Open AccessFlowshop scheduling with machine deterioration based on job sequences
Author(s) -
Alex J. RuizTorres,
Jose Ablanedo Rosas,
Daniel Jurburg
Publication year - 2021
Publication title -
ingeniería y competitividad revista científica y tecnológica/ingeniería y competitividad
Language(s) - English
Resource type - Journals
eISSN - 2027-8284
pISSN - 0123-3033
DOI - 10.25100/iyc.v23i2.10099
Subject(s) - tardiness , job shop scheduling , heuristics , computer science , mathematical optimization , scheduling (production processes) , permutation (music) , heuristic , flow shop scheduling , process (computing) , baseline (sea) , selection (genetic algorithm) , set (abstract data type) , machine learning , artificial intelligence , mathematics , schedule , physics , oceanography , acoustics , programming language , geology , operating system
This paper addresses the two-machine permutation flowshop problem with deterioration. The objectives are minimizing the makespan and the average tardiness. Jobs have a baseline process time in each machine and have a due date. The actual time to process a job depends on the machine performance level at the start of each job, which is a function of the previously processed jobs and their wear/deterioration effect on the machine. The article proposes multiple heuristics and a comprehensive set of experiments. The results indicate that as a group, the heuristics generate solutions that are very close to the optimal for both criteria. Furthermore, no heuristic approach is dominant for all experimental conditions, thus heuristic selection to solve practical problems should be based on the specific problem characteristics.