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Technical note: Split algorithm in O(n) for the capacitated vehicle routing problem

机译:技术说明:O(n)中的分裂算法用于车辆行进路线问题

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The Split algorithm is an essential building block of route-first cluster-second heuristics and modern genetic algorithms for vehicle routing problems. The algorithm is used to partition a solution, represented as a giant tour without occurrences of the depot, into separate routes with minimum cost. As highlighted by the recent survey of Prins et al. [18], no less than 70 recent articles use this technique. In the vehicle routing literature, Split is usually assimilated to the search for a shortest path in a directed acyclic graph g and solved in O(nB) using Bellman's algorithm, where n is the number of delivery points and B is the average number of feasible routes that start with a given customer in the giant tour. Some linear-time algorithms are also known for this problem as a consequence of a Monge property of g. In this paper, we highlight a stronger property of this graph, leading to a simple alternative algorithm in O(n). Experimentally, we observe that the approach is faster than the classical Split for problem instances of practical size. We also extend the method to deal with a limited fleet and soft capacity constraints. (C) 2015 Elsevier Ltd. All rights reserved.
机译:拆分算法是路线优先的簇第二启发式方法和现代遗传算法解决车辆路径问题的重要组成部分。该算法用于以最小的成本将表示为解决方案的解决方案划分为单独的路线,该解决方案表示为不存在仓库的巨型巡回演出。正如Prins等人最近的调查所强调的。 [18],最近不少于70篇文章使用这种技术。在车辆路线文献中,Split通常被同化为在有向无环图g中搜索最短路径,并使用Bellman算法在O(nB)中求解,其中n是交货点数,B是可行点的平均数从巨人之旅中的给定客户开始的路线。由于g的Monge属性,一些线性时间算法也已知用于此问题。在本文中,我们强调了该图的更强属性,从而导致了O(n)中的一种简单替代算法。通过实验,我们观察到对于实际大小的问题实例,该方法比经典的Split更快。我们还扩展了该方法,以处理有限的机队和软容量限制。 (C)2015 Elsevier Ltd.保留所有权利。

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