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Heuristics for the Stochastic Eulerian Tour Problem

机译:随机欧拉游览问题的启发式方法

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The Stochastic Eulerian Tour Problem (SETP) seeks the Eulerian tour of minimum expected length on an undirected Eulerian graph, when demand on the arcs that have to be serviced is probabilistic. The SETP is NP-hard and in this paper, we develop three constructive heuristics for this problem. The first two are greedy tour construction heuristics while the third is a sub-tour concatenation heuristic. Our experimental results show that for grid networks, the sub-tour concatenation heuristic performs well when the probability of service of each edge is greater than 0.1. For Euclidean networks, as the number of edges increases, the second heuristic performs the best among the three. Also, the expected length of our overall best solution is lower than the expected length of a random tour by LIP to 10% on average for grid networks and up to 2% for Euclidean networks.
机译:当必须服务的弧线的需求具有概率时,随机欧拉巡回问题(SETP)会在无向欧拉图上寻求最小预期长度的欧拉巡回。 SETP是NP难解的,在本文中,我们针对此问题开发了三种构造启发式方法。前两个是贪婪的旅游建设启发式方法,而第三个是子旅游串联启发式方法。我们的实验结果表明,对于网格网络,当每个边的服务概率大于0.1时,子旅行级联启发式算法表现良好。对于欧几里得网络,随着边数的增加,第二个启发式算法在这三个算法中表现最佳。此外,我们总体最佳解决方案的预期长度比LIP的随机巡回预期长度要低,网格网络的平均长度为10%,欧几里德网络的平均长度为2%。

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