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首页> 外文期刊>International Journal of Production Research >Joint decision-making on automated disassembly system scheme selection and recovery route assignment using multi-objective meta-heuristic algorithm
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Joint decision-making on automated disassembly system scheme selection and recovery route assignment using multi-objective meta-heuristic algorithm

机译：多目标元启发式算法的自动拆卸系统方案选择与恢复路径分配联合决策

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摘要

Green treatment on Waste Electrical and Electronic Equipmenthas increasingly attracted attention due to its significant environmental benefits and potential recovery earnings. Automated disassembly has been regarded as a powerful solution to enable more efficient recovery operations. Although numerous studies have contributed to the issues of disassembly, there are few researches that focus on decision model for selecting disassembly system scheme and recovery route in automated disassembly. In this paper, we propose a two-phase joint decision-making model to address this problem with the goal of balancing disassembly profit with environmental impact. First, we establish a multi-objective optimisation model to obtain the Pareto optimal recovery routes for each automated disassembly system scheme. Both recovery profit and energy consumption are evaluated for multi-station disassembly system. We design a multi-objective hybrid particle swarm optimisation algorithm based on symbiotic evolutionary mechanism to solve the proposed model. Then, we compare the Pareto optimal solutions of all the system schemes using a fuzzy set method and identify the best scheme. Finally, we conduct real case studies on the automated disassembly of different waste electric metres. The results demonstrate the superiority of automated disassembly and validate the effectiveness of our proposed model and algorithm.
机译：废弃电气电子设备的绿色处理由于其显着的环境效益和潜在的回收收益而日益受到关注。自动拆卸被视为一种强大的解决方案，可实现更高效的恢复操作。尽管有许多研究为拆卸问题做出了贡献，但很少有研究集中在决策模型上，这些决策模型用于选择自动拆卸中的拆卸系统方案和恢复路线。在本文中，我们提出了一个两阶段的联合决策模型来解决此问题，其目标是平衡拆卸利润与环境影响。首先，我们建立了一个多目标优化模型，以获得每种自动拆卸系统方案的帕累托最优回收路线。对于多工位拆卸系统，将评估回收利润和能耗。我们设计了一种基于共生进化机制的多目标混合粒子群优化算法来求解该模型。然后，我们使用模糊集方法比较所有系统方案的帕累托最优解，并确定最佳方案。最后，我们对不同废旧电表的自动拆卸进行了实际案例研究。结果证明了自动拆卸的优越性，并验证了我们提出的模型和算法的有效性。

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