mctransp.py

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"""
Model for solving the multi-commodity 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 mctransp(I,J,K,c,d,M):
    """mctransp -- model for solving the Multi-commodity Transportation Problem
    Parameters:
        - I: set of customers
        - J: set of facilities
        - K: set of commodities
        - c[i,j,k]: unit transportation cost on arc (i,j) for commodity k
        - d[i][k]: demand for commodity k at node i
        - M[j]: capacity
    Returns a model, ready to be solved.
    """
 
    model = Model("multi-commodity transportation")
 
    # Create variables
    x = {}
 
    for (i,j,k) in c:
        x[i,j,k] = model.addVar(vtype="C", name="x(%s,%s,%s)" % (i,j,k))
 
    # Demand constraints
    for i in I:
        for k in K:
            model.addCons(sum(x[i,j,k] for j in J if (i,j,k) in x) == d[i,k], "Demand(%s,%s)" % (i,k))
 
    # Capacity constraints
    for j in J:
        model.addCons(sum(x[i,j,k] for (i,j2,k) in x if j2 == j) <= M[j], "Capacity(%s)" % j)
 
    # Objective
    model.setObjective(quicksum(c[i,j,k]*x[i,j,k]  for (i,j,k) in x), "minimize")
 
    model.data = x
 
    return model
 
 
def make_inst1():
    """creates example data set 1"""
    d = {(1,1):80,   (1,2):85,   (1,3):300,  (1,4):6, # {(customer,commodity):demand}}
         (2,1):270,  (2,2):160,  (2,3):400,  (2,4):7,
         (3,1):250,  (3,2):130,  (3,3):350,  (3,4):4,
         (4,1):160,  (4,2):60,   (4,3):200,  (4,4):3,
         (5,1):180,  (5,2):40,   (5,3):150,  (5,4):5
         }
    I = set([i for (i,k) in d])
    K = set([k for (i,k) in d])
    J,M = multidict({1:3000, 2:3000, 3:3000})  # capacity
 
    produce = {1:[2,4], 2:[1,2,3], 3:[2,3,4]}  # products that can be produced in each facility
    weight = {1:5, 2:2, 3:3, 4:4}              # {commodity: weight}
    cost = {(1,1):4,  (1,2):6, (1,3):9,        # {(customer,factory): 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
            }
    c = {}
    for i in I:
        for j in J:
            for k in produce[j]:
                c[i,j,k] = cost[i,j] * weight[k]
 
    return I,J,K,c,d,M
 
def make_inst2():
    """creates example data set 2"""
    d = {(1,1):45,                             # {(customer,commodity):demand}}
         (2,1):20,
         (3,1):30,
         (4,1):30,
         }
    I = set([i for (i,k) in d])
    K = set([k for (i,k) in d])
    J,M = multidict({1:35, 2:50, 3:40})       # {factory: capacity}}
    produce = {1:[1], 2:[1], 3:[1]}           # products that can be produced in each facility
    weight = {1:1}                            # {commodity: weight}
    cost = {(1,1):8,    (1,2):9,    (1,3):14, # {(customer,factory): cost}
            (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 ,
            }
    c = {}
    for i in I:
        for j in J:
            for k in produce[j]:
                c[i,j,k] = cost[i,j] * weight[k]
 
    return I,J,K,c,d,M
 
 
def make_inst3():
    """creates example data set 3"""
    d = {(1,1):40,   (1,2):30,   (1,3):10,  # {(customer,commodity):demand}}
         (2,1):70,   (2,2):100,  (2,3):100,
         (3,1):0,    (3,2):0,    (3,3):250,
         (4,1):60,   (4,2):100,  (4,3):0,
         (5,1):180,  (5,2):0,    (5,3):0
         }
    I = set([i for (i,k) in d])
    K = set([k for (i,k) in d])
    J,M = multidict({1:500, 2:500, 3:500})  # capacity
 
    produce = {1:[2,4], 2:[1,2,3], 3:[2,3,4]}  # products that can be produced in each facility
    weight = {1:5, 2:2, 3:3, 4:4}              # {commodity: weight}
    cost = {(1,1):4,  (1,2):6, (1,3):9,        # {(customer,factory): 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
            }
    c = {}
    for i in I:
        for j in J:
            for k in produce[j]:
                c[i,j,k] = cost[i,j] * weight[k]
 
    return I,J,K,c,d,M
 
 
if __name__ == "__main__":
    I,J,K,c,d,M = make_inst3();
    model = mctransp(I,J,K,c,d,M)
    model.writeProblem("transp.lp")
    model.optimize()
 
    print("Optimal value:",model.getObjVal())
 
    EPS = 1.e-6
    x = model.data
 
    for (i,j,k) in x:
        if model.getVal(x[i,j,k]) > EPS:
            print("sending %10s units of %3s from plant %3s to customer %3s" % (model.getVal(x[i,j,k]),k,j,i))