Lot streaming in workforce scheduling problem for seru production system under Shojinka philosophy

GÜRSOY YILMAZ B., YILMAZ Ö. F., Çevikcan E.

COMPUTERS & INDUSTRIAL ENGINEERING, cilt.185, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 185
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.cie.2023.109680
  • Dergi Adı: COMPUTERS & INDUSTRIAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, DIALNET, Civil Engineering Abstracts
  • Anahtar Kelimeler: Genetic algorithm, Lot streaming, Seru production system, Shojinka philosophy, Workforce scheduling problem
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Drawing upon efficiency, flexibility, and responsiveness, this study attempts to explore the impact of lot streaming and worker assignment strategies in the workforce scheduling problem for the seru production system (SPS), which has emerged as an alternative to traditional assembly lines. The addressed problem involves de-cisions related to employee timetabling, lot splitting, and sublot scheduling. While scheduling problems in SPS have been extensively researched in recent years, the workforce scheduling problem has not received much attention in the literature. Moreover, to our best knowledge, it has not been investigated with the sublot division methodologies within the context of SPS so far. To bridge this gap, a generic novel optimization model is developed with the objective of minimizing average flow time (AFT) by integrating lot streaming and the Shojinka philosophy through modules within the model. Additionally, the structural properties of the problem are examined, and lower and upper bound formulations are developed based on these properties. Given the NP -hard nature of the problem, customized approaches based on the genetic algorithm (GA) are proposed for solving large-sized problems, considering the Shojinka and division methodologies. The computational results clarify that achieving Shojinka with variable sublots division methodology significantly reduces the AFT objective. Furthermore, the findings confirm that adapting variable sublots in operational scenarios results in a drastic improvement in system performance, regardless of the implemented worker assignment strategy.