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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Bibliographic Details
Published in:Computers & operations research Vol. 82; pp. 102 - 113
Main Authors: Tang, Qiuhua, Li, Zixiang, Zhang, LiPing, Zhang, Chaoyong
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.06.2017
Pergamon Press Inc
Subjects:
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
Online Access:Get full text
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Summary:•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.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2017.01.015