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A Stochastic Vehicle Routing Problem with Travel Time Uncertainty: Trade-Off Between Cost and Customer Service

机译:具有旅行时间不确定性的随机车辆路径问题:成本与客户服务之间的权衡

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摘要

On-time shipment delivery is critical for just-in-time production and quick response logistics. Due to uncertainties in travel and service times, on-time arrival probability of vehicles at customer locations can not be ensured. Therefore, on-time shipment delivery is a challenging job for carriers in congested road networks. In this paper, such on-time shipment delivery problems are formulated as a stochastic vehicle routing problem with soft time windows under travel and service time uncertainties. A new stochastic programming model is proposed to minimize carrier’s total cost, while guaranteeing a minimum on-time arrival probability at each customer location. The aim of this model is to find a good trade-off between carrier’s total cost and customer service level. To solve the proposed model, an iterated tabu search heuristic algorithm was developed, incorporating a route reduction mechanism. A discrete approximation method is proposed for generating arrival time distributions of vehicles in the presence of time windows. Several numerical examples were conducted to demonstrate the applicability of the proposed model and solution algorithm.
机译:准时交货对于准时生产和快速响应物流至关重要。由于旅行和服务时间的不确定性,无法保证车辆在客户位置准时到达的可能性。因此,对于拥挤的道路网络中的承运商而言,按时交货是一项具有挑战性的工作。在本文中,这种准时发货问题被表述为在旅行和服务时间不确定的情况下具有软时间窗的随机车辆路径问题。提出了一种新的随机编程模型,以最大程度地降低运营商的总成本,同时确保在每个客户位置的准时到达概率最小。该模型的目的是在运营商的总成本和客户服务水平之间找到一个良好的平衡。为了解决该模型,开发了一种结合了路由约简机制的迭代禁忌搜索启发式算法。提出了一种离散逼近方法,用于在存在时间窗的情况下生成车辆的到达时间分布。进行了几个数值算例,以证明所提出的模型和求解算法的适用性。

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