Abstract
For the realization of environmental protection and resource conservation, remanufacturing is of great significance. Disassembly is a key step in remanufacturing, the disassembly line system is the main scenario for product disassembly, usually consisting of multiple workstations, and has prolific productivity. The application of the robots in the disassembly line will eliminate various problems caused by manual disassembly. Moreover, the disassembly line balancing problem (DLBP) is of great importance for environmental remanufacturing. In the past, disassembly work was usually done manually with high cost and relatively low efficiency. Therefore, more and more researches are focusing on the automatic DLBP due to its high efficiency. This research solves the sequence-dependent robotic disassembly line balancing problem (SDRDLBP) with multiple objectives. It considers the sequence-dependent time increments and requires the generated feasible disassembly sequence to be assigned to ordered disassembly workstations according to the specific robotic workstation assignment method. In robotic DLBP, due to the special characteristics of robotic disassembly, we need to consider the moving time of the robots’ disassembly path during the disassembly process. This is also the first time to consider sequence-dependent time increments while considering the disassembly path of the robots. Then with the help of crossover and mutation operators, multi-objective evolutionary algorithms (MOEAs) are proposed to solve SDRDLBP. Based on the gear pump model, the performance of the used algorithm under different cycle times is analyzed and compared with another two algorithms. The average values of the HV and IGD indicators have been calculated, respectively. The results show the NSGA-II algorithm presents outstanding performance among the three MOEAs, and hence demonstrate the superiority of the NSGA-II algorithm.
Multi-Objective Ant Lion Optimizer for Stochastic Robotic Disassembly Line Balancing Problem Subject to Resource Constraints
