%0 Journal Article
%T A Hybrid Unconscious Search Algorithm for Mixed-model Assembly Line Balancing Problem with SDST, Parallel Workstation and Learning Effect
%J Journal of Optimization in Industrial Engineering
%I QIAU
%Z 2251-9904
%A Asadi-Zonouz, Moein
%A Khalili, Majid
%A Tayebi, Hamed
%D 2020
%\ 07/01/2020
%V 13
%N 2
%P 123-140
%! A Hybrid Unconscious Search Algorithm for Mixed-model Assembly Line Balancing Problem with SDST, Parallel Workstation and Learning Effect
%K Unconscious Search algorithm
%K Assembly line balancing problem
%K Learning Effect
%K Parallel workstation
%K Sequence-dependent setup times
%R 10.22094/joie.2020.579974.1605
%X 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.
%U http://www.qjie.ir/article_673184_95c806367c043a740eefb116eaf2b1bf.pdf