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
(Department of Industrial ans Systems Engineering, Tarbiat Modares University, Tehran, Iran)
Majid
Khalili
2
(Department of Industrial Engineering, Islamic Azad University Karaj Branch,Alborz,Iran)
Hamed
Tayebi
3
(Department of Industrial Engineering, Islamic Azad University Karaj Branch, Alborz, Iran)
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.
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