Ejercicio6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Ejemplo 5](images/Ej5.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyomo.environ import *\n",
    "import pandas as pd\n",
    "\n",
    "model = ConcreteModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>H1</th>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>120</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>280</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1    2    3    4  5\n",
       "H1  0  600  120  280    0  0\n",
       "H2  0    0  120  280  200  0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hot_datos = {0: [0,0], 1: [600,0], 2: [120,120], 3: [280,280],4: [0,200], 5:[0,0]}\n",
    "Hot_df = pd.DataFrame(Hot_datos,index=['H1','H2'])\n",
    "Hot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>500</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0  1  2    3    4    5\n",
       "C1  0  0  0  700  500  400"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cold_datos =  {0: [0], 1:[0], 2: [0], 3: [700], 4: [500], 5:[400]}\n",
    "Cold_df = pd.DataFrame(Cold_datos,index=['C1'])\n",
    "Cold_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "hotst = Hot_df.index.values.tolist()\n",
    "coldst = Cold_df.index.values.tolist()\n",
    "s=hotst.copy()\n",
    "s.insert(0,'S')\n",
    "w=coldst.copy()\n",
    "w.insert(0,'W')\n",
    "\n",
    "model.i=Set(initialize=hotst)\n",
    "model.j=Set(initialize=coldst)\n",
    "model.s=Set(initialize=s)\n",
    "model.S=Set(initialize='S')\n",
    "model.w=Set(initialize=w)\n",
    "model.W=Set(initialize='W')\n",
    "model.k=Set(initialize=Hot_df.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 Set Declarations\n",
      "    S : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    1 :  {'S',}\n",
      "    W : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    1 :  {'W',}\n",
      "    i : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    2 : {'H1', 'H2'}\n",
      "    j : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    1 : {'C1',}\n",
      "    k : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    6 : {0, 1, 2, 3, 4, 5}\n",
      "    s : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {'S', 'H1', 'H2'}\n",
      "    w : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    2 : {'W', 'C1'}\n",
      "\n",
      "7 Declarations: i j s S w W k\n"
     ]
    }
   ],
   "source": [
    "model.pprint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Qs = model.Qs = Var(model.k,within = NonNegativeReals) \n",
    "Qw = model.Qw = Var(model.k,within = NonNegativeReals) \n",
    "R = model.R = Var(model.s,model.k,within = NonNegativeReals)\n",
    "Q = model.Q = Var(model.s,model.w,model.k,within = NonNegativeReals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.util = Objective(expr = model.Qs[1] + model.Qw[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "nk = list(model.k)[1:] \n",
    "model.C1 = ConstraintList()\n",
    "for k in nk:\n",
    "    for i in model.i:\n",
    "        model.C1.add(\n",
    "        R[i,k-1]+Hot_df.loc[i,k] == R[i,k]+sum(Q[i,j,k] for j in model.j)+sum(Q[i,w,k] for w in model.W)\n",
    "        )\n",
    "\n",
    "model.C2 = ConstraintList()\n",
    "for k in nk:\n",
    "    for s in model.S:\n",
    "        model.C2.add(\n",
    "        R[s,k-1]+Qs[k] == R[s,k]+sum(Q[s,j,k] for j in model.j)\n",
    "        )        \n",
    "\n",
    "model.C3 = ConstraintList()\n",
    "for k in nk:\n",
    "    for j in model.j:\n",
    "        model.C3.add(\n",
    "        sum(Q[i,j,k] for i in model.i)+ sum(Q[i,j,k] for i in model.S) == Cold_df.loc[j,k]\n",
    "        )\n",
    "\n",
    "model.C4 = ConstraintList()\n",
    "for k in nk:\n",
    "        model.C4.add(\n",
    "        sum(Q[i,'W',k] for i in model.i)  == Qw[k]\n",
    "        )\n",
    "\n",
    "\n",
    "model.C5 = ConstraintList()\n",
    "for s in model.s:\n",
    "    model.C5.add(\n",
    "        R[s,0]==0\n",
    "        )\n",
    "\n",
    "model.C6 = ConstraintList()\n",
    "for s in model.s:\n",
    "    model.C6.add(\n",
    "        R[s,5]==0\n",
    "        )\n",
    "\n",
    "nm = list(model.k)[0:1]+list(model.k)[2:]\n",
    "model.C7 = ConstraintList()\n",
    "for m in nm:\n",
    "    model.C7.add(\n",
    "        Qs[m]==0\n",
    "        )\n",
    "\n",
    "nn = list(model.k)[0:5]\n",
    "model.C8 = ConstraintList()\n",
    "for n in nn:\n",
    "    model.C8.add(\n",
    "        Qw[n]==0\n",
    "        )\n",
    "\n",
    "model.C9 = ConstraintList()\n",
    "for k in model.k:\n",
    "    model.C9.add(\n",
    "        Q['S','W',k] ==0\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 Set Declarations\n",
      "    C1_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :   10 : {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}\n",
