An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors - A case study

MARICHELVAM M. K., GEETHA M., TOSUN Ö.

COMPUTERS & OPERATIONS RESEARCH, cilt.114, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 114
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.cor.2019.104812
  • Dergi Adı: COMPUTERS & OPERATIONS RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Hybrid flowshop, NP-hard, Makespan, Flow time, Particle swarm optimization, Variable neighbourhood search, SHOP, MACHINE, 2-STAGE, MAKESPAN, FLOWTIME
  • Akdeniz Üniversitesi Adresli: Evet

Özet

This paper addresses the multi-stage hybrid flowshop scheduling problem with identical parallel machines at each stage by considering the effect of human factors. The various levels of labours and the effects of their learning and forgetting are studied. The minimization of the weighted sum of the makespan and total flow time is the objective function. Since the problem is NP-hard, an improved version of the particle swarm optimization (PSO) algorithm is presented to solve the problem. A dispatching rule and a constructive heuristic are incorporated to improve the initial solutions of the PSO algorithm. The variable neighbourhood search (VNS) algorithm is also hybridized with the PSO algorithm to attain the optimal solutions consuming less computational time. An industrial scheduling problem of an automobile manufacturing unit is discussed. Moreover, several instances of the random benchmark problem are used to validate the performance of the proposed algorithm. Computational experiments have been performed and the results prove the effectiveness of the proposed approach. (C) 2019 Elsevier Ltd. All rights reserved.