发表机构
Federal University of ABC（ABC联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对快速路径可靠性问题，提出一种无需枚举最小路径的精确算法，利用基于目的地的边界和容量感知边界进行剪枝，生成无重复的完整下边界集合，并证明正确性与复杂度。
AI 中文摘要
快速路径可靠性问题旨在计算在给定时间限制内，给定流量沿单一源-目的地路径传输的概率。当施加预算限制时，路径可行性同时取决于前置时间、传输成本和容量。现有的精确方法可能需要事先枚举所有最小路径，或检查许多无法可行完成的局部路径。在本文中，我们提出了一种精确算法，该算法无需事先枚举最小路径即可生成相应的下边界状态向量。最短前置时间、最低成本和最大容量信息可产生可行的基于目的地的边界。更强的容量感知边界则从容量过滤的子网络中导出。这些边界允许提前剪枝不可行的分支，同时保留所有解。该算法生成精确的完整下边界集合，且无重复，并证明了其正确性和计算复杂度。
英文摘要
The quickest path reliability problem is to compute the probability that a given flow is transmitted along a single source-destination path within a given time limit. When a budget is imposed, path feasibility is jointly dependent on lead time, transmission cost, and capacity. Existing exact methods may require enumeration of all minimal paths a priori or examination of many partial paths that cannot be completed feasibly. In this paper, we propose an exact algorithm that generates the corresponding lower boundary state vectors without first enumerating minimal paths. Shortest-lead-time, least-cost, and widest-capacity information yield admissible destination-based bounds. Stronger capacity-aware bounds are derived from capacity-filtered subnetworks. These bounds allow early pruning of infeasible branches while keeping all solutions. The algorithm generates the exact complete set of lower bounds without any duplicates, and its correctness and computational complexity are proven.
