Optimising work-sharing in disassembly line balancing problems: a multi-cycle strategy

To improve workload balance and reduce cycle time in disassembly line balancing problems, a multi-cycle work-sharing strategy that allows tasks to be reassigned across sub-cycles is proposed for the first time. While this strategy enhances disassembly efficiency, it also increases total resource demands, as different tasks may require distinct resources for effective execution. Therefore, to effectively address this challenge, a bi-objective integer linear programming model is established with the goal of minimising both overall cycle time and total resource count. The epsilon constraint method is then employed to obtain solutions for small-scale problems. For large-scale problems, a Double Deep Q-Network-based Hyper-Heuristic (DDQN-HH) algorithm is developed. This approach incorporates specialised encoding and decoding strategies, two state functions, and eight heuristic action rules to enhance its effectiveness. And comparative experiments using multi-objective evaluation metrics demonstrate the DDQN-HH algorithm's superiority over three other leading algorithms: the Deep Q-Network-based Hyper Heuristic (DQN-HH) algorithm, the Random, Greedy-based Hyper Heuristic (RG-HH) algorithm, and the Multi-objective Equilibrium Optimiser (MOEO) algorithm. Furthermore, the DDQN-HH's practical significance in addressing real-world engineering problems is validated through managerial applications.