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Path enumeration by finding the constrained K-shortest paths

机译:通过找到约束的K最短路径进行路径枚举

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This paper deals with algorithms for finding the constrained K-shortest paths (CKSP) and their application to the path enumeration problem. An attractive property of using Constrained Shortest Paths for path enumeration is that paths can be selected based on objective criteria. The conventional way of finding these paths is to compute a sufficiently large number of overall shortest paths, and deleting the ones that do not satisfy the constraints. However for realistically sized networks, combined with restrictive constraints this method becomes unfeasible because of CPU time restrictions. A new method is proposed that finds the feasible shortest paths directly and can be applied in combination with a wide class of constraints. The paper explains how this CKSP algorithm can be implemented using the ordinary shortest path computation as its elementary operation. An example is provided in which the method is used to enumerate paths while avoiding strongly overlapping and overly circuitous paths. In this context the computational performance of the CKSP method is compared with that of the conventional method. On a network consisting of 200 nodes a speed-up factor exceeding 62 has been demonstrated on a problem that involves finding the 200 constrained shortest paths. The speed-up factor increases sharply with the size of the network and the level of restriction of the constraints. As opposed to the conventional method, the proposed implementation of the CKSP method displays only a limited sensitivity to the level of restriction of the constraints. While the conventional method could only deal with small networks, the proposed method can also enumerate paths for more realistically sized networks.
机译:本文讨论了寻找约束K最短路径(CKSP)的算法及其在路径枚举问题中的应用。使用约束最短路径进行路径枚举的一个吸引人的特性是可以根据客观标准选择路径。查找这些路径的常规方法是计算足够多的总最短路径,并删除不满足约束条件的路径。但是,对于实际规模的网络,再加上限制性约束,由于CPU时间限制,此方法变得不可行。提出了一种新方法,该方法可以直接找到可行的最短路径,并且可以与多种约束条件结合使用。本文解释了如何使用普通的最短路径计算作为其基本操作来实现这种CKSP算法。提供了一个示例,其中使用该方法枚举路径,同时避免强力重叠和过度circuit回的路径。在这种情况下,将CKSP方法的计算性能与常规方法进行了比较。在由200个节点组成的网络中,已经证明了涉及找到200个约束最短路径的问题的加速因子超过62。随着网络规模和约束条件的限制,加速因子急剧增加。与常规方法相反,建议的CKSP方法实现仅对约束的限制级别显示有限的敏感性。虽然常规方法只能处理小型网络,但所提出的方法也可以枚举更实际大小的网络的路径。

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