A differential evolution algorithm for the U-shaped assembly line balancing problem with hierarchical worker assignment
Engineering Computations (Swansea, Wales), ss.1-29, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1108/ec-07-2025-0713
- Dergi Adı: Engineering Computations (Swansea, Wales)
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Compendex, INSPEC, zbMATH, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Pharma Collection (ProQuest), Technology Collection (ProQuest)
- Sayfa Sayıları: ss.1-29
- Anahtar Kelimeler: Assembly line balancing, Differential evolution algorithm, Hierarchical worker assignment, U-shaped assembly lines
- Eskişehir Osmangazi Üniversitesi Adresli: Evet
Özet
Purpose – This study addresses the assembly line balancing problem with hierarchical worker assignment (ALBHW) in U-shaped assembly lines. Despite their practical relevance, U-shaped ALBHW problems have only been explored through exact solution approaches, which face serious limitations when dealing with large-scale instances. This study aims to address this gap by proposing an effective metaheuristic framework to minimise total labour cost while considering task-dependent processing times and hierarchical worker skill levels. Design/methodology/approach – A differential evolution algorithm (DEA), a population-based metaheuristic capable of generating high-quality solutions within reasonable computational time, is developed to solve the U-shaped ALBHW. The proposed approach extends the existing body of solution methods for this problem beyond exact optimisation techniques. The performance of the DEA is assessed using a set of 900 benchmark instances reported in the literature. Findings – Computational results demonstrate that the proposed DEA consistently produces high-quality solutions and significantly outperforms existing exact approaches, particularly as the problem size increases. Compared with exact methods, the DEA achieves labour cost reductions of approximately 7% for large-scale instances and 17% for very large-scale instances. Originality/value – This study introduces a metaheuristic solution framework for the U-shaped ALBHW, addressing a problem that has previously been handled exclusively through exact optimisation methods. By overcoming scalability limitations and validating the approach on an extensive set of benchmark instances, the study provides a substantive contribution to the skill-based line balancing literature.