      "    C2_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    5 : {1, 2, 3, 4, 5}\n",
      "    C3_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    5 : {1, 2, 3, 4, 5}\n",
      "    C4_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    5 : {1, 2, 3, 4, 5}\n",
      "    C5_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {1, 2, 3}\n",
      "    C6_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {1, 2, 3}\n",
      "    C7_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    5 : {1, 2, 3, 4, 5}\n",
      "    C8_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    5 : {1, 2, 3, 4, 5}\n",
      "    C9_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    6 : {1, 2, 3, 4, 5, 6}\n",
      "    Q_index : Size=1, Index=None, Ordered=True\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     3 :  s*w*k :   36 : {('S', 'W', 0), ('S', 'W', 1), ('S', 'W', 2), ('S', 'W', 3), ('S', 'W', 4), ('S', 'W', 5), ('S', 'C1', 0), ('S', 'C1', 1), ('S', 'C1', 2), ('S', 'C1', 3), ('S', 'C1', 4), ('S', 'C1', 5), ('H1', 'W', 0), ('H1', 'W', 1), ('H1', 'W', 2), ('H1', 'W', 3), ('H1', 'W', 4), ('H1', 'W', 5), ('H1', 'C1', 0), ('H1', 'C1', 1), ('H1', 'C1', 2), ('H1', 'C1', 3), ('H1', 'C1', 4), ('H1', 'C1', 5), ('H2', 'W', 0), ('H2', 'W', 1), ('H2', 'W', 2), ('H2', 'W', 3), ('H2', 'W', 4), ('H2', 'W', 5), ('H2', 'C1', 0), ('H2', 'C1', 1), ('H2', 'C1', 2), ('H2', 'C1', 3), ('H2', 'C1', 4), ('H2', 'C1', 5)}\n",
      "    R_index : Size=1, Index=None, Ordered=True\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     2 :    s*k :   18 : {('S', 0), ('S', 1), ('S', 2), ('S', 3), ('S', 4), ('S', 5), ('H1', 0), ('H1', 1), ('H1', 2), ('H1', 3), ('H1', 4), ('H1', 5), ('H2', 0), ('H2', 1), ('H2', 2), ('H2', 3), ('H2', 4), ('H2', 5)}\n",
      "    S : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    1 :  {'S',}\n",
      "    W : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    1 :  {'W',}\n",
      "    i : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    2 : {'H1', 'H2'}\n",
      "    j : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    1 : {'C1',}\n",
      "    k : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    6 : {0, 1, 2, 3, 4, 5}\n",
      "    s : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {'S', 'H1', 'H2'}\n",
      "    w : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    2 : {'W', 'C1'}\n",
      "\n",
      "4 Var Declarations\n",
      "    Q : Size=36, Index=Q_index\n",
      "        Key             : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "        ('H1', 'C1', 0) :     0 :  None :  None : False :  True : NonNegativeReals\n",
      "        ('H1', 'C1', 1) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "        ('H1', 'C1', 2) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "        ('H1', 'C1', 3) :     0 : 300.0 :  None : False : False : NonNegativeReals\n",
      "        ('H1', 'C1', 4) :     0 : 300.0 :  None : False : False : NonNegativeReals\n",
      "        ('H1', 'C1', 5) :     0 : 400.0 :  None : False : False : NonNegativeReals\n",
      "         ('H1', 'W', 0) :     0 :  None :  None : False :  True : NonNegativeReals\n",
      "         ('H1', 'W', 1) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('H1', 'W', 2) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('H1', 'W', 3) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('H1', 'W', 4) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('H1', 'W', 5) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "        ('H2', 'C1', 0) :     0 :  None :  None : False :  True : NonNegativeReals\n",
      "        ('H2', 'C1', 1) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "        ('H2', 'C1', 2) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "        ('H2', 'C1', 3) :     0 : 400.0 :  None : False : False : NonNegativeReals\n",
      "        ('H2', 'C1', 4) :     0 : 200.0 :  None : False : False : NonNegativeReals\n",
      "        ('H2', 'C1', 5) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('H2', 'W', 0) :     0 :  None :  None : False :  True : NonNegativeReals\n",
      "         ('H2', 'W', 1) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('H2', 'W', 2) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('H2', 'W', 3) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('H2', 'W', 4) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('H2', 'W', 5) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('S', 'C1', 0) :     0 :  None :  None : False :  True : NonNegativeReals\n",
