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

Subject Areas : 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

Received: 2018-11-27 Accepted : 2020-06-01 Published : 2020-12-01

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

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.