Vehicle Routing Problem.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Install libraries\n",
    "#import Pkg; Pkg.add(\"JuMP\")\n",
    "#import Pkg; Pkg.add(\"Cbc\")\n",
    "#import Pkg; Pkg.add(\"Distances\")\n",
    "#import Pkg; Pkg.add(\"CairoMakie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "using JuMP, Cbc, Distances, Distributions, CairoMakie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- JuMP: domain-specific modeling language for mathematical optimization embedded in Julia.\n",
    "\n",
    "- Distances: a Julia package for evaluating distances(metrics) between vectors.\n",
    "\n",
    "- Distributions: package provides a large collection of probabilistic distributions and related functions.\n",
    "\n",
    "- CairoMakie: data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_fac = 3\n",
    "n_clients_1 = 4 #mercados\n",
    "n_clients_2 = 4 #encuestados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "coordinates = [\n",
    "    -77.0125057 -12.1082707\n",
    "    -77.0009741 -12.1135569\n",
    "    -77.0235178 -12.1075984\n",
    "    \n",
    "    -77.025807 -12.118365\n",
    "    -77.024108 -12.110307\n",
    "    -77.010427 -12.108666\n",
    "    -77.016581 -12.11665\n",
    "    \n",
    "    -77.0214718 -12.1187303\n",
    "    -77.0176426 -12.1161587\n",
    "    -77.0118733 -12.1126066\n",
    "    -77.0101258 -12.1086169\n",
    "   \n",
    "];\n",
    "#facilities, 1t, 2t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pairwise(Euclidean(), coordinates, dims=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "#no se genera la diagonal\n",
    "for i in 1:size(d)[1]\n",
    "    d[i,i] = 9999999\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "#demanda (distribucion normal)\n",
    "q = rand(Normal(30,5), size(d)[1]);\n",
    "q[1:n_fac] .= 0;\n",
    "#[0 0 1 1 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = [500, 500, 500];\n",
    "#capacidades de vehiculos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K = 1\n",
    "#numero de vehiculos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LRP (generic function with 1 method)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function LRP(d, q, C)\n",
    "    \n",
    "    m = Model(Cbc.Optimizer)\n",
    "    #si se activa el m\n",
    "    @variable(m, y[i in 1:n_fac], Bin)\n",
    "    @variable(m, x[i in 1:n_fac+n_clients_1, j in 1:n_fac+n_clients_1, k in 1:K], Bin)\n",
    "    @variable(m, w[i in 1:n_fac+n_clients_2, j in 1:n_fac+n_clients_2, k in 1:K], Bin)\n",
    "    \n",
    "    @objective(m, Min, \n",
    "        sum(y) + \n",
    "        sum(d[i,j] * sum(x[i,j,k] for k in 1:K) for i in 1:n_fac+n_clients_1, j in 1:n_fac+n_clients_1)+\n",
    "        sum(d[i,j] * sum(w[i,j,k] for k in 1:K) for i in 1:n_fac+n_clients_2, j in 1:n_fac+n_clients_2)\n",
    "    )\n",
    "    \n",
    "    \n",
    "    #Level 1 constraints\n",
    "    #[] for\n",
    "    @constraint(m, [i in 1:n_fac+n_clients_1, k in 1:K], sum(x[i,j,k] for j in 1:n_fac+n_clients_1) == 1)\n",
    "    @constraint(m, [i in 1:n_fac, j in 1:n_fac, k in 1:K], x[i,j,k] == 0)\n",
    "    @constraint(m, [i in 1:n_fac+n_clients_1, k in 1:K], \n",
    "        sum(x[i,j,k] for j in 1:n_fac+n_clients_1) - sum(x[j,i,k] for j in 1:n_fac+n_clients_1) == 0)\n",
    "    @constraint(m, [i in n_fac+1:n_clients_1, k in 1:K], q[i] * sum(x[i,j,k] for j in 1:n_fac+n_clients_1) <= C[k])\n",
    "    \n",
    "    @constraint(m, [i in 1:n_fac, k in 1:K], \n",
    "        sum(x[i,1:n_fac+n_clients_1,k]) + sum(x[1:n_fac+n_clients_1,i,k]) - y[i] <= 1)\n",
