Multi-objective Fuzzy Partial Disassembly Line Balancing Considering Preventive Maintenance Scenarios Using Enhanced Hybrid Artificial Bee Colony Algorithm

In the existing researches about partial disassembly line balancing problem (PDLBP), all workstations are assumed always be available. In the real world, however, aging and failure of machines are inevitable, which may result in the shutdown of the whole disassembly line. To solve this problem, this study introduces preventive maintenance scenarios into PDLBP to prevent unexpected breakdowns and promote the productivity and smoothness of the disassembly line. Then, considering the substantial level of uncertainty in the state of end-of-life products, a multi-objective fuzzy mathematical model is established to minimize the cycle time, disassembly profit, and total assignment plan alteration simultaneously. And an enhanced hybrid artificial bee colony algorithm is developed to solve it. The attribute-driven heuristic strategy is applied to improve the quality of initial food sources, and the adaptive neighborhood search is designed to improve the exploitation efficiency of the employed bees and onlooker bees. Additionally, a hybrid global learning strategy is developed to guide the scout bees to raise the diversity of food sources. Finally, three case studies are conducted to validate the proposed algorithm. The results indicate that the proposed algorithm can achieve superior performance.