{
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  {
   "cell_type": "markdown",
   "id": "2fd45b3a-9a24-4782-812c-08223edb750e",
   "metadata": {},
   "source": [
    "# Prueba del algoritmo genetico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "45972b70-b2a6-48f2-aafa-9f660548079a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34eab22f-a400-4a14-9ac4-047d87dff69f",
   "metadata": {},
   "source": [
    "Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a5736dba-4c38-4b7f-9a1a-963bdb006236",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Ciudad:\n",
    "    regiones = {\n",
    "        'costa': {\n",
    "            'plazoentrega': 1\n",
    "        },\n",
    "        'sierra': {\n",
    "            'plazoentrega': 2\n",
    "        },\n",
    "        'selva': {\n",
    "            'plazoentrega': 3\n",
    "        }\n",
    "    }\n",
    "    def __init__(self, nombre, region, longitud, latitud):\n",
    "        self.nombre = nombre\n",
    "        self.region = region\n",
    "        self.x = longitud\n",
    "        self.y = latitud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "8ee9610f-d128-4d1b-9458-ca9048073f20",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Road_network:\n",
    "    def __init__(self, cities, distances):\n",
    "        \"\"\"Grafo completo del pais\n",
    "        \n",
    "        Params\n",
    "        ------\n",
    "        \n",
    "        cities: list\n",
    "            Lista de objetos Ciudad\n",
    "            \n",
    "        routes: dict\n",
    "            (Aun no se como implementar esto)\n",
    "        \"\"\"\n",
    "        \n",
    "        self.cities = cities\n",
    "        self.routes = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "7dd72c93-acba-46f9-ac99-8c3d1a8cfa67",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Vehiculo:\n",
    "    tipos = {\n",
    "        1: {\n",
    "            'cargamax': 50\n",
    "        },\n",
    "        2: {\n",
    "            'cargamax': 100\n",
    "        },\n",
    "        3: {\n",
    "            'cargamax': 200\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    def __init__(self):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "474a3596-f75d-411e-bf4c-20b8b8259434",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Pedido:\n",
    "    def __init__(self, cliente, cantidad):\n",
    "        self.cliente = cliente\n",
    "        self.cantidad = cantidad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "7602722f-9026-44dc-b922-e17a7d3af45b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Cliente:\n",
    "    def __init__(self, nombre, ciudad):\n",
    "        self.nombre = nombre\n",
    "        self.ciudad = ciudad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f6b4829a-9001-410c-b20c-01c65c777d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class VRP:\n",
    "    def __init__(self):\n",
    "        # Conjuntos\n",
    "        self.I = range(2)\n",
    "        self.J = range(3)\n",
    "        self.T = range(5)       # en horas\n",
    "        self.V = range(3)       # 3 tipos de vehiculos\n",
    "    \n",
    "    def init_data(self):\n",
    "        \"\"\"\n",
    "        Lista los parametros iniciales, definidos en \"Modelo Matematico\" en ISA v02\n",
    "        \n",
    "        i: almacen grande (depot)\n",
    "        j: almacen pequeño (customer)\n",
    "        v: tipo de vehiculo\n",
    "        t: tiempo\n",
    "        \"\"\"\n",
    "        # Parametros\n",
    "        # Nombres cortos confunden, pero matrices de varias dimensiones \n",
    "        # sin etiquetas confunden mas\n",
    "        \n",
    "        # Demanda\n",
    "        self.D_jt = [ [ random.choice([0,1,2,3]) for _ in self.I ] for _ in self.T ]\n",
    "        # Capacidades de vehiculos\n",
    "        self.VL_v = [ random.choice([10, 15, 20]) for _ in self.V ]\n",
    "        # distancia entre almacen i, j\n",
    "        self.d_ij = [ random.choice([25,50,100]) for _ in self.V ]\n",
    "        #self.r_ijvt = [ [ 0 for _ in self.V ] for "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ab559513-5c14-4dd2-a51d-7737114dfba1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 10, 15])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = VRP()\n",
    "p.init_data()\n",
    "np.array(p.VL_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2c3c85e0-a90c-4fda-86f7-778d7328c74d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[25, 50, 50]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.d_ij"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "511ff788-0d1a-4ac7-9575-de182d236574",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "611c9a0d-bb1a-48eb-af37-f033abe8ed66",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64c68216-b9f1-45f9-a0fe-3862ab106c24",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cb6bb08-1547-4b64-993f-b2c453535264",
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b83d9e98-db8f-45cd-9eff-07d4194f7e07",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8c06031-9c55-4e13-a27b-91b0886902e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9483c22-243f-44e5-9a6d-09da92354554",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12952643-4a10-40bf-8af3-41319c744732",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0306c4f-eb68-4009-9390-0f881c6a8cb4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3ad92de-b2ed-4f21-a696-1fa2981f89dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def genetic_algorithm(population, fitness_fn, ngen=100, pmut=0.1):\n",
    "    \"Algoritmo Genetico \"\n",
    "    \n",
    "    popsize = len(population)\n",
    "    evaluate_population(population, fitness_fn)  # evalua la poblacion inicial\n",
    "    ibest = sorted(range(len(population)), key=lambda i: population[i].fitness, reverse=True)[:1]\n",
    "    bestfitness = [population[ibest[0]].fitness]\n",
    "    print(\"Poblacion inicial, best_fitness = {}\".format(population[ibest[0]].fitness))\n",
    "    \n",
    "    for g in range(ngen):   # Por cada generacion\n",
    "        \n",
    "        ## Selecciona las parejas de padres para cruzamiento \n",
    "        mating_pool = []\n",
    "        for i in range(int(popsize/2)): mating_pool.append(select_parents_roulette(population)) \n",
    "        \n",
    "        ## Crea la poblacion descendencia cruzando las parejas del mating pool con Recombinación de 1 punto\n",
    "        offspring_population = []\n",
    "        for i in range(len(mating_pool)): \n",
    "            #offspring_population.extend( mating_pool[i][0].crossover_onepoint(mating_pool[i][1]) )\n",
    "            offspring_population.extend( mating_pool[i][0].crossover_uniform(mating_pool[i][1]) )\n",
    "\n",
    "        ## Aplica el operador de mutacion con probabilidad pmut en cada hijo generado\n",
    "        for i in range(len(offspring_population)):\n",
    "            if random.uniform(0, 1) < pmut: \n",
    "                offspring_population[i] = offspring_population[i].mutate_position()\n",
    "        \n",
    "        ## Evalua la poblacion descendencia\n",
    "        evaluate_population(offspring_population, fitness_fn)  # evalua la poblacion inicial\n",
    "        \n",
    "        ## Selecciona popsize individuos para la sgte. generación de la union de la pob. actual y  pob. descendencia\n",
    "        population = select_survivors(population, offspring_population, popsize)\n",
    "\n",
    "        ## Almacena la historia del fitness del mejor individuo\n",
    "        ibest = sorted(range(len(population)), key=lambda i: population[i].fitness, reverse=True)[:1]\n",
    "        bestfitness.append(population[ibest[0]].fitness)\n",
    "        print(\"generacion {}, best_fitness = {}\".format(g, population[ibest[0]].fitness))\n",
    "    \n",
    "    return population[ibest[0]], bestfitness  "
   ]
  }
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