有能力位置路由的非鲁棒强背包切割及相关问题

IF 0.7 4区管理学 Q3 Engineering

Military Operations Research Pub Date : 2023-06-06 DOI:10.1287/opre.2023.2458

P. Liguori, A. Mahjoub, G. Marquès, R. Sadykov, Eduardo Uchoa

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引用次数: 0

摘要

Liguori等人的“有能力位置路由和相关问题的非鲁棒强背包切割”为CLRP和其他共享嵌套背包结构的问题提供了一种新的BCP算法。它优于文献中现有的精确算法，使其成为解决具有大量仓库位置的实例的强大工具。一个关键的方法贡献是引入了RLKCs，这是一组来自“外部”背包约束的非鲁棒切割。这些切口在某种意义上很强，因为它们包含了主背包多面体的所有方面，在Dabia等人(2019)的封面切口中占主导地位。通过研究RLKCs的单调性和超可加性，可以使标记算法更有效地处理RLKCs。RLKCs对BCP性能的总体积极影响取决于问题和实例特征，但它们已被证明对仓库容量紧张的CLRP实例特别有效，使最终的BCP算法更加可靠。

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Nonrobust Strong Knapsack Cuts for Capacitated Location Routing and Related Problems

“Nonrobust Strong Knapsack Cuts for Capacitated Location Routing and Related Problems,” by Liguori et al., presents a novel BCP algorithm for the CLRP and for other problems that share a nested knapsack structure. It outperforms existing exact algorithms in the literature, making it a powerful tool for solving instances with a large number of depot locations. A key methodological contribution is the introduction of RLKCs, a family of nonrobust cuts derived from the “outer” knapsack constraints. These cuts are strong in the sense that they contain all facets of the master knapsack polytope, dominating the cover cuts by Dabia et al. (2019) . By exploring their monotonicity and superadditivity properties, it is possible to adapt the labeling algorithm for handling RLKCs efficiently. The overall positive impact of RLKCs on the BCP performance varies depending on the problem and instance characteristics, but they have proven particularly effective for CLRP instances with tight depot capacities, making the final BCP algorithm more reliable.

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来源期刊

Military Operations Research 管理科学-运筹学与管理科学

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Military Operations Research is a peer-reviewed journal of high academic quality. The Journal publishes articles that describe operations research (OR) methodologies and theories used in key military and national security applications. Of particular interest are papers that present: Case studies showing innovative OR applications Apply OR to major policy issues Introduce interesting new problems areas Highlight education issues Document the history of military and national security OR.