      "         ('S', 'C1', 1) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('S', 'C1', 2) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('S', 'C1', 3) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('S', 'C1', 4) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('S', 'C1', 5) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          ('S', 'W', 0) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          ('S', 'W', 1) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          ('S', 'W', 2) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          ('S', 'W', 3) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          ('S', 'W', 4) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          ('S', 'W', 5) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "    Qs : Size=6, Index=k\n",
      "        Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "          0 :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          1 :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          2 :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          3 :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          4 :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          5 :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "    Qw : Size=6, Index=k\n",
      "        Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "          0 :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          1 :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          2 :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          3 :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          4 :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "          5 :     0 :  -0.0 :  None : False : False : NonNegativeReals\n",
      "    R : Size=18, Index=R_index\n",
      "        Key       : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "        ('H1', 0) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "        ('H1', 1) :     0 : 600.0 :  None : False : False : NonNegativeReals\n",
      "        ('H1', 2) :     0 : 720.0 :  None : False : False : NonNegativeReals\n",
      "        ('H1', 3) :     0 : 700.0 :  None : False : False : NonNegativeReals\n",
      "        ('H1', 4) :     0 : 400.0 :  None : False : False : NonNegativeReals\n",
      "        ('H1', 5) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "        ('H2', 0) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "        ('H2', 1) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "        ('H2', 2) :     0 : 120.0 :  None : False : False : NonNegativeReals\n",
      "        ('H2', 3) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "        ('H2', 4) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "        ('H2', 5) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('S', 0) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('S', 1) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('S', 2) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('S', 3) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('S', 4) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "         ('S', 5) :     0 :   0.0 :  None : False : False : NonNegativeReals\n",
      "\n",
      "1 Objective Declarations\n",
      "    util : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Sense    : Expression\n",
      "        None :   True : minimize : Qs[1] + Qw[5]\n",
      "\n",
      "9 Constraint Declarations\n",
      "    C1 : Size=10, Index=C1_index, Active=True\n",
      "        Key : Lower : Body                                               : Upper : Active\n",
      "          1 :   0.0 : R[H1,0] + 600 - (R[H1,1] + Q[H1,C1,1] + Q[H1,W,1]) :   0.0 :   True\n",
      "          2 :   0.0 :       R[H2,0] - (R[H2,1] + Q[H2,C1,1] + Q[H2,W,1]) :   0.0 :   True\n",
      "          3 :   0.0 : R[H1,1] + 120 - (R[H1,2] + Q[H1,C1,2] + Q[H1,W,2]) :   0.0 :   True\n",
      "          4 :   0.0 : R[H2,1] + 120 - (R[H2,2] + Q[H2,C1,2] + Q[H2,W,2]) :   0.0 :   True\n",
      "          5 :   0.0 : R[H1,2] + 280 - (R[H1,3] + Q[H1,C1,3] + Q[H1,W,3]) :   0.0 :   True\n",
      "          6 :   0.0 : R[H2,2] + 280 - (R[H2,3] + Q[H2,C1,3] + Q[H2,W,3]) :   0.0 :   True\n",
      "          7 :   0.0 :       R[H1,3] - (R[H1,4] + Q[H1,C1,4] + Q[H1,W,4]) :   0.0 :   True\n",
      "          8 :   0.0 : R[H2,3] + 200 - (R[H2,4] + Q[H2,C1,4] + Q[H2,W,4]) :   0.0 :   True\n",
      "          9 :   0.0 :       R[H1,4] - (R[H1,5] + Q[H1,C1,5] + Q[H1,W,5]) :   0.0 :   True\n",
      "         10 :   0.0 :       R[H2,4] - (R[H2,5] + Q[H2,C1,5] + Q[H2,W,5]) :   0.0 :   True\n",
      "    C2 : Size=5, Index=C2_index, Active=True\n",
      "        Key : Lower : Body                                  : Upper : Active\n",
      "          1 :   0.0 : R[S,0] + Qs[1] - (R[S,1] + Q[S,C1,1]) :   0.0 :   True\n",
      "          2 :   0.0 : R[S,1] + Qs[2] - (R[S,2] + Q[S,C1,2]) :   0.0 :   True\n",
