A multiple rule-based genetic algorithm for cost-oriented stochastic assembly line balancing problem

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 po...

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Bibliographic Details
Published in:Assembly automation 2019-04, Vol.39 (1), p.124-139
Main Authors: Foroughi, Ahad, Gökçen, Hadi
Format: Article
Language:English
Subjects:
Algorithms
Assembly lines
Balancing
Capital costs
Genetic algorithms
Heuristic
Heuristic methods
Integer programming
Machinery
Manufacturing
Mathematical models
Optimization
Polynomials
Production costs
Production scheduling
Scientific papers
Wage rates
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Summary: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.
ISSN:0144-5154
2754-6969
1758-4078
2754-6977
DOI:10.1108/AA-03-2018-050