A Multiple Rule-Based Genetic Algorithm for Cost-Oriented Stochastic Assembly Line Balancing Problem

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. © 2018, Emerald Publishing Limited.