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searches.py
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searches.py
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import time
import random
import Data_Structures as ds
csv_format = "{},{},{},{},{},{},{},{},{}\n"
def assign_box_to_new_bin(box, current_problem, not_placed_boxes, optimized=False):
new_sbp = ds.SingleBinProblem(current_problem.bin)
new_sbp.add_boxes(box)
new_sbp.fillBin(optimized=optimized)
new_not_placed_boxes = [b for b in not_placed_boxes[1:]]
new_bins = [sbp.__copy__() for sbp in current_problem.M]
new_p = ds.PalletizationModel(current_problem.bin, new_not_placed_boxes, new_bins + [new_sbp])
new_p.try_to_close(len(new_p.M) - 1, optimized=optimized)
return new_p
def assign_box_to_bin(box, current_problem, i, not_placed_boxes, optimal=True, nodes=5000, optimized=False):
new_p = current_problem.__copy__()
new_not_placed_boxes = [b for b in not_placed_boxes[1:]]
new_p.boxList = new_not_placed_boxes
new_sbp = new_p.M[i]
new_sbp.add_boxes(box)
if optimal:
h2_result = ds.H2(new_sbp.boxList, new_p.bin, optimized=optimized)
if len(h2_result) == 1:
new_p.M[i] = h2_result[0]
return new_p, []
single_bin_result = new_sbp.fillBin(optimized=optimized)
return new_p, single_bin_result
else:
new_sbp.max_nodes = 5000
new_sbp.m_cut = True
new_sbp.m = 4
single_bin_result = new_sbp.fillBin(optimized=optimized)
return new_p, single_bin_result
def backtracking_condition(current_problem, i, box):
if current_problem.M[i].open:
l2 = current_problem.get_l2_bound(current_problem.M[i].boxList + [box])
if not l2 >= 2:
return True
return False
def assign_box_to_bin_min_max(box, current_problem, i, not_placed_boxes):
new_p = current_problem.__copy__()
new_not_placed_boxes = [b for b in not_placed_boxes[1:]]
new_p.boxList = new_not_placed_boxes
new_sbp = new_p.M[i]
new_sbp.add_boxes(box)
single_bin_result = new_sbp.fillBin(optimized=True)
return new_p, single_bin_result
def check_min_bound_feasibility(problem, not_placed_boxes):
lower_violation = problem.get_lower_violations()
items = problem.minDict.keys()
for item in items:
remaining_items = len([b for b in not_placed_boxes if b.itemName == item])
for sb in lower_violation:
items_placed = len([b for b in sb.placement_best_solution if b.itemName == item])
if items_placed < problem.minDict[item]:
to_add = problem.minDict[item] - items_placed
remaining_items -= to_add
if remaining_items < 0:
return False
return True
def insert_lower_bound(lower_bounds, box_list):
bs = ds.BoxSet(box_list)
lower_bounds.append(bs)
#print "dimensione lower bound:" + str(len(lower_bounds))
def check_lower_bound(lower_bounds, box_list):
if ds.BoxSet(box_list) in lower_bounds:
print "match lower bound"
return False
return True
def add_optimal_solution(optimal_solutions, placement_best_solution):
bs = ds.BoxSet(placement_best_solution)
bs.add_placement(placement_best_solution)
optimal_solutions.append(bs)
#print "dimensione delle soluzioni ottime:" + str(len(optimal_solutions))
def get_soluzione_ottima(bs, optimal_solutions):
for opt in optimal_solutions:
if bs.__eq__(opt):
return opt
return None
def assign_box_to_bin_v2(box, current_problem, i, not_placed_boxes, optimal_solutions, lower_bounds, optimal=True, nodes=5000, optimized=True):
to_place = [box] + current_problem.M[i].boxList
bs = ds.BoxSet(to_place)
optimal_placement = get_soluzione_ottima(bs, optimal_solutions)
if optimal_placement is None:
new_p = current_problem.__copy__()
new_not_placed_boxes = [b for b in not_placed_boxes[1:]]
new_p.boxList = new_not_placed_boxes
new_sbp = new_p.M[i]
new_sbp.add_boxes(box)
h2_result = ds.H2(new_sbp.boxList, new_p.bin, optimized=optimized)
