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test_pulp.py
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test_pulp.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pulp import *
from random import *
from math import ceil
from pyomo.environ import *
from pyomo.opt import SolverFactory
def schedule_pulp():
at = [randint(0, 100) for i in xrange(10)]
num = len(at)
size = [randint(5, 30) for i in xrange(num)]
wt = [randint(1, 6) for i in xrange(num)]
idx = [str(i) for i in xrange(num)]
sst = [0 for i in xrange(num)]
st = LpVariable.dicts("st", idx, 0, 100, cat="Integer")
prob = LpProblem("test", LpMinimize)
x = [st[i]+size[int(i)]/10-at[int(i)] for i in idx if st[i]+size[int(i)]/10-at[int(i)]>0]
prob += lpSum([st[i]+size[int(i)]/10-at[int(i)] for i in idx if st[i]+size[int(i)]/10-at[int(i)] > 0])
for i in idx:
prob += st[i]+size[int(i)]/10-at[int(i)] <= wt[int(i)]
prob_status = prob.solve()
print LpStatus[prob_status]
for i in xrange(num):
sst[i] = int(ceil(value(st[str(i)])))
print prob.objective
print value(prob.objective)
print at
print size
print wt
print sst
def schedule_pyomo():
num = 10
at = [randint(0, 100) for i in xrange(num)]
length = [randint(2, 5) for i in xrange(num)]
wt = [randint(1, 6) for i in xrange(num)]
model = AbstractModel("schedule")
model.n = Param(default=num)
model.i = RangeSet(0, model.n-1)
model.st = Var(model.i, domain=NonNegativeIntegers, bounds=(0, model.n-1))
et = [model.st[i]+length[i] for i in xrange(num)]
lt = [max(et[i]-at[i], 0) for i in xrange(num)]
# def obj_rule(model, length, at, num):
def obj_rule(model):
# a = [[length[model.rank[x]] for x in xrange(i)] for i in xrange(num)]
# st = [sum(a[i]) for i in xrange(num)]
# et = [st[i]+length[i] for i in xrange(num)]
# lt = [max(et[i]-at[i], 0) for i in xrange(num)]
# return lt
return sum([max((model.st[i]+length[i]-at[i]), 0) for i in xrange(10)])
model.obj = Objective(rule=obj_rule, sense=minimize)
def c1_rule(model, j):
# a = [[length[model.rank[x]] for x in xrange(i)] for i in xrange(num)]
# st = [sum(a[i]) for i in xrange(num)]
return lt[j] - wt[j] <= 0
model.c1 = Constraint(model.i, rule=c1_rule)
opt = SolverFactory("glpk")
instance = model.create()
result = opt.solve(instance)
instance.load(result)
print result.solution[0].status
if __name__ == '__main__':
# schedule_pulp()
schedule_pyomo()