区间连续预算不确定下的可恢复鲁棒代表选择问题
Recoverable robust representatives selection problem under interval continuous budgeted uncertainty
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中文总结 AI 辅助
本文针对区间连续预算不确定下的可恢复鲁棒代表选择问题，利用其结构特性设计了一般情况及重要特例的强多项式时间算法，改变了该问题的计算复杂性格局。
中文摘要 AI 辅助
本文研究可恢复鲁棒代表选择问题，其中不确定的第二阶段成本采用具有连续预算的区间不确定模型。离散不确定预算下的该问题变体已知为NP难问题，而我们证明转向连续预算会从根本上改变计算复杂性格局。具体而言，通过利用连续预算模型下问题的结构特性，我们为一般情况设计了强多项式时间算法，还为一个重要特例提出了更高效的强多项式时间算法。
英文摘要
In this paper, the recoverable robust representative selection problem is considered, where uncertain second-stage costs are modeled using interval uncertainty with a continuous budget. While the variant under a discrete uncertainty budget is known to be NP-hard, we show that transitioning to a continuous budget fundamentally alters the computational complexity landscape. Specifically, by exploiting the structural properties of the problem under the continuous budget model, we design a strongly polynomial-time algorithm for the general case. Furthermore, we propose an even more efficient strongly polynomial-time algorithm for an important special case.