    "    \n",
    "    #Level 2 constraints\n",
    "    @constraint(m, [i in 1:n_fac+n_clients_2, k in 1:K], sum(w[i,j,k] for j in 1:n_fac+n_clients_2) == 1)\n",
    "    @constraint(m, [i in 1:n_fac, j in 1:n_fac, k in 1:K], w[i,j,k] == 0)\n",
    "    @constraint(m, [i in 1:n_fac+n_clients_2, k in 1:K], \n",
    "        sum(w[i,j,k] for j in 1:n_fac+n_clients_2) - sum(w[j,i,k] for j in 1:n_fac+n_clients_2) == 0)\n",
    "    @constraint(m, [i in n_fac+1:n_clients_2, k in 1:K], q[i] * sum(w[i,j,k] for j in 1:n_fac+n_clients_2) <= C[k])\n",
    "    \n",
    "    @constraint(m, [i in 1:n_fac, k in 1:K], \n",
    "        sum(w[i,1:n_fac+n_clients_2,k]) + sum(w[1:n_fac+n_clients_2,i,k]) - y[i] <= 1)\n",
    "    \n",
    "    #optimizar \n",
    "    optimize!(m)\n",
    "    #solver\n",
    "    x_sol = JuMP.value.(x) #recupera toda la matriz x\n",
    "    w_sol = JuMP.value.(w)\n",
    "    y_sol = JuMP.value.(y)\n",
    "    \n",
    "    return x_sol, w_sol, y_sol\n",
    "    \n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "UndefVarError: LRP not defined",
     "output_type": "error",
     "traceback": [
      "UndefVarError: LRP not defined",
      "",
      "Stacktrace:",
      " [1] top-level scope",
      "   @ In[2]:1",
      " [2] eval",
      "   @ .\\boot.jl:360 [inlined]",
      " [3] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)",
      "   @ Base .\\loading.jl:1094"
     ]
    }
   ],
   "source": [
    "x_sol, w_sol, y_sol = LRP(d,q,C);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "selected_edges (generic function with 1 method)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#parejas de donde sales a donde vas\n",
    "function selected_edges(x::Array{Float64}, n)\n",
    "    return Tuple{Int,Int}[(j,i) for i in 1:n, j in 1:n if x[i, j] > 0.5]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trace_route (generic function with 1 method)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#trazas reales de la matriz de distancia\n",
    "function trace_route(edges, coordinates, tier)\n",
    "    \n",
    "    if tier == 1 \n",
    "        x = hcat([coordinates[i,:] for (i,j) in edges]...)'\n",
    "        y = hcat([coordinates[j,:] for (i,j) in edges]...)'\n",
    "    else\n",
    "        x = hcat([i <= n_fac ? coordinates[i,:] : coordinates[i+n_clients_1,:] for (i,j) in edges]...)'\n",
    "        y = hcat([j <= n_fac ? coordinates[j,:] : coordinates[j+n_clients_1,:] for (i,j) in edges]...)'\n",
    "    end\n",
    "    \n",
    "    x,y\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges_1 = selected_edges(x_sol, size(x_sol, 1));\n",
    "edges_2 = selected_edges(w_sol, size(w_sol, 1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "route_x_1, route_y_1 = trace_route(edges_1, coordinates, 1);\n",
    "route_x_2, route_y_2 = trace_route(edges_2, coordinates, 2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "Figure()"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CairoMakie.scatter(coordinates)\n",
    "\n",
    "for i in 1:n_fac+n_clients_1\n",
    "    scatterlines!([route_x_1[i,1], route_y_1[i,1]], [route_x_1[i,2], route_y_1[i,2]], color = :blue, markersize=20, marker=:diamond)\n",
    "end\n",
    "\n",
    "for i in 1:n_fac+n_clients_2\n",
    "    scatterlines!([route_x_2[i,1], route_y_2[i,1]], [route_x_2[i,2], route_y_2[i,2]], color = :red, markersize=15, marker=:circle)\n",
    "end\n",
    "\n",
    "current_figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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