Multi-objective Discrete Grey Wolf Optimizer for Solving Stochastic Multi-objective Disassembly Sequencing and Line Balancing Problem

Zhiwei Zhang, Xiwang Guo, Mengchu Zhou, Shixin Liu, Liang Qi
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引用次数: 10

Abstract

There is a growing concern in recycling plants for minimizing the negative environmental impacts (such as carbon emissions) of disassembling end-of-life products. Uncertainty caused by their different usage stages exists when disassembling them. In this paper, we propose a stochastic multi-objective disassembly sequencing and line balancing problem based on an AND/OR graph. By considering disassembly failure risk, we construct objectives of maximizing profit and minimizing carbon emission and energy consumption to help sustain economic development. Then, we propose a novel multi-objective discrete grey wolf optimizer to solve it. We show its effectiveness via a product example. The results show the superiority of the proposed algorithm over classical non-dominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition.
求解随机多目标拆解排序与线路平衡问题的多目标离散灰狼优化算法
人们越来越关注回收工厂,以尽量减少拆卸报废产品对环境的负面影响(如碳排放)。拆解时,由于使用阶段不同,存在不确定性。本文提出了一个基于and /OR图的随机多目标拆解排序和生产线平衡问题。通过考虑拆解失效风险,构建利润最大化、碳排放和能耗最小化的目标,帮助经济持续发展。然后,我们提出了一种新的多目标离散灰狼优化器来解决这个问题。我们通过一个产品实例来证明它的有效性。结果表明,该算法优于经典的非支配排序遗传算法II和基于分解的多目标进化算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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