# using known values
s = pmo.sos([v1,v2], weights=[1.2,2.5])
assert s.weights == (1.2,2.5)
# using paramters
p = pmo.parameter_list(
pmo.parameter() for i in range(2))
s = pmo.sos([v1,v2], weights=[p[0]**2, p[1]**2])
assert len(s.weights) == 2
p[0].value = 1
p[1].value = 2
assert tuple(pmo.value(w) for w in s.weights) == (1, 4)
# using data expressions
d = pmo.expression_list(
pmo.data_expression() for i in range(2))
s = pmo.sos([v1,v2], weights=d)
assert len(s.weights) == 2
d[0].expr = p[0] + 1
d[1].expr = p[0] + p[1]
assert tuple(pmo.value(w) for w in s.weights) == (2, 3)
#
# Example model (discontiguous variable domain)
#
domain = [-1.1, 4.49, 8.1, -30.2, 12.5]
m = pmo.block()
m.z = pmo.variable_list(
for j in m.q:
m.cl.append(pmo.constraint(lb=-5, body=m.vl[j] - m.v, ub=5))
# @Constraints_list
# @Expressions_single
m.e = pmo.expression(-m.v)
# @Expressions_single
# @Expressions_dict
m.ed = pmo.expression_dict()
for i in m.s:
m.ed[i] = \
pmo.expression(-m.vd[i])
# @Expressions_dict
# @Expressions_list
# uses 0-based indexed
m.el = pmo.expression_list()
for j in m.q:
m.el.append(pmo.expression(-m.vl[j]))
# @Expressions_list
# @Objectives_single
m.o = pmo.objective(-m.v)
# @Objectives_single
# @Objectives_dict
m.od = pmo.objective_dict()
for i in m.s:
m.od[i] = \
pmo.objective(-m.vd[i])
# @Objectives_dict
# @Objectives_list
# uses 0-based indexing
model.con_worker = pmo.constraint_list()
for _worker in model.set_workers:
model.con_worker.append(
pmo.constraint(
sum(model.var_x[(_worker, _task)]
for _task in model.set_tasks) <= 1))
## Each task is assigned to exactly 1 worker
model.con_task = pmo.constraint_list()
for _task in model.set_tasks:
model.con_task.append(
pmo.constraint(
sum(model.var_x[(_worker, _task)]
for _worker in model.set_workers) == 1))
# Create the objective
expr = pmo.expression_list()
for _worker in model.set_workers:
for _task in model.set_tasks:
expr.append(
pmo.expression(model.param_cost[(_worker, _task)] *
model.var_x[(_worker, _task)]))
model.obj = pmo.objective(sum(expr), sense=pmo.minimize)
# Solve
opt = pmo.SolverFactory('cplex')
result = opt.solve(model)
# Print the solution
if result.solver.termination_condition == TerminationCondition.optimal or result.solver.status == SolverStatus.ok:
print('Total cost = ', pmo.value(model.obj), '\n')
for i in range(NUM_WORKERS):
# @Constraints_list
# @Expressions_single
m.e = pmo.expression(-m.v)
# @Expressions_single
# @Expressions_dict
m.ed = pmo.expression_dict()
for i in m.s:
m.ed[i] = \
pmo.expression(-m.vd[i])
# @Expressions_dict
# @Expressions_list
# uses 0-based indexed
m.el = pmo.expression_list()
for j in m.q:
m.el.append(
pmo.expression(-m.vl[j]))
# @Expressions_list
# @Objectives_single
m.o = pmo.objective(-m.v)
# @Objectives_single
# @Objectives_dict
m.od = pmo.objective_dict()
for i in m.s:
m.od[i] = \
pmo.objective(-m.vd[i])