      "          3 :   0.0 : R[S,2] + Qs[3] - (R[S,3] + Q[S,C1,3]) :   0.0 :   True\n",
      "          4 :   0.0 : R[S,3] + Qs[4] - (R[S,4] + Q[S,C1,4]) :   0.0 :   True\n",
      "          5 :   0.0 : R[S,4] + Qs[5] - (R[S,5] + Q[S,C1,5]) :   0.0 :   True\n",
      "    C3 : Size=5, Index=C3_index, Active=True\n",
      "        Key : Lower : Body                                : Upper : Active\n",
      "          1 :   0.0 : Q[H1,C1,1] + Q[H2,C1,1] + Q[S,C1,1] :   0.0 :   True\n",
      "          2 :   0.0 : Q[H1,C1,2] + Q[H2,C1,2] + Q[S,C1,2] :   0.0 :   True\n",
      "          3 : 700.0 : Q[H1,C1,3] + Q[H2,C1,3] + Q[S,C1,3] : 700.0 :   True\n",
      "          4 : 500.0 : Q[H1,C1,4] + Q[H2,C1,4] + Q[S,C1,4] : 500.0 :   True\n",
      "          5 : 400.0 : Q[H1,C1,5] + Q[H2,C1,5] + Q[S,C1,5] : 400.0 :   True\n",
      "    C4 : Size=5, Index=C4_index, Active=True\n",
      "        Key : Lower : Body                          : Upper : Active\n",
      "          1 :   0.0 : Q[H1,W,1] + Q[H2,W,1] - Qw[1] :   0.0 :   True\n",
      "          2 :   0.0 : Q[H1,W,2] + Q[H2,W,2] - Qw[2] :   0.0 :   True\n",
      "          3 :   0.0 : Q[H1,W,3] + Q[H2,W,3] - Qw[3] :   0.0 :   True\n",
      "          4 :   0.0 : Q[H1,W,4] + Q[H2,W,4] - Qw[4] :   0.0 :   True\n",
      "          5 :   0.0 : Q[H1,W,5] + Q[H2,W,5] - Qw[5] :   0.0 :   True\n",
      "    C5 : Size=3, Index=C5_index, Active=True\n",
      "        Key : Lower : Body    : Upper : Active\n",
      "          1 :   0.0 :  R[S,0] :   0.0 :   True\n",
      "          2 :   0.0 : R[H1,0] :   0.0 :   True\n",
      "          3 :   0.0 : R[H2,0] :   0.0 :   True\n",
      "    C6 : Size=3, Index=C6_index, Active=True\n",
      "        Key : Lower : Body    : Upper : Active\n",
      "          1 :   0.0 :  R[S,5] :   0.0 :   True\n",
      "          2 :   0.0 : R[H1,5] :   0.0 :   True\n",
      "          3 :   0.0 : R[H2,5] :   0.0 :   True\n",
      "    C7 : Size=5, Index=C7_index, Active=True\n",
      "        Key : Lower : Body  : Upper : Active\n",
      "          1 :   0.0 : Qs[0] :   0.0 :   True\n",
      "          2 :   0.0 : Qs[2] :   0.0 :   True\n",
      "          3 :   0.0 : Qs[3] :   0.0 :   True\n",
      "          4 :   0.0 : Qs[4] :   0.0 :   True\n",
      "          5 :   0.0 : Qs[5] :   0.0 :   True\n",
      "    C8 : Size=5, Index=C8_index, Active=True\n",
      "        Key : Lower : Body  : Upper : Active\n",
      "          1 :   0.0 : Qw[0] :   0.0 :   True\n",
      "          2 :   0.0 : Qw[1] :   0.0 :   True\n",
      "          3 :   0.0 : Qw[2] :   0.0 :   True\n",
      "          4 :   0.0 : Qw[3] :   0.0 :   True\n",
      "          5 :   0.0 : Qw[4] :   0.0 :   True\n",
      "    C9 : Size=6, Index=C9_index, Active=True\n",
      "        Key : Lower : Body     : Upper : Active\n",
      "          1 :   0.0 : Q[S,W,0] :   0.0 :   True\n",
      "          2 :   0.0 : Q[S,W,1] :   0.0 :   True\n",
      "          3 :   0.0 : Q[S,W,2] :   0.0 :   True\n",
      "          4 :   0.0 : Q[S,W,3] :   0.0 :   True\n",
      "          5 :   0.0 : Q[S,W,4] :   0.0 :   True\n",
      "          6 :   0.0 : Q[S,W,5] :   0.0 :   True\n",
      "\n",
      "32 Declarations: i j s S w W k Qs Qw R_index R Q_index Q util C1_index C1 C2_index C2 C3_index C3 C4_index C4 C5_index C5 C6_index C6 C7_index C7 C8_index C8 C9_index C9\n",
      "# ==========================================================\n",
      "# = Solver Results                                         =\n",
      "# ==========================================================\n",
      "# ----------------------------------------------------------\n",
      "#   Problem Information\n",
      "# ----------------------------------------------------------\n",
      "Problem: \n",
      "- Name: unknown\n",
      "  Lower bound: 0.0\n",
      "  Upper bound: 0.0\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 48\n",
      "  Number of variables: 62\n",
      "  Number of nonzeros: 113\n",
      "  Sense: minimize\n",
      "# ----------------------------------------------------------\n",
      "#   Solver Information\n",
      "# ----------------------------------------------------------\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Termination condition: optimal\n",
      "  Statistics: \n",
      "    Branch and bound: \n",
      "      Number of bounded subproblems: 0\n",
      "      Number of created subproblems: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.027684450149536133\n",
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n"
     ]
    }
   ],
   "source": [
    "results = SolverFactory('glpk').solve(model)\n",
    "model.pprint()\n",
    "results.write()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cold utility = -0.00, Hot utility = 0.00\n"
     ]
    }
