b7-4958-4065-c130-eb19ec61b600"
      },
      "source": [
        "ff"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Job</th>\n",
              "      <th>delay</th>\n",
              "      <th>due</th>\n",
              "      <th>end</th>\n",
              "      <th>line</th>\n",
              "      <th>operation</th>\n",
              "      <th>run</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
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              "      <td>67.0</td>\n",
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              "    <tr>\n",
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              "<p>61 rows × 8 columns</p>\n",
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              "                       Job  delay   due     end  line  operation   run   start\n",
              "0     17023287gd29904300iX    0.0  80.0    59.0   1.0        1.0   1.0    58.0\n",
              "1     17023335gd28013000iX    0.0  87.0    86.0   1.0        1.0   1.0    85.0\n",
              "2     17023335gd28001700iX    0.0  87.0   135.0   1.0        1.0   1.0   134.0\n",
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              "..                     ...    ...   ...     ...   ...        ...   ...     ...\n",
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              "59    17023439gd28311300iX    0.0  88.0    79.0   6.0        2.0   8.0    71.0\n",
              "60  4501763335gC25020000UC    0.0  93.0    90.0   6.0        2.0   7.0    83.0\n",
              "\n",
              "[61 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTrYTZe2u6FW",
        "colab_type": "code",
        "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_type": "code",
        "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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v8Aqv5gzdQyC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import plotly.io as pio\n",
        "pio.renderers.default = \"colab\""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}