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

محورهای موضوعی : Strategic Management

Moein Asadi-Zonouz ¹ , Majid Khalili ² , Hamed Tayebi ³

1 - Department of Industrial ans Systems Engineering, Tarbiat Modares University, Tehran, Iran
2 - Department of Industrial Engineering, Islamic Azad University Karaj Branch,Alborz,Iran
3 - Department of Industrial Engineering, Islamic Azad University Karaj Branch, Alborz, Iran

تاریخ دریافت : 1397/09/06 تاریخ پذیرش : 1399/03/12 تاریخ انتشار : 1399/09/11

کلید واژه: Assembly line balancing problem, Unconscious Search algorithm, Learning Effect, Parallel workstation, Sequence-dependent setup times,

چکیده مقاله :

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.

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