transp_nofn.py

##@file transp_nofn.py
#@brief a model for the transportation problem
"""
Model for solving a transportation problem:
minimize the total transportation cost for satisfying demand at
customers, from capacitated facilities.

Data:
    I - set of customers
    J - set of facilities
    c[i,j] - unit transportation cost on arc (i,j)
    d[i] - demand at node i
    M[j] - capacity

Copyright (c) by Joao Pedro PEDROSO and Mikio KUBO, 2012
"""

from pyscipopt import Model, quicksum, multidict

d = {1:80, 2:270, 3:250 , 4:160, 5:180}  # demand
I = d.keys()

M = {1:500, 2:500, 3:500}  # capacity
J = M.keys()

c = {(1,1):4,    (1,2):6,    (1,3):9,  # cost
     (2,1):5,    (2,2):4,    (2,3):7,
     (3,1):6,    (3,2):3,    (3,3):4,
     (4,1):8,    (4,2):5,    (4,3):3,
     (5,1):10,   (5,2):8,    (5,3):4,
     }

model = Model("transportation")

# Create variables
x = {}

for i in I:
    for j in J:
        x[i,j] = model.addVar(vtype="C", name="x(%s,%s)" % (i,j))

# Demand constraints
for i in I:
    model.addCons(sum(x[i,j] for j in J if (i,j) in x) == d[i], name="Demand(%s)" % i)

# Capacity constraints
for j in J:
    model.addCons(sum(x[i,j] for i in I if (i,j) in x) <= M[j], name="Capacity(%s)" % j)

# Objective
model.setObjective(quicksum(c[i,j]*x[i,j]  for (i,j) in x), "minimize")

model.optimize()

print("Optimal value:", model.getObjVal())

EPS = 1.e-6

for (i,j) in x:
    if model.getVal(x[i,j]) > EPS:
        print("sending quantity %10s from factory %3s to customer %3s" % (model.getVal(x[i,j]),j,i))