Industrial products’ reuse, recovery, and recycling are very important due to the exhaustion of ecological resources. Effective product disassembly planning methods can improve the recovery efficiency and reduce harmful impact on the environment. However, the existing approaches pay little attention to disassembly resources, such as tools and operators that can significantly influence the optimal disassembly sequences. This article considers a multiobjective resource-constrained disassembly optimization problem modeled with timed Petri nets such that energy consumption is minimized, while disassembly profit is maximized. Since its solution complexity has exponential growth with the number of components in a product, a multiobjective genetic algorithm based on an external archive is used to solve it. Its effectiveness is verified by comparing it with nondominated sorting genetic algorithm II and a collaborative resource allocation strategy for a multiobjective evolutionary algorithm based on decomposition. Note to Practitioners—This article establishes a novel dual-objective optimization model for product disassembly subject to multiresource constraints. In an actual disassembly process, a decision-maker may want to minimize energy consumption and maximize disassembly profit. This article considers both objectives and proposes a multiobjective genetic algorithm based on an external archive to solve optimal disassembly problems. The experimental results show that the proposed approach can solve them effectively. The obtained solutions give decision-makers multiple choices to select the right disassembly process when an actual product is disassembled.

中文翻译：

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多资源约束下的选择性拆卸，利润最大化、能耗最小


由于生态资源的枯竭，工业产品的再利用、回收和循环利用变得非常重要。有效的产品拆解规划方法可以提高回收效率并减少对环境的有害影响。然而，现有的方法很少关注拆卸资源，例如可以显着影响最佳拆卸顺序的工具和操作员。本文考虑使用定时 Petri 网建模的多目标资源约束拆卸优化问题，从而最小化能耗，同时最大化拆卸利润。由于其求解复杂度随着产品中组件数量呈指数增长，因此采用基于外部档案的多目标遗传算法来求解。通过与非支配排序遗传算法II和基于分解的多目标进化算法协同资源分配策略的比较，验证了其有效性。从业者须知——本文建立了一种新颖的多资源约束下产品拆解双目标优化模型。在实际的拆解过程中，决策者可能希望最小化能源消耗并最大化拆解利润。本文考虑了这两个目标，提出了一种基于外部档案的多目标遗传算法来解决最优反汇编问题。实验结果表明所提出的方法能够有效地解决这些问题。所获得的解决方案为决策者在实际产品拆解时选择正确的拆解工艺提供了多种选择。