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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:采用多目标元启发式算法自动拆卸系统方案选择和恢复路由分配的联合决策

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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:由于其重大的环境效益和潜在的恢复收益,对废物电气和电子设备的绿色处理越来越受到关注。自动拆卸已被视为强大的解决方案,以实现更有效的恢复操作。虽然许多研究有助于拆卸问题,但很少有研究专注于在自动拆卸中选择拆卸系统方案和恢复路线的决策模型。在本文中,我们提出了一个两相联合决策模型,以解决这个问题,以解决与环境影响的拆卸利润平衡。首先,我们建立一个多目标优化模型,以获得每个自动拆卸系统方案的帕累托最佳恢复路线。对多站拆卸系统进行评估恢复利润和能耗。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,解决了提出的模型。然后,我们使用模糊集方法比较所有系统方案的Pareto最佳解决方案并识别最佳方案。最后,我们对不同废物电表的自动拆卸进行了实际案例研究。结果展示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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