A multiple rule-based genetic algorithm for cost-oriented stochastic assembly line balancing problem
ISSN: 0144-5154
Article publication date: 2 October 2018
Issue publication date: 16 April 2019
Abstract
Purpose
This research aims to address the cost-oriented stochastic assembly line balancing problem (ALBP) and propose a chance-constrained programming model.
Design/methodology/approach
The cost-oriented stochastic ALBP is solved for small- to medium-sized problems. Owing to the non-deterministic polynomial-time (NP)-hardness problem, a multiple rule-based genetic algorithm (GA) is proposed for large-scale problems.
Findings
The experimental results show that the proposed GA has superior performance and efficiency compared to the global optimum solutions obtained by the IBM ILOG CPLEX optimization software.
Originality/value
To the best of the authors’ knowledge, only one study has discussed the cost-oriented stochastic ALBP using the new concept of cost. Owing to the NP-hard nature of the problem, it was necessary to develop a heuristic or meta-heuristic algorithm for large data sets; this research paper contributes to filling this gap.
Keywords
Citation
Foroughi, A. and Gökçen, H. (2019), "A multiple rule-based genetic algorithm for cost-oriented stochastic assembly line balancing problem", Assembly Automation, Vol. 39 No. 1, pp. 124-139. https://doi.org/10.1108/AA-03-2018-050
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited