- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
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| Citation: | ZHANG Zeqiang, JIANG Jin, YIN Tao, XU Peiyu. Modeling and Optimization for U-shaped Partial Multi-Objective Disassembly Line Balancing Problem[J]. Journal of Southwest Jiaotong University, 2022, 57(2): 235-244. doi: 10.3969/j.issn.0258-2724.20200694
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Aiming at the advantages of U-shaped layout such as high production efficiency and strong flexibility, combined with the actual disassembly process that only needs to consider the required parts and hazardous parts, the U-shaped partial disassembly line balancing problem (UPDLBP) is proposed, and multi-objective mathematics model is established with the optimization objectives of minimizing the number of workstations, idle time balance indicators, disassembly depth and disassembly costs. On this basis, adaptive opposition-based learning multi-objective wolfpack algorithm (AOBL-MWPA) is proposed for solution calculation. The algorithm adopts adaptive scouting behavior and takes into account the global optimization performance in the early stage of the algorithm iteration and the stability in the later stage; The calling behavior and besieging behavior are discretized under the premise of satisfying the constraints of the priority relationship; Opposition-based learning strategy (OBLS) is used to avoid the algorithm from falling into the local optimum; Pareto solution set idea and crowding distance mechanism of non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) are given to screen to obtain multiple non-inferior solutions. The proposed algorithm is applied to 19 benchmark examples and compared with existing literature algorithms. Finally, the proposed model and algorithm are applied to the example design of a U-shaped partial disassembly line of a certain automobile. The results show that, the proposed algorithm can solve the optimal value of small-scale problems in terms of the number of workstations on and the idle time balance index. The results obtained in medium and large-scale problems are better than other algorithms; the optimal value can be obtained for both the hazard index and the demand index, and the optimization rate is 100%. Ten sets of optional design schemes are obtained from the case study, which verifies the practicability and effectiveness of the proposed algorithm.
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