def fgh_heuristic(frame: JobSchedulingFrame, count_alpha: int = 1) -> list:
init_jobs = [idx_job for idx_job in range(frame.count_jobs)]
sum_times = [
frame.get_sum_processing_time(idx_job) for idx_job in init_jobs
]
tetta = sum(sum_times) / frame.count_jobs
time_min = min(sum_times)
time_max = max(sum_times)
alpha_min = time_min / (time_min + tetta)
alpha_max = time_max / (time_max + tetta)
period = (alpha_max - alpha_min) / count_alpha
solutions = []
for i in range(count_alpha):
alpha = alpha_max - period * i
init_jobs.sort(
key=lambda idx_job: fgh_index(sum_times[idx_job], alpha, tetta),
reverse=True)
solutions.append(copy(init_jobs))
for i in range(count_alpha):
solutions[i], _ = local_search_partitial_sequence(frame, solutions[i])
solutions.sort(key=lambda solution: compute_end_time(frame, solution))
return solutions[0]
def neh_heuristics(frame: JobSchedulingFrame) -> list:
"""
Compute approximate solution for instance of Flow Job problem by
NEH heuristic.
Parameters
----------
frame: JobSchedulingFrame
Returns
-------
solution: list
sequence of job index
Notes
-----
Journal Paper:
Nawaz,M., Enscore,Jr.E.E, and Ham,I. (1983) A Heuristics Algorithm for
the m Machine, n Job Flowshop Sequencing Problem.
Omega-International Journal of Management Science 11(1), 91-95
"""
count_jobs = frame.count_jobs
all_processing_times = [0] * count_jobs
for j in range(count_jobs):
all_processing_times[j] = frame.get_sum_processing_time(j)
init_jobs = [j for j in range(count_jobs)]
init_jobs.sort(key=lambda x: all_processing_times[x], reverse=True)
solution, _ = local_search_partitial_sequence(frame, init_jobs)
return solution