if len(h2_result) == 1:
new_p.M[i] = h2_result[0]
add_optimal_solution(optimal_solutions, h2_result[0].placement_best_solution)
return new_p, []
#print "sto usando fill bin"
if not optimal:
new_sbp.max_nodes = nodes
new_sbp.m_cut = True
new_sbp.m = 1
single_bin_result = new_sbp.fillBin(optimized=optimized)
if single_bin_result == []:
add_optimal_solution(optimal_solutions, new_sbp.placement_best_solution)
else:
insert_lower_bound(lower_bounds, current_problem.M[i].boxList + single_bin_result)
return new_p, single_bin_result
else:
new_p = current_problem.__copy__()
new_not_placed_boxes = [b for b in not_placed_boxes[1:]]
new_p.boxList = new_not_placed_boxes
new_sbp = new_p.M[i]
new_sbp.boxList = optimal_placement.placement
new_sbp.placement_best_solution = optimal_placement.placement
#print "soluzione ottima riutilizzata"
return new_p, []
class IDSearchMinMaxConstraints:
def __init__(self, first_problem, optimal=True):
self.first_problem = first_problem
self.optimal = optimal
if not optimal:
random.shuffle(first_problem.boxList)
else:
self.first_problem.boxList = sorted(self.first_problem.boxList, key=lambda box: box.get_volume(), reverse=True)
self.max_depth = self.first_problem.get_l2_bound(self.first_problem.boxList)
self.lower_bounds = []
self.optimal_solutions = []
self.max_nodes = int(len(self.first_problem.boxList) * 2)
self.node_count = 0
def inizialize_problem_depth(self):
problem_copy = self.first_problem.__copy__()
problem_copy.boxList = [box for box in self.first_problem.boxList]
for i in range(int(self.max_depth)):
problem_copy.M.append(ds.SingleBinProblem(problem_copy.bin))
min_constr = problem_copy.fill_min_bin()
self.node_count = 0
return problem_copy, min_constr
def initialize_min_constraints(self):
problem, min_constr = self.inizialize_problem_depth()
return min_constr, problem
def search_id(self):
f, problem = self.initialize_min_constraints()
if not f:
return 'fail'
res = self.backtracking_search_optimized_id_min_max(problem)
while res == 'fail':
self.max_depth += 1
self.max_nodes = self.max_nodes * 2
f, problem = self.initialize_min_constraints()
if not f:
return 'fail'
res = self.backtracking_search_optimized_id_min_max(problem)
if self.max_depth >= len(self.first_problem.boxList):
return 'fail'
return res
def search_id_multi(self, index, risultato):
f, problem = self.initialize_min_constraints()
if not f:
risultato[index] = 'fail'
res = self.backtracking_search_optimized_id_min_max(problem)
while res == 'fail':
#print "aumento"
self.max_depth += 1
self.max_nodes = self.max_nodes * 2
f, problem = self.initialize_min_constraints()
if not f:
risultato[index] = 'fail'
return
res = self.backtracking_search_optimized_id_min_max(problem)
if self.max_depth >= len(self.first_problem.boxList):
risultato[index] = 'fail'
return
risultato[index] = res
def backtracking_search_optimized_id_min_max(self, current_problem):
if self.node_count < self.max_nodes or self.optimal:
not_placed_boxes = current_problem.boxList
if not_placed_boxes == []:
if current_problem.check_item_count():
return current_problem
else:
return "fail"
else:
box = not_placed_boxes[0]
for i in range(len(current_problem.M)):
if self.node_count < self.max_nodes or self.optimal \
and backtracking_condition(current_problem, i, box) \
and current_problem.check_item_upper():
new_p, single_bin_result = assign_box_to_bin(box,
current_problem,
i,
not_placed_boxes,
nodes=self.max_nodes,
optimal=self.optimal,
optimized=True)
if single_bin_result == []:
#self.node_count += 1
print self.node_count
result = self.backtracking_search_optimized_id_min_max(new_p)
if result != "fail":
return result
return "fail"
else:
return "fail"