Multi-objective optimization of stochastic disassembly line balancing with station paralleling

•We focus on stochastic disassembly line balancing problem with station paralleling.•The problem is defined using an AND/OR Graph of the product to obtain disassembly tasks and precedence relations among them.•A genetic algorithm (GA) is proposed to obtain Pareto-optimal solutions.•Effectiveness is compared with goal programming (GP) and a heuristic approach (HA).•The results show that GA is an effective and efficient solution algorithm.

One of the major activities performed in product recovery is disassembly. Disassembly line is the most suitable setting to disassemble a product. Therefore, designing and balancing efficient disassembly systems are important to optimize the product recovery process. In this study, we deal with multi-objective optimization of a stochastic disassembly line balancing problem (DLBP) with station paralleling and propose a new genetic algorithm (GA) for solving this multi-objective optimization problem. The line balance and design costs objectives are simultaneously optimized by using an AND/OR Graph (AOG) of the product. The proposed GA is designed to generate Pareto-optimal solutions considering two different fitness evaluation approaches, repair algorithms and a diversification strategy. It is tested on 96 test problems that were generated using the benchmark problem generation scheme for problems defined on AOG as developed in literature. In addition, to validate the performance of the algorithm, a goal programming approach and a heuristic approach are presented and their results are compared with those obtained by using GA. Computational results show that GA can be considered as an effective and efficient solution algorithm for solving stochastic DLBP with station paralleling in terms of the solution quality and CPU time.