def opt_ETI(memoBT, memo_ET, memo_ETI, et_global_solution, upper_bound, jobs, last, problem):
total_cplex_time = 0
if memo_ETI[last].eti_penalty >= 0:
eti_penalty = memo_ETI[last].eti_penalty
if memo_ETI[last].end_times[last] >= upper_bound:
eti_penalty = utils.BIG_NUMBER
return list(memo_ETI[last].block_lasts), list(
memo_ETI[last].end_times), eti_penalty, total_cplex_time
block_lasts = list()
end_times = list()
et_slt = et.opt_ET(memo_ET, et_global_solution, jobs, problem,
last)
if et_slt.head_last == last: # if the optimal schedule of ET problem has only one block
block_start = et_slt.tail_start
eti_penalty = et_slt.et_penalty
block_lasts.append(last)
t = block_start
for j in range(last + 1):
t = t + problem.processing_times[jobs[j]]
end_times.append(t)
else:
block_lasts, end_times, eti_penalty, cplex_time = dp(memoBT, memo_ET, memo_ETI, et_global_solution,
et_slt.head_last, et_slt.tail_first, jobs, last, problem)
total_cplex_time += cplex_time
memo_ETI[last].block_lasts = list(block_lasts)
memo_ETI[last].end_times = list(end_times)
memo_ETI[last].eti_penalty = eti_penalty
if end_times[last] >= upper_bound:
eti_penalty = utils.BIG_NUMBER
return block_lasts, end_times, eti_penalty, total_cplex_time
def opt_ETI_Bounded(memoBT, memo_ET, memo_ETI_bounded, et_global_solution,
upper_bound, idle_bound, jobs, last, problem):
if memo_ETI_bounded[last].eti_penalty >= 0:
eti_penalty = memo_ETI_bounded[last].eti_penalty
if memo_ETI_bounded[last].end_times[last] >= upper_bound:
eti_penalty = utils.BIG_NUMBER
elif memo_ETI_bounded[last].num_idle > idle_bound:
eti_penalty = utils.BIG_NUMBER
return list(memo_ETI_bounded[last].block_lasts), list(
memo_ETI_bounded[last].end_times), eti_penalty, 0
block_lasts = list()
end_times = list()
if idle_bound == 0:
start_UB, end_UB, et_penalty_UB, cplex_time = bt.time_block(
memoBT, jobs, 0, last, problem)
block_lasts.append(last)
t = start_UB
for i in range(last + 1):
t += problem.processing_times[jobs[i]]
end_times.append(t)
return block_lasts, end_times, et_penalty_UB, 0
et_slt = et.opt_ET(memo_ET, et_global_solution, jobs, problem, last)
# new lemma 5
if et_slt.tail_end > upper_bound:
et_penalty_boundary, _ = et.opt_ET_with_boundary(
jobs, 0, last, problem, upper_bound)
delta_et = et_penalty_boundary - et_slt.et_penalty
if et_slt.num_idle * problem.b < delta_et:
return block_lasts, end_times, utils.BIG_NUMBER, 0
if et_slt.tail_first <= et_global_solution.block_lasts[0]:
block_start, end_UB, et_penalty_UB, cplex_time = bt.time_block(
memoBT, jobs, 0, last, problem)
eti_penalty = et_penalty_UB
block_lasts.append(last)
t = block_start
for j in range(last + 1):
t = t + problem.processing_times[jobs[j]]
end_times.append(t)
else:
start_UB, end_UB, et_penalty_UB, cplex_time = bt.time_block(
memoBT, jobs, 0, last, problem)
idle_bound = min(
idle_bound, et_slt.num_idle,
math.floor(
round(et_penalty_UB - et_slt.et_penalty, 4) / problem.b))
block_lasts, end_times, eti_penalty, cplex_time = dp_Bounded(
memoBT, memo_ET, memo_ETI_bounded, et_global_solution,
et_slt.head_last, et_slt.tail_first, jobs, last, idle_bound,
problem)
num_idle = len(block_lasts) - 1
memo_ETI_bounded[last].block_lasts = list(block_lasts)
memo_ETI_bounded[last].end_times = list(end_times)
memo_ETI_bounded[last].eti_penalty = eti_penalty
memo_ETI_bounded[last].num_idle = num_idle
if num_idle > idle_bound:
eti_penalty = utils.BIG_NUMBER
if end_times[last] >= upper_bound:
eti_penalty = utils.BIG_NUMBER
return block_lasts, end_times, eti_penalty, 0
