Multi-Objective Variable Neighborhood Search Algorithm to Optimize the Multi-Product U-Shaped Disassembly Line Balancing Problem with a Fixed Number of Stations

To improve the disassembly efficiency of a U-shaped disassembly line and reduce the potentially harmful effects on the environment and human health, we study the multi-product U-shaped disassembly line balancing problem with a fixed number of stations (MUDLBPF). Firstly, we formulate a mathematical model aimed at minimizing cycle time, balancing loads, and reducing hazard indicators. Secondly, a multi-objective variable neighborhood search (MOVNS) algorithm is proposed. A multi-segment encoding method is proposed to maintain the independence of different products. Considering the characteristics of multiple products, a two-stage decoding method is presented. The method includes product assignment and task assignment. To optimize decoding efficiency, a minimum deviation method is put forward to generate feasible solutions. A segmented neighborhood structure containing seven operators is developed to improve the search efficiency. Finally, numerical experiments are performed and the results show that the MOVNS can solve the MUDLBPF effectively and efficiently.