Assembly line rebalancing and worker assignment considering ergonomic risks in an automotive parts manufacturing plant

Cimen T., BAYKASOĞLU A., Akyol Ş.

INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS, cilt.13, sa.3, ss.363-384, 2022 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 3
  • Basım Tarihi: 2022
  • Doi Numarası: 10.5267/j.ijiec.2022.2.001
  • Dergi Adı: INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.363-384
  • Anahtar Kelimeler: Assembly line rebalancing problem, Worker assignment, Occupational repetitive action, Ergonomic risk assessment, Randomized constructive rule-based heuristic, RE-BALANCING PROBLEM, MULTIOBJECTIVE GENETIC ALGORITHM, HEURISTIC ALGORITHM, MODEL, RECONFIGURATION, EXPOSURE
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

This paper recommends a new kind of assembly line rebalancing and worker assignment problem, taking ergonomic risks into account. Assembly line rebalancing problem (ALRBP) occurs when a current line must be rebalanced due to conditions such as changes in demand, production processes, product design, or quality issues. Although there are several research attempts on ALRBP in the relevant literature, only a few studies consider workers as unique individuals. This paper aims to solve the double reassignment problem: tasks to workers and workers to stations, considering ergonomic risk factors. This paper is the first study that comprises worker assignment and ergonomic constraints in ALRBP literature to the best of our knowledge. Objectives of our novel problem are to minimize rebalancing cost, which includes transportation of tasks and workers and minimize stations' ergonomic risk factors. A randomized constructive rule-based heuristic approach is developed to cope with the problem. The proposed solution approach is applied to benchmark data, and obtained results are promising. Moreover, the proposed solution approach is implemented in an automotive parts manufacturing plant. (c) 2022 by the authors; licensee Growing Science, Canada