def CP_model_restricted(params):
##
NUM_COLORS, NUM_ITEMS, BIN_SIZE, DISCREPANCY, ITEM_SIZES, COLORS, NUM_BINS, Timelimit, SEED = params
mdl = CpoModel()
X = [mdl.integer_var(min=0, max=NUM_BINS) for item in range(NUM_ITEMS)]
CP = mdl.integer_var_list(NUM_BINS, min=0, max=BIN_SIZE)
for bin in range(NUM_BINS - 1):
mdl.add(CP[bin] >= CP[bin + 1])
mdl.add(pack(CP, [X[item] for item in range(NUM_ITEMS)], ITEM_SIZES))
CC = [
mdl.integer_var_list(NUM_COLORS, min=0, max=BIN_SIZE)
for bin in range(NUM_BINS)
]
for color in range(NUM_COLORS):
mdl.add(
distribute([CC[bin][color] for bin in range(NUM_BINS)], [
X[item] for item in range(NUM_ITEMS) if COLORS[item] == color
], [bin for bin in range(NUM_BINS)]))
for bin in range(NUM_BINS):
TC = mdl.sum(CC[bin])
MC = mdl.max(CC[bin])
# mdl.add_kpi(TC, name="TC_{}".format(bin))
# mdl.add_kpi(MC, name="MC_{}".format(bin))
# mdl.add(
# (TC+mdl.mod(TC, 2))/2 >= MC
#
# )
# mdl.add(
# (TC - mdl.mod(TC, NUM_COLORS))/NUM_COLORS >= MC
# )
# mdl.add(
# mdl.sum(
# CC[bin][color] > 0 for color in range(NUM_COLORS)
# ) <= MAX_COLORS_IN_BIN
# )
for item in range(NUM_ITEMS):
for item2 in range(item + 1, NUM_ITEMS):
if COLORS[item] == COLORS[item2]:
mdl.add(
mdl.if_then(
X[item] == X[item2],
mdl.any([
X[i] == X[item]
for i in range(item + 1, item2 + 1)
if COLORS[i] != COLORS[item]
])
# mdl.count([mdl.element(COLORS, i) for i in range(item, item2+1)], abs(COLORS[item] -1)) > 0
))
# mdl.add(
# mdl.minimize(count_different(X))
# )
# bins_used = mdl.sum(CP[bin] > 0 for bin in range(NUM_BINS))
# mdl.add(
# mdl.minimize(
# bins_used
# )
# )
mdl.add(mdl.minimize(mdl.max(X) + 1))
try:
msol = mdl.solve(TimeLimit=20)
mdl._myInstance = (NUM_COLORS, NUM_ITEMS, BIN_SIZE, DISCREPANCY, SEED)
#
# print(ITEM_SIZES)
# print(COLORS)
#
# print([msol[X[item]] for item in range(NUM_ITEMS)])
Xs = np.array([msol[X[i]] for i in range(NUM_ITEMS)])
if solution_checker(Xs, COLORS, ITEM_SIZES, BIN_SIZE, SEED,
'restricted_cp_binary'):
write_to_global_cp(msol, mdl, 'restricted_binary')
except Exception as err:
print(err)
write_to_global_failed(NUM_COLORS,
NUM_ITEMS,
BIN_SIZE,
DISCREPANCY,
SEED,
'restricted_cp_binary',
is_bad_solution=False)
def CP_model_subsequence_restricted(params):
##
NUM_COLORS, NUM_ITEMS, BIN_SIZE, DISCREPANCY, ITEM_SIZES, COLORS, NUM_BINS, Timelimit, SEED = params
print("*** Running CP Subseq Restricted with instance NUM_COLORS{} NUM_ITEMS{} BIN_SIZE {} DISCREPANCY {} SEED {}".format(
NUM_COLORS, NUM_ITEMS, BIN_SIZE, DISCREPANCY, SEED))
mdl = CpoModel()
ITEMS = [mdl.interval_var(start=[0, sum(ITEM_SIZES)], size=ITEM_SIZES[item], optional=False, name="item_"+str(item)) for item in range(NUM_ITEMS)]
ITEMS_TO_BINS = [[mdl.interval_var(start=(0, BIN_SIZE-min(ITEM_SIZES)), end=(min(ITEM_SIZES), BIN_SIZE), size=ITEM_SIZES[item], optional=True, name="item{}InBin{}".format(item, bin)) for bin in range(NUM_BINS)] for item in range(NUM_ITEMS)]
