""" transp.py: 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 gurobipy import * I,d = multidict({1:80, 2:270, 3:250 , 4:160, 5:180}) # demand J,M = multidict({1:500, 2:500, 3:500}) # capacity 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)) model.update() # Demand constraints for i in I: model.addConstr(quicksum(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.addConstr(quicksum(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), GRB.MINIMIZE) model.optimize() print "Optimal value:", model.ObjVal EPS = 1.e-6 for (i,j) in x: if x[i,j].X > EPS: print "sending quantity %10s from factory %3s to customer %3s" % (x[i,j].X,j,i)