Balancing stochastic two-sided assembly line with multiple constraints using hybrid teaching-learning-based optimization algorithm

•Stochastic two-sided assembly line balancing with multiple constraints is considered.•New priority-based decoding approach is developed to deal with multiple constraints.•Hybrid TLBO algorithm is developed by combing the TLBO, crossover operator and VNS.•Comparative evaluation of eleven algorithms...

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Vydáno v:	Computers & operations research Ročník 82; s. 102 - 113
Hlavní autoři:	Tang, Qiuhua, Li, Zixiang, Zhang, LiPing, Zhang, Chaoyong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Elsevier Ltd 01.06.2017 Pergamon Press Inc
Témata:	Assembly lines Multiple constraints Optimization algorithms Stochastic models Stochastic two-sided assembly line balancing Studies Task analysis Teaching-learning-based Optimization Variable neighborhood search Teaching-learning-based Optimization Stochastic two-sided assembly line balancing Multiple constraints Variable neighborhood search
ISSN:	0305-0548, 1873-765X, 0305-0548
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Shrnutí:	•Stochastic two-sided assembly line balancing with multiple constraints is considered.•New priority-based decoding approach is developed to deal with multiple constraints.•Hybrid TLBO algorithm is developed by combing the TLBO, crossover operator and VNS.•Comparative evaluation of eleven algorithms indicates the superiority of hybrid HTLBO. Two-sided assembly lines are usually found in the factories which produce large-sized products. In most literatures, the task times are assumed to be deterministic while these tasks may have varying operation times in real application, causing the reduction of performance or even the infeasibility of the schedule. Moreover, the ignorance of some specific constraints including positional constraints, zoning constraints and synchronism constraints will result in the invalidation of the schedule. To solve this stochastic two-sided assembly line balancing problem with multiple constraints, we propose a hybrid teaching-learning-based optimization (HTLBO) approach which combines both a novel teaching-learning-based optimization algorithm for global search and a variable neighborhood search with seven neighborhood operators for local search. Especially, a new priority-based decoding approach is developed to ensure that the selected tasks satisfy most of the constraints identified by multiple thresholds of the priority value and to reduce the idle times related to sequence-dependence among tasks. Experimental results on benchmark problems demonstrate both remarkable efficiency and universality of the developed decoding approach, and the comparison among 11 algorithms shows the effectiveness of the proposed HTLBO.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14
ISSN:	0305-0548 1873-765X 0305-0548
DOI:	10.1016/j.cor.2017.01.015