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bpsolver.py
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bpsolver.py
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# -*- coding: UTF-8 -*-
##########################################################################
# Copyright or © or Copr. Michaël Gabay (2013)
#
# michael [dot] gabay [at] g-scop.grenoble-inp.fr
#
# This software is a computer program whose purpose is to be
# a proof of concept on using game theoretical approaches to prove
# lower bounds on online packing and scheduling problems.
#
# This software is governed by the CeCILL license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify and/ or redistribute the software under the terms of the CeCILL
# license as circulated by CEA, CNRS and INRIA at the following URL
# "http://www.cecill.info".
#
# As a counterpart to the access to the source code and rights to copy,
# modify and redistribute granted by the license, users are provided only
# with a limited warranty and the software's author, the holder of the
# economic rights, and the successive licensors have only limited
# liability.
#
# In this respect, the user's attention is drawn to the risks associated
# with loading, using, modifying and/or developing or reproducing the
# software by the user in light of its specific status of free software,
# that may mean that it is complicated to manipulate, and that also
# therefore means that it is reserved for developers and experienced
# professionals having in-depth computer knowledge. Users are therefore
# encouraged to load and test the software's suitability as regards their
# requirements in conditions enabling the security of their systems and/or
# data to be ensured and, more generally, to use and operate it in the
# same conditions as regards security.
#
# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL license and that you accept its terms.
##########################################################################
"""
Solver to the bin packing problem, using integer programming
"""
import os
import sys
# Required for pypy to locate py4j and pulp
sys.path.extend(['/usr/local/lib/python2.7/dist-packages/py4j-0.8-py2.7.egg', '/usr/local/lib/python2.7/dist-packages', '/usr/lib/python2.7/dist-packages'])
import random
import binascii
from bins import *
from heuristic import *
################## Bin Packing modeling ####################
# Memorize problem solved
mem = {}
def init_solver(solver, lib):
if solver == "CHOCO" or solver == "CP":
global jpype
import jpype
elif solver == "CHOCO4J" or solver == "CP4J":
global JavaGateway, GatewayClient
from py4j.java_gateway import JavaGateway, GatewayClient
from subprocess import Popen
import time
global p4jproc
args = ['java', '-jar', lib+'/solver.jar']
p4jproc = Popen(args)
time.sleep(1) # wait a second to make sure the server is really open
else:
#import yaposib
plp = __import__('pulp',globals(),locals())
globals().update(plp.__dict__)
def terminate_solver(solver):
if solver == "CHOCO4J" or solver == "CP4J":
p4jproc.kill()
def make_key(items, num_bins, capacity):
d = {}
for i in items:
s = i.size
if s in d: d[s] += 1
else: d[s] = 1
l = []
for i, j in d.iteritems():
l.append(i)
l.append(j)
# MEMORY: if num_bins and capacity never change, spare some memory
# by removing the following 2 lines:
l.append(num_bins)
l.append(capacity)
return binascii.rlecode_hqx(' '.join(str(i) for i in l))
#return tuple(l)
calls = 0
def is_feasible(items, num_bins, capacity, solver="GLPK"):
ret, res = is_trivial(items, num_bins, capacity)
if ret:
return res
global mem
t = make_key(items, num_bins, capacity)
if t in mem:
return mem[t]
ret, res = heuristics(items, num_bins, capacity)
if ret:
sol = res
mem[t] = sol
return sol
"""
Current implemented bound is not interesting enough
m = compute_min_bins(items, capacity)
if m > num_bins:
sol = False
mem[t] = sol
return sol
else:
"""
global calls
calls += 1
sys.stdout.write("\rCP calls:\t%d" %calls)
sys.stdout.flush()
if solver == "CHOCO" or solver == "CP":
sol = CPSolve(items, num_bins, capacity)
elif solver == "CHOCO4J" or solver == "CP4J":
sol = py4j_solve(items, num_bins, capacity)
else:
mod = make_model(items, num_bins, capacity)
sol = solve(mod, solver)
#sol = grb_solve(items, num_bins, capacity)
#assert grb_solve(items, num_bins, capacity) == sol
mem[t] = sol
return sol
def make_model(items, num_bins, capacity):
ritems = xrange(len(items))
rbins = xrange(num_bins)
prob = LpProblem("Bin Packing Feasibility",LpMinimize)
prob += 0, "No objective: feasibility problem"
var = {(i,j): LpVariable("x("+str(i)+","+str(j)+")",0,1,LpBinary)\
for i in ritems for j in rbins}
# Big item assignments can be fixed from the beginning
items.sort(reverse=True)
cur = 0
count = 0
half = 0
for i in items:
if 2*i.size < capacity:
break
prob += var[(count,cur)] == 1,\
"Fixing item "+str(count)+" to bin "+str(cur)
