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Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

机译:解决交通拥挤下的动态车辆路径问题

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摘要

This paper proposes a dynamic vehicle routing problem (DVRP) model with nonstationary stochastic travel times under traffic congestion. Depending on the traffic conditions, the travel time between two nodes, particularly in a city, may not be proportional to distance and changes both dynamically and stochastically over time. Considering this environment, we propose a Markov decision process model to solve this problem and adopt a rollout-based approach to the solution, using approximate dynamic programming to avoid the curse of dimensionality. We also investigate how to estimate the probability distribution of travel times of arcs which, reflecting reality, are considered to consist of multiple road segments. Experiments are conducted using a real-world problem faced by Singapore logistics/delivery company and authentic road traffic information.
机译:提出了一种交通拥堵下具有非平稳随机行驶时间的动态车辆路径问题模型。根据交通状况,两个节点之间(尤其是在城市中)的旅行时间可能与距离不成比例,并且会随着时间而动态和随机地变化。考虑到这种环境,我们提出了一个马尔可夫决策过程模型来解决此问题,并采用基于卷展的方法进行求解,并使用近似动态编程来避免维数的诅咒。我们还研究了如何估计弧线的行驶时间的概率分布,该弧线反映了现实,被认为由多个路段组成。实验是使用新加坡物流/送货公司面临的真实问题和真实的道路交通信息进行的。

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