Multi-objective disassembly line design and optimisation considering energy efficiency and human factors

Green manufacturing and remanufacturing revolve around recycling end-of-life (EOL) products. One crucial aspect of this process is the disassembly line balancing problem (DLBP), which involves assigning disassembly tasks to workstations in a logical order. However, current DLBP models often overlook operator-related factors and energy efficiency. This paper introduces a multi-objective DLBP model that considers both operator and energy factors. The model aims to minimise disassembly line idle rate, improve line smoothness, reduce energy consumption, and balance operator workload under multiple constraints. Since this problem is NP-hard, we also develop a hybrid intelligent algorithm that combines simulated annealing and the water cycle algorithm (SWCA) to solve DLBP effectively using real-world case disassembly. For multi-objective optimisation problems, choosing multiple non-inferior solutions can be challenging. In this work, we embrace an 'optimisation before decision-making' approach, integrating multi-criteria decision-making techniques with neural networks optimised by a particle swarm optimisation (PSO) algorithm to identify the most satisfactory solution.