This research aims to address the cost-oriented stochastic assembly line balancing problem (ALBP) and propose a chance-constrained programming model.
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.
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.
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.
