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An effective Progressive Hedging algorithm for the two-layers time window assignment vehicle routing problem in a stochastic environment

机译:一种有效的逐步对冲算法在随机环境中的两层时间窗口分配车辆路由问题

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This paper presents an effective Progressive Hedging algorithm for vehicle routing problem with two-layers time window assignment and stochastic service times (2L-TWAVRPSST). Based on a predefined exogenous time window determined by the customers, an endogenous time window is assigned to each customer. Endogenous time windows have flexible width and composed of two-layers. The outer layer is wider than inner layer and is determined by violation variable. This approach aims to giving more flexibility to career companies for serving more customers using less vehicles. Customers could be visited even after the end of their assigned time windows by paying proportional penalty, while extra violation from the outer layer is not permitted. This problem is formulated as a two-stage stochastic model with the first-stage decisions of assigning inner and outer layers time window. Then in the second stage, routes are planned for each scenario combination of stochastic demand and service time. The validity and effectiveness of the proposed model was examined by various numerical examples. The problem was solved by a Progressive Hedging (PH) algorithm for large-scale instances. The results confirm efficiency of the considered solution approach in different instances.
机译:本文为两层时间窗口分配和随机服务时间(2L-TWAVRPSST)提供了一种有效的逐步倒塌算法。基于由客户确定的预定义的外源时间窗口,将内源时间窗口分配给每个客户。内源时间窗口具有灵活的宽度和由两层组成。外层比内层宽,并且通过违规变量确定。这种方法旨在为使用较少的车辆提供更多客户的职业公司提供更大的灵活性。即使在分配的时间窗口结束后,可以通过支付比例惩罚后访问客户,但不允许从外层的额外违规。该问题被制定为两阶段随机模型,具有分配内层和外层时间窗口的第一阶段决定。然后在第二阶段,计划用于随机需求和服务时间的每种情况组合的路线。通过各种数值示例检查所提出模型的有效性和有效性。该问题由大规模实例的渐进性对冲(pH)算法解决。结果确认了不同实例中所考虑的解决方案方法的效率。

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