if 2*i.size == capacity and half == 0:
half = 1
else:
half = 0
cur += 1
count += 1
# If there were no big item, we can still fix the first one
if count == 0:
prob += var[(0,0)] == 1, "First item symmetry breaking"
for i in ritems:
prob += lpSum(var[(i,j)] for j in rbins) == 1,\
"Assign item "+str(i)
"""
# Symmetry breaking constraints of assignement
prev = -1
for i in ritems:
if items[i].size != prev:
prev = items[i].size
continue
for j in rbins:
prob += lpSum(var[(i-1,k)] for k in xrange(j+1)) >= var[(i,j)],\
"Breaking symmetry, "+str((i,j))
"""
for j in rbins:
prob +=\
lpSum(var[(i, j)]*items[i].size for i in ritems) <= capacity,\
"Capacity constraint for bin "+str(j)
return prob
def is_trivial(items, num_bins, capacity):
# Assumes that all items are smaller than the capacity
"""
for i in items:
if i.size > capacity:
return True, False
"""
if len(items) <= num_bins:
return True, True
# compute sum of items and try first-fit
sm = 0
cur = capacity
idx = 0
for i in items:
s = i.size
sm += s
if cur >= s: cur -= s
else:
cur = capacity - s
idx += 1
if idx < num_bins:
return True, True
if sm > num_bins*capacity:
return True, False
return False, True
def heuristics(items, num_bins, capacity):
# First-Fit Increasing
items.sort(reverse=True)
if (len(items) <= num_bins): # defensive: verified in is_trivial
return True, True
if (items[num_bins-1].size + items[num_bins].size > capacity):
return True, False
big = 0
half = 0
idx = 0
cur = capacity
for i in items:
s = i.size
if 2*s > capacity: big += 1
elif 2*s == capacity: half += 1
if cur >= s: cur -= s
else:
cur = capacity - s
idx += 1
if idx < num_bins:
return True, True
if 2*big + half > 2*num_bins:
return True, False
# BFD
#items.sort(reverse=True)
tmp_bins = bin_factory(num_bins, capacity)
if first_fit(items, tmp_bins):
return True, True
# Randomized best fit
#for i in xrange(max(2,capacity-8)):
# clean_bins(tmp_bins)
# random.shuffle(items)
# if first_fit(items, tmp_bins):
# return True, True
return False, True
def solve(model, solver="GLPK"):
if solver == "CPLEX":
model.solve(CPLEX(msg=0))
elif solver == "GUROBI":
model.solve(GUROBI(msg=0))
elif solver == "COIN":
model.solve(COIN(msg=0))
elif solver == "CBC":
model.solve(PULP_CBC_CMD(msg=0))
else:
#model.solve(YAPOSIB(msg=0,warning=0))
model.solve(GLPK(msg=0))
if model.status == 1:
return True
return False
def grb_solve(items, num_bins, capacity):
ritems = xrange(len(items))
rbins = xrange(num_bins)
# Model
m = Model("Bin Packing Feasibility")
# dvar item is in/out
var = {(i,j): m.addVar(name="x(%s,%s)" % (i,j),vtype=GRB.BINARY)\
for i in ritems for j in rbins}
# Update model to integrate new variables
m.update()
# Objective
#m.setObjective(0)
# Big item assignments can be fixed from the beginning
items.sort(reverse=True)
cur = 0
count = 0
half = 0
for i in items:
if 2*i.size < capacity:
break
m.addConstr(var[(count,cur)] == 1,\
"Fixing item "+str(count)+" to bin "+str(cur))
if 2*i.size == capacity and half == 0:
half = 1
else:
half = 0
cur += 1
count += 1
# If there were no big item, we can still fix the first one
if count == 0:
m.addConstr(var[(0,0)] == 1, "First item symmetry breaking")
# Capacity constraint
for i in ritems:
m.addConstr(quicksum(var[(i,j)] for j in rbins) == 1,\
"Assign item %s" % i)
for j in rbins:
m.addConstr(
quicksum(var[(i, j)]*items[i].size for i in ritems) <= capacity,\
"Capacity constraint for bin %s" %j)
# Solve
m.setParam('OutputFlag', 0)
m.optimize()
return m.status == GRB.status.OPTIMAL
def CPSolve(items, num_bins, capacity):
ClassSolver = jpype.JClass("solver.BPSolver")
items = [i.size for i in items]
bp = ClassSolver(items, num_bins, capacity)
return bp.isFeasible()
def run_jvm(jvmpath, jarpath):
classpath = ''
for files in os.listdir(jarpath):
if files.endswith(".jar"):
classpath += ':'+jarpath+files
classpath = classpath[1:]
cpath = '-Djava.class.path=%s' % classpath
jpype.startJVM(jvmpath, cpath)
# Unnecessary
def close_jvm():
jpype.shutdownJVM()
gateway = None
def py4j_run():
global gateway
gateway = JavaGateway()
def py4j_solve(items, num_bins, capacity):
global gateway
if not gateway: py4j_run()
int_class = gateway.jvm.int
jitems = gateway.new_array(int_class,len(items))
for i, it in enumerate(items):
jitems[i] = it.size
solver = stack = gateway.entry_point.getSolver()
solver.reset(jitems, num_bins, capacity)
return solver.isFeasible()
################## Example ####################
def main():
it = [Item(i+1) for i in xrange(4)]
m = make_model(it, 2, 4)
assert not solve(m)
#assert not solve(m, "COIN")
#assert not solve(m, "GUROBI")
#assert not solve(m, "CPLEX")
m = make_model(it, 2, 5)
assert solve(m)
#assert solve(m, "COIN")
#assert solve(m, "GUROBI")
#assert solve(m, "CPLEX")
m = make_model(it, 3, 4)
assert solve(m)
#assert solve(m, "COIN")
#assert solve(m, "GUROBI")
#assert solve(m, "CPLEX")
print "Dummy tests passed"
if __name__ == "__main__":
main()