Constraint programming approach for multi-resource-constrained unrelated parallel machine scheduling problem with sequence-dependent setup times

Yunusoglu P., Yildiz Ş. A.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, cilt.60, sa.7, ss.2212-2229, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 60 Sayı: 7
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1080/00207543.2021.1885068
  • Dergi Adı: INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.2212-2229
  • Anahtar Kelimeler: Unrelated parallel machines, scheduling, multiple resources, constraint programming, branching strategies, GENETIC ALGORITHM
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

This paper studies the multi-resource-constrained unrelated parallel machine scheduling problem under various operational constraints with the objective of minimising maximum completion time among the scheduled jobs. Sequence-dependent setup times, precedence relations, machine eligibility restrictions and release dates are incorporated into the problem as operational constraints to reflect real-world manufacturing environments. The considered problem is in NP-hard class of problems, which cannot be solved in deterministic polynomial time. Our aim in this study is to develop an exact solution approach based on constraint programming (CP), which shows good performance in solving scheduling problems. In this regard, we propose a CP model and enrich this model by adding lower bound restrictions and redundant constraints. Moreover, to achieve a reduction in computation time, we propose two branching strategies for the proposed CP model. The performance of the CP model is tested using randomly generated and benchmark instances from the literature. The computational results indicate that the proposed CP model outperforms the best solutions with an average gap of 15.52%.