Many-objective best-order-sort genetic algorithm for mixed-model multi-robotic disassembly line balancing

Abstract Disassembly is a crucial step of the end-of-life product recovery with growing environmental concerns. Improving the disassembly process helps to raise the resource utilization rate and to reduce the environmental pollution. Robotic disassembly line is considered to be one of the most efficient and eco-friendly disassembly systems. In this article, the mixed-model multi-robotic disassembly line balancing problem is addressed with the objectives of optimizing the number of robots, number of workstations, total load density, hazardous task cost, and CO2 saving rate. A mathematical programming formulation is proposed to represent the problem based on the transformed AND/OR graph of product. Since the addressed problem is NP-hard, a many-objective best-order-sort genetic algorithm, including a particular encoding/decoding procedure, two genetic operators, a neighborhood search operator, and the best-order-sort mechanism, has been developed. The performance of the proposed algorithm is compared with three state-of-the-art evolutionary algorithms. Computational results show that the proposed algorithm can be considered as an efficient solution method for solving this problem.