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

机译:用于解决随机多目标拆卸测序和线路平衡问题的多目标离散灰狼优化器

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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.
机译:回收植物越来越担心,以使拆卸寿命终生产品的负面环境影响(如碳排放)最小化。在拆卸它们时,由他们不同的使用阶段引起的不确定性。在本文中,我们提出了基于和/或图的随机多目标拆卸测序和线路平衡问题。通过考虑拆卸失败风险,我们构建了最大化利润和最小化碳排放和能源消耗的目标,以帮助维持经济发展。然后,我们提出了一种新颖的多目标离散灰狼优化器来解决它。我们通过产品示例展示了其有效性。结果显示了基于分解的经典非主导分类遗传算法II和多目标进化算法的提出算法的优越性。

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