   ],
   "source": [
    "Qss = value(model.Qs[1])\n",
    "Qww = value(model.Qw[5])\n",
    "print('Cold utility = {0:2.2f}, Hot utility = {1:2.2f}'.format(Qww, Qss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora resolvemos el problema de mínimo número de intercambios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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>C1</th>\n",
       "      <th>W</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H1</th>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H2</th>\n",
       "      <td>600</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      C1  W\n",
       "S      0  0\n",
       "H1  1000  0\n",
       "H2   600  0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U = {'C1': [0, 1000,600],  'W':[0,0,0]}\n",
    "U_df = pd.DataFrame(U, index=['S', 'H1', 'H2'])\n",
    "U_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.y=Var(model.s,model.w, within=Binary)\n",
    "\n",
    "model.C10 = ConstraintList()\n",
    "for s in model.s:\n",
    "    for w in model.w:\n",
    "        model.C10.add(\n",
    "        sum(Q[s,w,k] for k in model.k)-U_df.loc[s,w]*model.y[s,w] <=0\n",
    "        )    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.util.deactivate()\n",
    "model.sumy = Objective(expr=sum(model.y[s,w] for s in model.s for w in model.w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# ==========================================================\n",
      "# = Solver Results                                         =\n",
      "# ==========================================================\n",
      "# ----------------------------------------------------------\n",
      "#   Problem Information\n",
      "# ----------------------------------------------------------\n",
      "Problem: \n",
      "- Name: unknown\n",
      "  Lower bound: 2.0\n",
      "  Upper bound: 2.0\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 54\n",
      "  Number of variables: 73\n",
      "  Number of nonzeros: 151\n",
      "  Sense: minimize\n",
      "# ----------------------------------------------------------\n",
      "#   Solver Information\n",
      "# ----------------------------------------------------------\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Termination condition: optimal\n",
      "  Statistics: \n",
      "    Branch and bound: \n",
      "      Number of bounded subproblems: 1\n",
      "      Number of created subproblems: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.020473957061767578\n",
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n"
     ]
    }
   ],
   "source": [
    "results = SolverFactory('glpk').solve(model)\n",
    "#model.pprint()\n",
    "results.write()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Ejemplo 5](images/Ej5_2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelNLP = ConcreteModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" f1I = modelNLP.f1I = Var(bounds=(0,10), initialize=2.942)\n",
    "f2I = modelNLP.f2I = Var(bounds=(0,10),initialize=7.058)\n",
    "f1O = modelNLP.f1O = Var(bounds=(0,10),initialize=2.942)\n",
    "f2O = modelNLP.f2O = Var(bounds=(0,10),initialize=7.058)\n",
    "f1E = modelNLP.f1E = Var(bounds=(0,10),initialize=2.942)\n",
    "f2E = modelNLP.f2E = Var(bounds=(0,10),initialize=7.058)\n",
    "f12B = modelNLP.f12B = Var(bounds=(0,10),initialize=0)\n",
    "f21B = modelNLP.f21B = Var(bounds=(0,10),initialize=0)\n",
    "\n",
    "T1I = modelNLP.T1I = Var(bounds=(150,500),initialize=150)\n",
    "T2I = modelNLP.T2I = Var(bounds=(150,500),initialize=150)\n",
    "T1O = modelNLP.T1O = Var(bounds=(150,500),initialize=150)\n",
    "T2O = modelNLP.T2O = Var(bounds=(150,500),initialize=150)\n",
    "\n",
    "DT11 = modelNLP.DT11 = Var(bounds=(10,500),initialize=10)\n",
    "DT12 = modelNLP.DT12 = Var(bounds=(10,500),initialize=10)\n",
    "DT21 = modelNLP.DT21 = Var(bounds=(10,500),initialize=10)\n",
    "DT22 = modelNLP.DT22 = Var(bounds=(10,500),initialize=10) \"\"\"\n",
    "\n",
    "f1I = modelNLP.f1I = Var(bounds=(0,10), initialize=10)\n",
    "f2I = modelNLP.f2I = Var(bounds=(0,10),initialize=10)\n",
    "f1O = modelNLP.f1O = Var(bounds=(0,10),initialize=10)\n",
    "f2O = modelNLP.f2O = Var(bounds=(0,10),initialize=10)\n",
    "f1E = modelNLP.f1E = Var(bounds=(0,10),initialize=10)\n",
    "f2E = modelNLP.f2E = Var(bounds=(0,10),initialize=10)\n",
    "f12B = modelNLP.f12B = Var(bounds=(0,10),initialize=10)\n",
    "f21B = modelNLP.f21B = Var(bounds=(0,10),initialize=10)\n",
    "\n",
    "T1I = modelNLP.T1I = Var(bounds=(150,500),initialize=500)\n",
    "T2I = modelNLP.T2I = Var(bounds=(150,500),initialize=500)\n",
    "T1O = modelNLP.T1O = Var(bounds=(150,500),initialize=500)\n",
    "T2O = modelNLP.T2O = Var(bounds=(150,500),initialize=500)\n",