ITEMS_S = mdl.sequence_var(ITEMS, types=COLORS, name="itemSequence")
BIN_S = [mdl.sequence_var([ITEMS_TO_BINS[item][bin] for item in range(NUM_ITEMS)], types=[COLORS[item] for item in range(NUM_ITEMS)], name="bin{}Sequence".format(bin)) for bin in range(NUM_BINS)]
# COLORS_S = [[mdl.sequence_var(ITEMS[item][bin] for item in range(NUM_ITEMS) if COLORS[item] == color)] for color in range(NUM_COLORS)]
BINS_USED = [mdl.binary_var() for bin in range(NUM_BINS)]
for item in range(NUM_ITEMS):
# for bin in range(NUM_BINS):
# mdl.add_kpi(
# mdl.presence_of(ITEMS_TO_BINS[item][bin]),
# "item_"+str(item)+" "+str(bin)
# )
mdl.add(
mdl.sum(
[mdl.presence_of(
ITEMS_TO_BINS[item][bin])
for bin in range(NUM_BINS)]
) == 1
)
# for item in range(NUM_ITEMS):
# mdl.add(
# mdl.alternative(ITEMS[item], [ITEMS_TO_BINS[item][bin] for bin in range(NUM_BINS)])
# )
for bin in range(NUM_BINS):
mdl.add(mdl.no_overlap(BIN_S[bin]))
for item in range(NUM_ITEMS-1):
mdl.add(
mdl.end_before_start(ITEMS[item], ITEMS[item+1])
)
mdl.add(mdl.no_overlap(ITEMS_S))
for bin in range(NUM_BINS):
mdl.add(
mdl.same_common_subsequence(
ITEMS_S,
BIN_S[bin],
)
)
for item in range(NUM_ITEMS):
for bin in range(NUM_BINS):
# if (item, bin) in [(17,5), (20, 5), (29, 5)]:
# mdl.add_kpi(mdl.type_of_next(BIN_S[bin], ITEMS_TO_BINS[item][bin], lastValue=-1, absentValue=-1), name="typeofnext{}_{}".format(item, bin))
#
# mdl.add_kpi(mdl.type_of_prev(BIN_S[bin], ITEMS_TO_BINS[item][bin], firstValue=-1, absentValue=-1), name="typeofprev{}_{}".format(item, bin))
mdl.add(
COLORS[item] != mdl.type_of_next(BIN_S[bin], ITEMS_TO_BINS[item][bin], lastValue=-1, absentValue=-1)
)
mdl.add(
COLORS[item] != mdl.type_of_prev(BIN_S[bin], ITEMS_TO_BINS[item][bin], firstValue=-1, absentValue=-1)
)
for bin in range(NUM_BINS):
mdl.add(
BINS_USED[bin] == mdl.any([
mdl.presence_of(ITEMS_TO_BINS[item][bin]) for item in range(NUM_ITEMS)
])
)
for bin in range(NUM_BINS-1):
mdl.add(
BINS_USED[bin] >= BINS_USED[bin+1]
)
mdl.add(
mdl.minimize(
mdl.sum(
BINS_USED
)
)
)
# mdl.add(
# mdl.sum(
# BINS_USED
# ) < 48
# )
try:
msol = mdl.solve(TimeLimit = Timelimit)
mdl._myInstance = (NUM_COLORS, NUM_ITEMS, BIN_SIZE, DISCREPANCY, SEED)
if msol:
ITEMS_TO_BINS_S = []
for bin in range(NUM_BINS):
b_ = []
for item in range(NUM_ITEMS):
s = msol[ITEMS_TO_BINS[item][bin]]
if s:
b_.append(s)
ITEMS_TO_BINS_S.append(b_)
# return mdl
seq = msol.get_var_solution(BIN_S[0])
print(ITEM_SIZES)
print("CL: ",COLORS)
Xs = []
for item in range(NUM_ITEMS):
for bin in range(NUM_BINS):
s = msol[ITEMS_TO_BINS[item][bin]]
if s:
# print(s)
Xs.append(bin)
print("Xs: ", Xs)
Xs = np.array(Xs)
if solution_checker(Xs, params, "restricted_cp_subsequence"):
write_to_global_cp(msol, mdl, 'restricted_subsequence')
except Exception as err:
print(err)
write_to_global_failed(params, 'restricted_cp_subsequence', is_bad_solution=False)
