A Hybrid Unconscious Search Algorithm for Mixed-model Assembly Line Balancing Problem with SDST, Parallel Workstation and Learning Effect

Journal of Optimization in Industrial Engineering

مقاله 9، دوره 13، شماره 2 - شماره پیاپی 28، اسفند 2020، صفحه 123-140   

نوع مقاله: Original Manuscript

شناسه دیجیتال (DOI): 10.22094/joie.2020.579974.1605

نویسندگان

Moein Asadi-Zonouz¹؛ Majid Khalili^{* 2}؛ Hamed Tayebi³

¹Department of Industrial ans Systems Engineering, Tarbiat Modares University, Tehran, Iran

²Department of Industrial Engineering, Islamic Azad University Karaj Branch,Alborz,Iran

³Department of Industrial Engineering, Islamic Azad University Karaj Branch, Alborz, Iran

چکیده

Due to the variety of products, simultaneous production of different models has an important role in production systems. Moreover, considering the realistic constraints in designing production lines attracted a lot of attentions in recent researches. Since the assembly line balancing problem is NP-hard, efficient methods are needed to solve this kind of problems. In this study, a new hybrid method based on unconscious search algorithm (USGA) is proposed to solve mixed-model assembly line balancing problem considering some realistic conditions such as parallel workstation, zoning constraints, sequence dependent setup times and learning effect. This method is a modified version of the unconscious search algorithm which applies the operators of genetic algorithm as the local search step. Performance of the proposed algorithm is tested on a set of test problems and compared with GA and ACOGA. The experimental results indicate that USGA outperforms GA and ACOGA.

کلیدواژه‌ها

Unconscious Search algorithm؛ Assembly line balancing problem؛ Learning Effect؛ Parallel workstation؛ Sequence-dependent setup times

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