    "\n",
    "DT11 = modelNLP.DT11 = Var(bounds=(10,500),initialize=500)\n",
    "DT12 = modelNLP.DT12 = Var(bounds=(10,500),initialize=500)\n",
    "DT21 = modelNLP.DT21 = Var(bounds=(10,500),initialize=500)\n",
    "DT22 = modelNLP.DT22 = Var(bounds=(10,500),initialize=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelNLP.SPLIT1 = Constraint(expr=f1I+f2I==10)\n",
    "modelNLP.SPLIT2 = Constraint(expr=f1O+f12B-f1E==0)\n",
    "modelNLP.SPLIT3 = Constraint(expr=f2O+f21B-f2E==0)\n",
    "modelNLP.MIX1 = Constraint(expr=f1I+f21B-f1E==0)\n",
    "modelNLP.MIX2 = Constraint(expr=f2I+f12B-f2E==0)\n",
    "modelNLP.MIX3 = Constraint(expr=f1O+f2O==10)\n",
    "\n",
    "\n",
    "modelNLP.EN_MIX1 = Constraint(expr=150*f1I+T2O*f21B-T1I*f1E==0)\n",
    "modelNLP.EN_MIX2 = Constraint(expr=150*f2I+T1O*f12B-T2I*f2E==0)\n",
    "modelNLP.EN_MIX3 = Constraint(expr=T2O*f2O+T1O*f1O-310*10==0)\n",
    "\n",
    "modelNLP.HEATEX1 = Constraint(expr=f1E*(T1O-T1I)==1000)\n",
    "modelNLP.HEATEX2 = Constraint(expr=f2E*(T2O-T2I)==600)\n",
    "\n",
    "modelNLP.D11 = Constraint(expr=500-T1O>=10)\n",
    "modelNLP.D12 = Constraint(expr=250-T1I>=10)\n",
    "modelNLP.D21 = Constraint(expr=350-T2O>=10)\n",
    "modelNLP.D22 = Constraint(expr=200-T2I>=10)\n",
    "\n",
    "modelNLP.DELT11 = Constraint(expr=500-T1O==DT11)\n",
    "modelNLP.DELT12 = Constraint(expr=250-T1I==DT12)\n",
    "modelNLP.DELT21 = Constraint(expr=350-T2O==DT21)\n",
    "modelNLP.DELT22 = Constraint(expr=200-T2I==DT22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelNLP.Coste = Objective(expr=1300*(1000/(0.05*(2/3*(DT11*DT12)**0.5+1/6*(DT11+DT12))))**0.6+1300*(600/(0.05*(2/3*(DT21*DT22)**0.5+1/6*(DT21+DT22))))**0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# ==========================================================\n",
      "# = Solver Results                                         =\n",
      "# ==========================================================\n",
      "# ----------------------------------------------------------\n",
      "#   Problem Information\n",
      "# ----------------------------------------------------------\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 19\n",
      "  Number of variables: 16\n",
      "  Sense: unknown\n",
      "# ----------------------------------------------------------\n",
      "#   Solver Information\n",
      "# ----------------------------------------------------------\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.11.1\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.17274713516235352\n",
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "El coste usando ipopt es de 57587.93\n",
      "El área del primer cambiador es 165.81\n",
      "El área del segundo cambiador es 183.75\n",
      "La cantidad que se manda al cambiador 1 es 4.85\n",
      "La cantidad que entra al cambiador 1 es 4.85\n",
      "La cantidad que se manda al cambiador 2 es 5.15\n",
      "La cantidad que entra al cambiador 2 es 5.15\n"
     ]
    }
   ],
   "source": [
    "solver = 'ipopt'\n",
    "resultsNLP = SolverFactory(solver).solve(modelNLP)\n",
    "#modelNLP.pprint()\n",
    "resultsNLP.write()\n",
    "Cost = value(modelNLP.Coste)\n",
    "Area1 = 1000/(0.05*(2/3*(value(DT11)*value(DT12))**0.5+1/6*(value(DT11)+value(DT12))))\n",
    "Area2 = 600/(0.05*(2/3*(value(DT21)*value(DT22))**0.5+1/6*(value(DT21)+value(DT22))))\n",
    "\n",
    "print('El coste usando {0} es de {1:.2f}'.format(solver,Cost))\n",
    "print('El área del primer cambiador es {0:.2f}'.format(Area1))\n",
    "print('El área del segundo cambiador es {0:.2f}'.format(Area2))\n",
    "print('La cantidad que se manda al cambiador 1 es {0:.2f}'.format(value(modelNLP.f1I)))\n",
    "print('La cantidad que entra al cambiador 1 es {0:.2f}'.format(value(modelNLP.f1E)))\n",
    "print('La cantidad que se manda al cambiador 2 es {0:.2f}'.format(value(modelNLP.f2I)))\n",
    "print('La cantidad que entra al cambiador 2 es {0:.2f}'.format(value(modelNLP.f2E)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# ==========================================================\n",
      "# = Solver Results                                         =\n",
      "# ==========================================================\n",
      "# ----------------------------------------------------------\n",
      "#   Problem Information\n",
      "# ----------------------------------------------------------\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 19\n",
      "  Number of variables: 16\n",
      "  Sense: unknown\n",
      "# ----------------------------------------------------------\n",
      "#   Solver Information\n",
      "# ----------------------------------------------------------\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.11.1\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.02399444580078125\n",
