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An Ant Colony algorithm hybridized with insertion heuristics for the Time Dependent Vehicle Routing Problem with Time Windows

机译:带有插入启发式算法的蚁群算法与时窗相关的时变车辆路径问题

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摘要

This paper presents an Ant Colony System algorithm hybridized with insertion heuristics for the Time-Dependent Vehicle Routing Problem with Time Windows (TDVRPTW). In the TDVRPTW a fleet of vehicles must deliver goods to a set of customers, time window constraints of the customers must be respected and the fact that the travel time between two points depends on the time of departure has to be taken into account. The latter assumption is particularly important in an urban context where the traffic plays a significant role.A shortcoming of Ant Colony algorithms for capacitated routing problems is that, at the final stages of the algorithm, ants tend to create infeasible solutions with unrouted clients. Hence, we propose enhancing the algorithm with an aggressive insertion heuristic relying on the minimum delay metric. Computational results confirm the benefits of more involved insertion heuristics. Moreover, the resulting algorithm turns out to be competitive, matching or improving the best known results in several benchmark problems.
机译:本文提出了一种与插入启发式混合的蚁群系统算法,用于带时间窗的时变车辆路径问题(TDVRPTW)。在TDVRPTW中,必须将一组车队交付给一组客户,必须尊重客户的时间窗口限制,并且必须考虑到两点之间的行驶时间取决于出发时间这一事实。后一种假设在交通流量起着重要作用的城市环境中尤为重要。蚁群算法对容量路由问题的一个缺点是,在算法的最后阶段,蚂蚁往往会为未路由的客户创建不可行的解决方案。因此,我们建议使用依赖最小延迟度量的积极插入启发式算法来增强算法。计算结果证实了更多涉及插入启发法的好处。而且,最终的算法在多个基准测试问题中具有竞争性,可以匹配或改善最著名的结果。

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