transp.py

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"""
Model for solving a transportation problem:
minimize the total transportation cost for satisfying demand at
customers, from capacitated facilities.
 
Copyright (c) by Joao Pedro PEDROSO and Mikio KUBO, 2012
"""
from pyscipopt import Model, quicksum, multidict
 
def transp(I,J,c,d,M):
    """transp -- model for solving the transportation problem
    Parameters:
        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
    Returns a model, ready to be solved.
    """
 
    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(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.addCons(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), "minimize")
 
    model.optimize()
 
    model.data = x
    return model
 
 
def make_inst1():
    """creates example data set 1"""
    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,
         }
    return I,J,c,d,M
 
 
def make_inst2():
    """creates example data set 2"""
    I,d = multidict({1:45, 2:20, 3:30 , 4:30}) # demand
    J,M = multidict({1:35, 2:50, 3:40})        # capacity
    c = {(1,1):8,    (1,2):9,    (1,3):14  ,   # {(customer,factory) : cost<float>}
         (2,1):6,    (2,2):12,   (2,3):9   ,
         (3,1):10,   (3,2):13,   (3,3):16  ,
         (4,1):9,    (4,2):7,    (4,3):5   ,
         }
    return I,J,c,d,M
 
 
if __name__ == "__main__":
    I,J,c,d,M = make_inst1();
    # I,J,c,d,M = make_inst2();
    model = transp(I,J,c,d,M)
    model.optimize()
 
    print("Optimal value:", model.getObjVal())
 
    EPS = 1.e-6
    x = model.data
 
    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))