A novel MILP model and an improved genetic algorithm for disassembly line balancing and sequence planning with partial destructive mode

Collaborative optimization of disassembly line balancing and disassembly sequence planning can effectively reduce ineffective operation time. Considering the uncertainty of the connection relationship between parts of end-of-life products, a partial destructive disassembly mode based on the disassembly feasibility is introduced. In this study, a mixed integer linear programming (MILP) model of disassembly line balancing and sequence planning (DLBSP) is constructed. The aim is to minimize the number of stations, smoothness index, and energy consumption, and maximize the disassembly profit simultaneously. To obtain high-quality disassembly schemes, a multi-objective improved genetic algorithm is proposed. An encoding and decoding strategy based on problem characteristics is designed to improve the quality of the initial solutions. The feasibility of crossover and mutation operations is ensured by introducing precedence constraints, and a single-point insertion neighborhood strategy is designed to improve the local search ability of the algorithm. Then, the effectiveness of the proposed model and algorithm is verified in a small-scale case, and the performance of the proposed algorithm is better than that of nine meta-heuristics through 22 test cases. Next, the proposed model and algorithm are applied to a real CRT TV disassembly case. It is shown that the new disassembly scheme can reduce two stations, improve smoothness by 99.83%, increase profits by 89.75%, and reduce energy consumption by 24.66% compared to the original scheme. Finally, a DLBSP optimization system is developed, which can reduce the application difficulty of the proposed model and algorithm.