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首页> 外文期刊>Journal of Intelligent Manufacturing >On the end-of-life state oriented multi-objective disassembly line balancing problem
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On the end-of-life state oriented multi-objective disassembly line balancing problem

机译:关于寿命终端的国家的多目标拆卸线平衡问题

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摘要

The biggest difference between a disassembly line and an assembly line is that there are many uncertainties in structure and quality of the disassembled products in a disassembly line. The disassembly line balancing problem, considering the effect of end-of-life states caused by the uncertainty of the structure or the quality of the disassembled products, is addressed in this paper. A multi-objective mathematical model for the addressed problem is built with three optimization goals: minimizing the number of workstations, minimizing the idle index and minimizing the number of resources. Then a multi-objective hybrid migrating birds optimization algorithm is proposed, which uses a greedy random search operation based on embedding mechanism to generate neighborhood individuals. To avoid the problem of easily being trapped into a local optimum by a basic migrating birds optimization algorithm, a reset mechanism based on simulated annealing operation is set up to accept other solutions with a certain probability, so that the algorithm can escape out of a local optimum. By solving disassembly examples of different scales in the literature and comparing with the existing algorithms, the effectiveness and superiority of the proposed multi-objective hybrid migrating birds optimization algorithm is validated. Finally, the proposed model and algorithm are applied to solving two disassembly instances, and the solving results are compared with the single-objective optimal solution solved by LINGO 11.0 solver and the basic migrating birds optimization algorithm to further identify the performance of the proposed algorithm.
机译:拆卸线和装配线之间的最大区别是在拆卸线中的拆卸产品的结构和质量存在许多不确定性。在本文中,考虑到由结构的不确定性或拆卸产品的质量引起的寿命状态的效果,拆卸线路平衡问题。解决问题的多目标数学模型采用三个优化目标构建:最大限度地减少工作站数量,最大限度地减少空闲索引并最大限度地减少资源数量。然后提出了一种多目标混合迁移鸟类优化算法,其使用基于嵌入机制来生成邻域个体的贪婪随机搜索操作。为避免通过基本迁移鸟类优化算法容易被困到本地最佳的问题,建立了一种基于模拟退火操作的复位机制,以接受具有某种概率的其他解决方案,使得该算法可以从本地逃脱最佳。通过求解文献中不同尺度的拆卸示例并与现有算法进行比较,验证了所提出的多目标混合迁移鸟类优化算法的有效性和优势。最后,将所提出的模型和算法应用于解决两个拆卸实例,并将求解结果与通过Lingo 11.0解算器和基本迁移鸟类优化算法进行的单目标最佳解决方案进行比较,以进一步识别所提出的算法的性能。

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