The effective dismantling of discarded products regardless being used or not is critically important to their reuse, recovery, and recycling. However, the existing product disassembly planning methods pay little or no attention to resource constraints, e.g., limited numbers of disassembly operators and tools. Thus, a resulting plan when being executed may be ineffective in practice. This paper presents a dual-objective optimization model for selective disassembly sequences by considering multiresource constraints such that disassembly profit is maximized and time is minimized. A scatter search is adopted to solve the proposed dual-objective optimization model. It embodies the generation of diverse initial solutions, global assessment of objective functions, a crossover combination operator, a local search strategy for improved solutions, and a reference set update method. To analyze the effect of different weights on its performance, simulations are conducted on different products. Its effectiveness is verified by comparing its optimization results and those of genetic local search. Note to Practitioners—This work deals with a sequence modeling and planning problem of product disassembly. It establishes a novel dual-objective optimization model for product disassembly subject to multiresource constraints. Previously, such a problem is handled through a methodology based on the optimization of a single objective, i.e., disassembly time or cost. The resultant solution is insufficient without fully considering disassembly resources, e.g., labors and tools. Also, in an actual disassembly process, a decision-maker may want to maximize disassembly profit, as well as minimize disassembly time. This work considers both objectives and proposes scatter search to solve disassembly problems. The results demonstrate that the proposed approach can solve them effectively. The obtained solutions give decision makers some desired choices to select a right disassembly process when an actual product is disassembled.

中文翻译：

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基于双目标程序和散点搜索的多资源约束下拆卸序列优化

无论是否使用废弃产品，有效地对其进行拆解对于其再利用，回收和循环利用都至关重要。但是，现有的产品拆卸计划方法很少或根本没有注意资源约束，例如，数量有限的拆卸操作员和工具。因此，在执行时所产生的计划在实践中可能是无效的。通过考虑多资源约束，提出了选择性拆卸序列的双目标优化模型，以使拆卸利润最大化，时间最小化。采用分散搜索来解决所提出的双目标优化模型。它体现了生成各种初始解决方案，对目标函数进行全局评估，交叉组合运算符，用于改进解决方案的本地搜索策略，以及参考集更新方法。为了分析不同权重对其性能的影响，对不同产品进行了仿真。通过比较其优化结果和遗传本地搜索的结果，验证了其有效性。给从业者的注意事项-这项工作涉及产品拆卸的序列建模和计划问题。建立了一种多资源约束下产品拆卸的新型双目标优化模型。以前，这种问题是通过基于单个目标（即拆卸时间或成本）的优化的方法来处理的。如果不充分考虑拆卸资源（例如人工和工具），则最终的解决方案是不够的。另外，在实际的拆卸过程中，决策者可能希望最大程度地提高拆卸利润，并最大程度地减少了拆卸时间。这项工作考虑了这两个目标，并提出了分散搜索以解决拆卸问题。结果表明，该方法可以有效地解决这些问题。获得的解决方案为决策者提供了一些期望的选择，以便在拆卸实际产品时选择正确的拆卸过程。