multiobj.py
#!/usr/bin/python
# Copyright 2016, Gurobi Optimization, Inc.
# Want to cover three different sets but subject to a common budget of
# elements allowed to be used. However, the sets have different priorities to
# be covered; and we tackle this by using multi-objective optimization.
from __future__ import print_function
from gurobipy import *
try:
# Sample data
Groundset = range(20)
Subsets = range(4)
Budget = 12;
Set = [ [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ],
[ 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 ],
[ 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0 ],
[ 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0 ] ]
SetObjPriority = [ 3, 2, 2, 1]
SetObjWeight = [1.0, 0.25, 1.25, 1.0]
# Create initial model
model = Model('multiobj')
# Initialize decision variables for ground set:
# x[e] == 1 if element e is chosen for the covering.
Elem = model.addVars(Groundset, vtype=GRB.BINARY, name='El')
# Constraint: limit total number of elements to be picked to be at most
# Budget
model.addConstr(Elem.sum() <= Budget, name='Budget')
# Set global sense for ALL objectives
model.ModelSense = GRB.MAXIMIZE
# Limit how many solutions to collect
model.setParam(GRB.Param.PoolSolutions, 100)
# Set number of objectives
model.NumObj = 4
# Set and configure i-th objective
for i in Subsets:
model.setParam(GRB.Param.ObjNumber, i)
model.ObjNPriority = SetObjPriority[i]
model.ObjNWeight = SetObjWeight[i]
model.ObjNName = 'Set' + str(i)
model.ObjNRelTol = 0.01
model.ObjNAbsTol = 1.0 + i
model.setAttr(GRB.Attr.ObjN, Elem, Set[i])
# Save problem
model.write('multiobj.lp')
# Optimize
model.optimize()
model.setParam(GRB.Param.OutputFlag, 0)
# Status checking
status = model.Status
if status == GRB.Status.INF_OR_UNBD or \
status == GRB.Status.INFEASIBLE or \
status == GRB.Status.UNBOUNDED:
print('The model cannot be solved because it is infeasible or unbounded')
sys.exit(1)
if status != GRB.Status.OPTIMAL:
print('Optimization was stopped with status ' + str(status))
sys.exit(1)
# Print best selected set
print('Selected elements in best solution:')
for e in Groundset:
if Elem[e].X > 0.9:
print(' El%d' % e, end='')
print('')
# Print number of solutions stored
nSolutions = model.SolCount
print('Number of solutions found: ' + str(nSolutions))
# Print objective values of solutions
if nSolutions > 10:
nSolutions = 10
print('Objective values for first ' + str(nSolutions) + ' solutions:')
for i in Subsets:
model.setParam(GRB.Param.ObjNumber, i)
print('\tSet%d' % i, end='')
for e in range(nSolutions):
model.setParam(GRB.Param.SolutionNumber, e)
print(' %6g' % model.ObjNVal, end='')
print('')
except GurobiError as e:
print('Error code ' + str(e.errno) + ": " + str(e))
except AttributeError as e:
print('Encountered an attribute error: ' + str(e))
