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The Transportation Problem (simple example)
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Transportation Problem \n",
"\n",
"Data Table Representation \n",
"\n",
"| | Warehouse 1 | Warehouse 2 | Warehouse 3 | Supply |\n",
"| -------- | ----------: : ----------: | ----------: | -----: |\n",
"|Factory 1 | 30 | 20 | 10 | 100 |\n",
"|Factory 2 | 25 | 15 | 5 | 200 |\n",
"| Demand | 100 | 120 | 80 | |\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2×3 Array{Int64,2}:\n",
" 30 20 10\n",
" 25 15 5"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cost = [ 30 20 10 ; \n",
" 25 15 5 ]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2-element Array{Int64,1}:\n",
" 100\n",
" 200"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"supply = [100, 200]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3-element Array{Int64,1}:\n",
" 100\n",
" 120\n",
" 80"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demand = [100, 120, 80]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Min 30 x[1,1] + 20 x[1,2] + 10 x[1,3] + 25 x[2,1] + 15 x[2,2] + 5 x[2,3]\n",
"Subject to\n",
" factory[1] : x[1,1] + x[1,2] + x[1,3] = 100.0\n",
" factory[2] : x[2,1] + x[2,2] + x[2,3] = 200.0\n",
" warehouse[1] : x[1,1] + x[2,1] = 100.0\n",
" warehouse[2] : x[1,2] + x[2,2] = 120.0\n",
" warehouse[3] : x[1,3] + x[2,3] = 80.0\n",
" x[1,1] ≥ 0.0\n",
" x[2,1] ≥ 0.0\n",
" x[1,2] ≥ 0.0\n",
" x[2,2] ≥ 0.0\n",
" x[1,3] ≥ 0.0\n",
" x[2,3] ≥ 0.0\n"
]
}
],
"source": [
"m = 2 # number of factories (suppliers)\n",
"n = 3 # number of warehouses (demanders)\n",
"\n",
"using JuMP, GLPK\n",
"tp = Model(GLPK.Optimizer)\n",
"@variable(tp, x[1:m, 1:n] >= 0)\n",
"@objective(tp, Min, sum(cost[i,j] * x[i,j] for i in 1:m, j in 1:n))\n",
"@constraint(tp, factory[i in 1:2], sum(x[i,j] for j in 1:n) == supply[i])\n",
"@constraint(tp, warehouse[j in 1:3], sum(x[i,j] for i in 1:m) == demand[j])\n",
"print(tp)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"raw_status(tp) = \"Solution is optimal\"\n",
"objective_value(tp) = 5200.0\n",
"value.(x) = [100.0 0.0 0.0; 0.0 120.0 80.0]\n"
]
},
{
"data": {
"text/plain": [
"2×3 Array{Float64,2}:\n",
" 100.0 0.0 0.0\n",
" 0.0 120.0 80.0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimize!(tp)\n",
"@show raw_status(tp)\n",
"@show objective_value(tp)\n",
"@show value.(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.5.2",
"language": "julia",
"name": "julia-1.5"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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