Heuristic based genetic algorithms for the re-entrant total completion time flowshop scheduling with learning consideration
- DOI
- 10.1080/18756891.2016.1256572How to use a DOI?
- Keywords
- re-entrant flowshop; learning effect; heuristic-based genetic algorithm
- Abstract
Recently, both the learning effect scheduling and re-entrant scheduling have received more attention separately in research community. However, the learning effect concept has not been introduced into re-entrant scheduling in the environment setting. To fill this research gap, we investigate re-entrant permutation flowshop scheduling with a position-based learning effect to minimize the total completion time. Because the same problem without learning or re-entrant has been proved NP-hard, we thus develop some heuristics and a genetic algorithm (GA) to search for approximate solutions. To solve this problem, we first adopt four existed heuristics for the problem; we then apply the same four methods combined with three local searches to solve the proposed problem; in the last stage we develop a heuristic-based genetic algorithm seeded with four good different initials obtained from the second stage for finding a good quality of solutions. Finally, we conduct experimental tests to evaluate the behaviours of all the proposed algorithms when the number of re-entrant times or machine number or learning effect or job size changes.
- Copyright
- © 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
Download article (PDF)
View full text (HTML)
Cite this article
TY - JOUR AU - Jianyou Xu AU - Win-Chin Lin AU - Junjie Wu AU - Shuenn-Ren Cheng AU - Zi-Ling Wang AU - Chin-Chia Wu* PY - 2016 DA - 2016/12/01 TI - Heuristic based genetic algorithms for the re-entrant total completion time flowshop scheduling with learning consideration JO - International Journal of Computational Intelligence Systems SP - 1082 EP - 1100 VL - 9 IS - 6 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2016.1256572 DO - 10.1080/18756891.2016.1256572 ID - Xu2016 ER -