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "El coste usando multistart es de 57587.93\n",
      "El área del primer cambiador es 165.81\n",
      "El área del segundo cambiador es 183.75\n",
      "La cantidad que se manda al cambiador 1 es 4.85\n",
      "La cantidad que entra al cambiador 1 es 4.85\n",
      "La cantidad que se manda al cambiador 2 es 5.15\n",
      "La cantidad que entra al cambiador 2 es 5.15\n"
     ]
    }
   ],
   "source": [
    "solver = 'multistart'\n",
    "resultsNLP = SolverFactory(solver).solve(modelNLP)\n",
    "#modelNLP.pprint()\n",
    "resultsNLP.write()\n",
    "Cost = value(modelNLP.Coste)\n",
    "Area1 = 1000/(0.05*(2/3*(value(DT11)*value(DT12))**0.5+1/6*(value(DT11)+value(DT12))))\n",
    "Area2 = 600/(0.05*(2/3*(value(DT21)*value(DT22))**0.5+1/6*(value(DT21)+value(DT22))))\n",
    "\n",
    "print('El coste usando {0} es de {1:.2f}'.format(solver,Cost))\n",
    "print('El área del primer cambiador es {0:.2f}'.format(Area1))\n",
    "print('El área del segundo cambiador es {0:.2f}'.format(Area2))\n",
    "print('La cantidad que se manda al cambiador 1 es {0:.2f}'.format(value(modelNLP.f1I)))\n",
    "print('La cantidad que entra al cambiador 1 es {0:.2f}'.format(value(modelNLP.f1E)))\n",
    "print('La cantidad que se manda al cambiador 2 es {0:.2f}'.format(value(modelNLP.f2I)))\n",
    "print('La cantidad que entra al cambiador 2 es {0:.2f}'.format(value(modelNLP.f2E)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Job model.gms Start 10/02/23 17:34:43 25.0.0 r65090 WEX-WEI x86 64bit/MS Windows\n",
      "GAMS 25.0.0   Copyright (C) 1987-2018 GAMS Development. All rights reserved\n",
      "*** ************* BETA release\n",
      "*** GAMS Base Module 25.0.0 r65090 BETA Released 12Jan18 WEI x86 64bit/MS Windows \n",
      "*** ************* BETA release\n",
      "Licensee: Small MUD - 5 User License                     S170124:0844AO-WIN\n",
      "          Universidad Politecnica de Madrid, INGENIERIA QUIMICA INDUDC13043\n",
      "          License for teaching and research at degree granting institutions\n",
      "          License Admin: Ismael Diaz, ismael.diaz@upm.es                   \n",
      "--- Starting compilation\n",
      "--- model.gms(220) 2 Mb\n",
      "--- Starting execution: elapsed 0:00:00.065\n",
      "--- model.gms(119) 3 Mb\n",
      "--- Generating NLP model GAMS_MODEL\n",
      "--- model.gms(130) 5 Mb\n",
      "--- Reset Solvelink = 2\n",
      "---   20 rows  17 columns  53 non-zeroes\n",
      "---   67 nl-code  22 nl-non-zeroes\n",
      "--- model.gms(130) 3 Mb\n",
      "--- Executing BARON (Solvelink=2): elapsed 0:00:00.166\n",
      "\n",
      "GAMS/BARON       25.0.0 r65090 BETA Released 12Jan18 WEI x86 64bit/MS Windows \n",
      "\n",
      "--- Using Option File\n",
      "Reading parameter(s) from \"C:\\Users\\ismad\\AppData\\Local\\Temp\\tmpc9p6v5b2\\baron.opt\"\n",
      ">>   NLPSol= 6\n",
      ">>  ExtNLPsolver=conopt4\n",
      ">>  optcr=0.0001\n",
      ">>  optcr=0.0001\n",
      ">>  NumSol=10\n",
      "Finished reading from \"C:\\Users\\ismad\\AppData\\Local\\Temp\\tmpc9p6v5b2\\baron.opt\"\n",
      "===========================================================================\n",
      " BARON version 17.10.16. Built: WIN-64 Mon Oct 16 18:19:35 EDT 2017\n",
      "\n",
      " BARON is a product of The Optimization Firm, LLC. http://www.minlp.com/\n",
      "\n",
      " If you use this software, please cite publications from\n",
      " http://www.minlp.com/about-baron, such as: \n",
      "\n",
      " Tawarmalani, M. and N. V. Sahinidis, A polyhedral\n",
      " branch-and-cut approach to global optimization,\n",
      " Mathematical Programming, 103:225-249, 2005.\n",
      "===========================================================================\n",
      " This BARON run may utilize the following subsolver(s)\n",
      " For LP/MIP: ILOG CPLEX                                      \n",
      " For NLP: GAMS external NLP solver\n",
      "===========================================================================\n",
      " Starting solution is feasible with a value of     56825.8302117     \n",
      " Doing local search\n",
      " Solving bounding LP\n",
      " Starting multi-start local search\n",
      " Done with local search\n",
      "===========================================================================\n",
      "  Iteration    Open nodes         Time (s)    Lower bound      Upper bound\n",
      "          1             1             0.02     54402.1          57587.9    \n",
      "         29             0             0.06     56825.8          67683.7    \n",
      "\n",
      " Cleaning up\n",
      "*        29+            0             0.06     56825.8          56825.8    \n",
