Modeling and Solution of Multi-objective Parallel Disassembly Line Balancing Problem Considering Human Factors

Disassembly is of significant importance in maintenance, remanufacturing, and recycling, as it promotes sustainable development and resource utilization. However, due to the rapid advancement of production automation, designers often overlook the fact that manual operations offer greater flexibility and employee satisfaction during task execution, thereby impacting production efficiency and effectiveness. Therefore, considering human factors in disassembly line balancing problems holds significant research significance. In this work, we investigate the multi-objective parallel disassembly line balancing problem, taking into account factors such as worker fatigue. We establish a mathematical model that incorporates worker fatigue and employ the Pareto Envelope based Selection Algorithm II (PESA-II) for solving the proposed problem. Comparative analysis is conducted, comparing the results with other algorithms, including Pareto Archived Evolution Strategy (PESA), Carnivorous plant algorithm (CPA), Fruit Fly Optimization Algorithm (FOA), and Multivariate Singular Spectrum Analysis (MSSA), based on multiple sets of experimental cases to demonstrate the outstanding performance of PESA-II across various metrics.