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Decentralized coordination for truck platooning
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2022-08-01 , DOI: 10.1111/mice.12899
Yikai Zeng 1 , Meng Wang 2 , Raj Thilak Rajan 1
Affiliation  

Coordination for truck platooning refers to the active formation of a group of heavy-duty vehicles traveling at close spacing to reduce the overall truck operations costs. Conventionally, this coordination is achieved by optimizing various truck-related parameters, such as schedules, velocities, and routes, based on an objective function that minimizes a certain cost, for example, fuel usage. However, prevalent algorithms for the coordination problem are typically integer-constrained, which are not only hard to solve but are not readily scalable to increasing fleet sizes and networks. In this paper, to overcome these limitations, we propose a centralized formulation to optimize the truck parameters and solve a multidimensional objective cost function including fuel, operation time costs and preferential penalty. Furthermore, to improve the scalability of our proposed approach, we propose a decentralized algorithm for the platoon coordination problem involving multiple fleets and objectives. We perform both theoretical and numerical studies to evaluate the performance of our decentralized algorithm against the centralized solution. Our analysis indicates that the computation time of the proposed decentralized algorithms is invariant to the increasing fleet size, at the cost of a small relative gap to the optimum cost given by the centralized method. We discuss these results and present future directions for research.

中文翻译:

卡车编队的分散协调

卡车编队协调是指主动编组一组近距离行驶的重型车辆,以降低卡车的整体运营成本。通常,这种协调是通过基于最小化特定成本(例如燃料使用)的目标函数来优化各种与卡车相关的参数(例如时间表、速度和路线)来实现的。然而,用于协调问题的流行算法通常是整数约束的,这不仅难以解决,而且不易扩展以适应不断增加的车队规模和网络。在本文中,为了克服这些限制,我们提出了一个集中公式来优化卡车参数并解决一个多维目标成本函数,包括燃料、操作时间成本和优惠惩罚。此外,为了提高我们提出的方法的可扩展性,我们提出了一种分散算法来解决涉及多个车队和目标的排协调问题。我们进行理论和数值研究,以评估我们的分散算法相对于集中解决方案的性能。我们的分析表明,所提出的分散算法的计算时间对于不断增加的车队规模是不变的,代价是与集中方法给出的最佳成本的相对差距很小。我们讨论了这些结果并提出了未来的研究方向。我们进行理论和数值研究,以评估我们的分散算法相对于集中解决方案的性能。我们的分析表明,所提出的分散算法的计算时间对于不断增加的车队规模是不变的,代价是与集中方法给出的最佳成本的相对差距很小。我们讨论了这些结果并提出了未来的研究方向。我们进行理论和数值研究,以评估我们的分散算法相对于集中解决方案的性能。我们的分析表明,所提出的分散算法的计算时间对于不断增加的车队规模是不变的,代价是与集中方法给出的最佳成本的相对差距很小。我们讨论了这些结果并提出了未来的研究方向。
更新日期:2022-08-01
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