      "\n",
      "                         *** Normal completion ***            \n",
      "\n",
      " Wall clock time:                     1.00\n",
      " Total CPU time used:                 0.06\n",
      "\n",
      " Total no. of BaR iterations:      29\n",
      " Best solution found at node:      29\n",
      " Max. no. of nodes in memory:       4\n",
      "\n",
      " All done\n",
      "===========================================================================\n",
      "--- Solution 1 = 67683.7098433298\n",
      "--- Solution 2 = 57587.9329540251\n",
      "--- Solution 3 = 56825.8302116866\n",
      "\n",
      "Solution      = 56825.8302116866  \n",
      "Best possible = 56825.8302117\n",
      "Absolute gap  = 1.33732100948691E-8  optca = 1E-9\n",
      "Relative gap  = 2.35336818574376E-13  optcr = 0.0001\n",
      "  (Note that BARON uses a different formula to compute the relative gap as\n",
      "   was used for the above reported value.)\n",
      "\n",
      "--- Reading solution for model GAMS_MODEL\n",
      "--- model.gms(220) 3 Mb\n",
      "--- Putfile results C:\\Users\\ismad\\AppData\\Local\\Temp\\tmpc9p6v5b2\\results.dat\n",
      "--- Putfile statresults C:\\Users\\ismad\\AppData\\Local\\Temp\\tmpc9p6v5b2\\resultsstat.dat\n",
      "*** Status: Normal completion\n",
      "--- Job model.gms Stop 10/02/23 17:34:44 elapsed 0:00:01.019\n",
      "# ==========================================================\n",
      "# = Solver Results                                         =\n",
      "# ==========================================================\n",
      "# ----------------------------------------------------------\n",
      "#   Problem Information\n",
      "# ----------------------------------------------------------\n",
      "Problem: \n",
      "- Name: C\\x3a\\Users\\ismad\\AppData\\Local\\Temp\\tmpc9p6v5b2\\model.gms\n",
      "  Lower bound: 56825.8302117\n",
      "  Upper bound: 56825.83021168662\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 20.0\n",
      "  Number of variables: 17.0\n",
      "  Number of binary variables: None\n",
      "  Number of integer variables: 0.0\n",
      "  Number of continuous variables: 17.0\n",
      "  Number of nonzeros: 53.0\n",
      "  Sense: minimize\n",
      "# ----------------------------------------------------------\n",
      "#   Solver Information\n",
      "# ----------------------------------------------------------\n",
      "Solver: \n",
      "- Name: GAMS (25, 0, 0, 0)\n",
      "  Status: ok\n",
      "  Return code: 0\n",
      "  Message: None\n",
      "  User time: 0.9539999300614\n",
      "  System time: None\n",
      "  Wallclock time: None\n",
      "  Termination condition: optimal\n",
      "  Termination message: None\n",
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "El coste usando gams es de 56825.83\n",
      "El área del primer cambiador es 207.36\n",
      "El área del segundo cambiador es 137.23\n",
      "La cantidad que se manda al cambiador 1 es 0.00\n",
      "La cantidad que entra al cambiador 1 es 10.00\n",
      "La cantidad que se manda al cambiador 2 es 10.00\n",
      "La cantidad que entra al cambiador 2 es 10.00\n"
     ]
    }
   ],
   "source": [
    "solver = 'gams'\n",
    "options = {}\n",
    "#resultsNLP = SolverFactory(solver).solve(modelNLP, solver='baron', keepfiles=True, tee=True, add_options=['option optcr=1e-9;', 'option optca=0.001;', 'option NLP=baron;'])\n",
    "resultsNLP = SolverFactory('gams').solve(modelNLP, tee=True, solver='baron', add_options = ['GAMS_MODEL.optfile = 1;','$onecho > baron.opt', ' NLPSol= 6', 'ExtNLPsolver=conopt4', 'optcr=0.0001','optcr=0.0001','NumSol=10', '$offecho'])\n",
    "#modelNLP.pprint()\n",
    "resultsNLP.write()\n",
    "Cost = value(modelNLP.Coste)\n",
    "Area1 = 1000/(0.05*(2/3*(value(DT11)*value(DT12))**0.5+1/6*(value(DT11)+value(DT12))))\n",
    "Area2 = 600/(0.05*(2/3*(value(DT21)*value(DT22))**0.5+1/6*(value(DT21)+value(DT22))))\n",
    "\n",
    "print('El coste usando {0} es de {1:.2f}'.format(solver,Cost))\n",
    "print('El área del primer cambiador es {0:.2f}'.format(Area1))\n",
    "print('El área del segundo cambiador es {0:.2f}'.format(Area2))\n",
    "print('La cantidad que se manda al cambiador 1 es {0:.2f}'.format(value(modelNLP.f1I)))\n",
    "print('La cantidad que entra al cambiador 1 es {0:.2f}'.format(value(modelNLP.f1E)))\n",
    "print('La cantidad que se manda al cambiador 2 es {0:.2f}'.format(value(modelNLP.f2I)))\n",
    "print('La cantidad que entra al cambiador 2 es {0:.2f}'.format(value(modelNLP.f2E)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e06ff7da33dc9620448857a90ad8b5f428f0d573d205a934d2841c8aee45ea32"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}