Nonrobust Strong Knapsack Cuts for Capacitated Location Routing and Related Problems
提出一种针对容量限制选址路径问题的新BCP算法，引入非鲁棒强背包割（RLKCs），在仓库容量紧的实例上表现优异，超越现有精确算法。
“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.