Genetic algorithm based approaches for solving hybrid flowshop scheduling problem under production leveling based lot streaming strategy
Flexible Services and Manufacturing Journal, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s10696-026-09690-5
- Dergi Adı: Flexible Services and Manufacturing Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Applied Science & Technology Source, Compendex, INSPEC, zbMATH, Business Source Ultimate (EBSCO), Materials Science & Engineering Collection (ProQuest), Pharma Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Genetic algorithm, Hybrid flowshop scheduling, Lot streaming, Mixed integer linear programming, Production leveling
- Kocaeli Üniversitesi Adresli: Evet
Özet
Lot streaming strategy enables to divide jobs into smaller lots and scheduling them in order to diminish production time and deliver products more quickly in multi-stage manufacturing systems. Production leveling (heijunka) indicates determination of the size and order of sublots belonging to products for a time period and it ensures to obtain a production pattern relevant to the addressed period. In the Just In Time production environment, it is aimed to reach a balanced production schedule eliminating wastes, increasing flexibility, and minimizing workload differences among workstations by means of this concept. In this paper, it has been aimed to determine an optimal production pattern for each of the unrelated parallel machines under a production leveling based lot streaming strategy by considering also sequence-independent setup times, machine eligibility, stage skipping, and work shift constraints. In this context, initially, a mixed integer linear programming model including all constraints of the addressed hybrid flowshop scheduling problem has been developed and a numerical example has been presented for a better explanation. Since it is not possible to obtain a solution in a reasonable time as the problem size increases for this problem, four genetic algorithm based meta-heuristic approaches differ according to encoding and decoding structures have been proposed. Some significant parameters utilized in these meta-heuristic approaches have been calibrated through the Taguchi method for small, medium, and large-sized instances separately. Finally, the experimental analysis results obtained using randomly generated test instances have been presented to evaluate the performance of the mathematical model and the proposed meta-heuristic approaches.