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khuyentran1401/ambulance.ipynb

Created December 27, 2019 04:19
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Ambulance
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ambulance.ipynb
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"metadata": {
"colab": {
"name": "Ambulance",
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"collapsed_sections": [],
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},
"kernelspec": {
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}
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"cells": [
{
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"metadata": {
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]
},
{
"cell_type": "code",
"metadata": {
"id": "dTOCBGL_JrIW",
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"ok": true,
"headers": [
[
"content-type",
"application/javascript"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 88
},
"outputId": "df8a2998-62f5-47b0-e23e-ad89364e8ce9"
},
"source": [
"import pandas as pd\n",
"\n",
"from google.colab import files\n",
"uploaded = files.upload()"
],
"execution_count": 251,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <input type=\"file\" id=\"files-e734c9de-f4d1-4e6d-aae6-484b44d5a36a\" name=\"files[]\" multiple disabled />\n",
" <output id=\"result-e734c9de-f4d1-4e6d-aae6-484b44d5a36a\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Saving distance.xlsx to distance (8).xlsx\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "0Ix93ptRKB4b",
"colab_type": "code",
"colab": {}
},
"source": [
"import io\n",
"df = pd.read_excel(io.BytesIO(uploaded['distance.xlsx']),header=2,index_col=0,keep_default_na=False)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "rtT0hiG2KLPp",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"outputId": "87e5dd26-b9d9-4b95-a548-68bba019bc11"
},
"source": [
"df = df[df <=2]\n",
"df"
],
"execution_count": 253,
"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",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1 2 3 4 5 6 7 8\n",
"1 0.0 NaN NaN NaN NaN NaN NaN NaN\n",
"2 NaN 0.0 NaN NaN NaN NaN NaN NaN\n",
"3 NaN NaN 0.0 2.0 2.0 NaN NaN NaN\n",
"4 NaN NaN 2.0 0.0 NaN 2.0 NaN NaN\n",
"5 NaN NaN 2.0 NaN 0.0 2.0 2.0 NaN\n",
"6 NaN NaN NaN 2.0 2.0 0.0 NaN 2.0\n",
"7 NaN NaN NaN NaN 2.0 NaN 0.0 2.0\n",
"8 NaN NaN NaN NaN NaN 2.0 2.0 0.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 253
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-y1hK0peK7aW",
"colab_type": "code",
"colab": {}
},
"source": [
"df.replace([0,2],[1,1],inplace=True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2eulhVP3K0y9",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"outputId": "e0ff8bd9-e526-4856-cc64-f03a61ddfce5"
},
"source": [
"df.fillna(0,inplace=True)\n",
"df"
],
"execution_count": 255,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
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" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <tr>\n",
" <th>7</th>\n",
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" <td>0.0</td>\n",
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"text/plain": [
" 1 2 3 4 5 6 7 8\n",
"1 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
"2 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
"3 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0\n",
"4 0.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0\n",
"5 0.0 0.0 1.0 0.0 1.0 1.0 1.0 0.0\n",
"6 0.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0\n",
"7 0.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0\n",
"8 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 255
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "OkqM2ufZMz1a",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"outputId": "28e326f7-8258-47a7-c5c9-573f4c46ff36"
},
"source": [
"a = df.as_matrix()"
],
"execution_count": 256,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "s6LoDSIBLV5A",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 88
},
"outputId": "85cbc513-88c8-4e0a-d65f-f04c1664c8f4"
},
"source": [
"pip install mip==1.4.2"
],
"execution_count": 257,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: mip==1.4.2 in /usr/local/lib/python3.6/dist-packages (1.4.2)\n",
"Requirement already satisfied: cffi in /usr/local/lib/python3.6/dist-packages (from mip==1.4.2) (1.13.2)\n",
"Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi->mip==1.4.2) (2.19)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "p99D-0d9LYAH",
"colab_type": "code",
"colab": {}
},
"source": [
"from mip.model import *\n",
"m = Model(sense=MAXIMIZE)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "GL5N4GZtL3AI",
"colab_type": "code",
"colab": {}
},
"source": [
"pop = [40, 30, 35, 20, 15, 50, 45, 60]\n",
"n = 1"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "O9XzQtLJMEyp",
"colab_type": "code",
"colab": {}
},
"source": [
"y = [m.add_var(var_type=BINARY) for i in range(len(pop))]\n",
"b = [1]*8"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zvoKuyrIOzX-",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "9aa6bfe9-f75a-4c0e-9e06-725226939287"
},
"source": [
"b"
],
"execution_count": 261,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[1, 1, 1, 1, 1, 1, 1, 1]"
]
},
"metadata": {
"tags": []
},
"execution_count": 261
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jDbaU3mFLePp",
"colab_type": "code",
"colab": {}
},
"source": [
"m.objective = maximize(xsum(pop[i] * y[i] for i in range(8)))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "k8L1BEcYMn-k",
"colab_type": "code",
"colab": {}
},
"source": [
"for i in range (8):\n",
" m += xsum(a[i][j] * y[j] for j in range(8)) >= b[i]\n",
"\n",
"m += xsum(y[i] for i in range(8)) == n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "wvZO1AFgj1kv",
"colab_type": "code",
"colab": {}
},
"source": [
"status = m.optimize()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "fsvbomDjN3gv",
"colab_type": "code",
"colab": {}
},
"source": [
"if status == OptimizationStatus.OPTIMAL:\n",
" print('optimal solution cost {} found'.format(m.objective_value))\n",
"elif status == OptimizationStatus.FEASIBLE:\n",
" print('sol.cost {} found, best possible: {}'.format(m.objective_value, m.objective_bound))\n",
"elif status == OptimizationStatus.NO_SOLUTION_FOUND:\n",
" print('no feasible solution found, lower bound is: {}'.format(m.objective_bound))\n",
"if status == OptimizationStatus.OPTIMAL or status == OptimizationStatus.FEASIBLE:\n",
" print('solution:')\n",
" for v in m.vars:\n",
" print('{} : {}'.format(v.name, v.x))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "fBt3__jeOTeL",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 320
},
"outputId": "5f37398a-2fc1-4b4e-af46-2f8f11b0542f"
},
"source": [
"for v in m.vars:\n",
" print('{} : {}'.format(v.name, v.x))"
],
"execution_count": 271,
"outputs": [
{
"output_type": "error",
"ename": "SolutionNotAvailable",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mSolutionNotAvailable\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-271-f3fe897b19c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'{} : {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\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[0;32m/usr/local/lib/python3.6/dist-packages/mip/model.py\u001b[0m in \u001b[0;36mx\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1763\u001b[0m or self.__model.status == OptimizationStatus.CUTOFF):\n\u001b[1;32m 1764\u001b[0m raise SolutionNotAvailable('Infeasible __model, \\\n\u001b[0;32m-> 1765\u001b[0;31m solution not available.')\n\u001b[0m\u001b[1;32m 1766\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mOptimizationStatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUNBOUNDED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1767\u001b[0m raise SolutionNotAvailable('Unbounded __model, solution not \\\n",
"\u001b[0;31mSolutionNotAvailable\u001b[0m: Infeasible __model, solution not available."
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "els9xsSfjwlg",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "d0f1ec13-1d21-4b04-d5d2-6e925a7a8a1e"
},
"source": [
"m."
],
"execution_count": 266,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<mip.model.Model at 0x7f23cfe81668>"
]
},
"metadata": {
"tags": []
},
"execution_count": 266
}
]
}
]
}
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