A Hybrid Unconscious Search Algorithm for Mixed-model Assembly Line Balancing Problem with SDST, Parallel Workstation and Learning Effect
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JOIE-13-2_009
تاریخ نمایه سازی: 26 شهریور 1399
Abstract:
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.
Keywords:
Unconscious Search algorithm , Assembly line balancing problem , Learning Effect , Parallel workstation , Sequence-dependent setup times
Authors
Moein Asadi-Zonouz
Department of Industrial ans Systems Engineering, Tarbiat Modares University, Tehran, Iran
Majid Khalili
Department of Industrial Engineering, Islamic Azad University Karaj Branch,Alborz,Iran
Hamed Tayebi
Department of Industrial Engineering, Islamic Azad University Karaj Branch, Alborz, Iran
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