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An edge scanning method for the continuous deviation-flow refueling station location problem on a general network
Networks ( IF 2.1 ) Pub Date : 2021-03-23 , DOI: 10.1002/net.22032
Omar Abbaas 1 , Jose A. Ventura 1
Affiliation  

This study addresses the continuous deviation-flow refueling station location problem on a general network. Instead of having a finite number of candidate locations, we consider any point in the network as a candidate location. In addition, vehicles are allowed to deviate from their prescribed (shortest) paths to refuel. At the beginning, we focus on the location of a single refueling facility, which is a relevant problem in the initial stages of development of a refueling infrastructure for alternative fuels in a transportation network. The objective is to maximize the traffic flow covered (in roundtrips per time unit). We propose an exact algorithm that determines the endpoints of all refueling segments on each edge of the network that cover the corresponding origin–destination flows. Then, the set with the best endpoints is shown to be optimal and can be used to determine the entire set of optimal locations. Network reduction rules and a network decomposition procedure are also discussed to reduce the size of the problem and improve the computational efficiency. Later, the entire set of endpoints is used in a set covering model to locate multiple refueling stations under the assumption that every vehicle only needs to refuel once on each way of the round trip. Moreover, we limit the allowed refueling deviation distance from the shortest path. Finally, a numerical example is provided to illustrate the proposed methodology.

中文翻译:

通用网络上连续偏流加油站选址问题的边缘扫描方法

本研究解决了一般网络上的连续偏离流量加油站选址问题。我们将网络中的任何点视为候选位置,而不是有限数量的候选位置。此外,允许车辆偏离其规定的(最短)路径来加油。一开始,我们专注于单一加油设施的位置,这是交通网络中替代燃料加油基础设施开发初期的一个相关问题。目标是最大化覆盖的交通流量(以每时间单位往返)。我们提出了一种精确的算法,可以确定网络每个边缘上覆盖相应起点-终点流的所有加油段的端点。然后,具有最佳端点的集合被证明是最佳的,可用于确定整组最佳位置。还讨论了网络缩减规则和网络分解过程,以减少问题的大小并提高计算效率。之后,在假设每辆车在往返的每条路上只需要加油一次的情况下,在集合覆盖模型中使用整组端点来定位多个加油站。此外,我们限制了最短路径的允许加油偏差距离。最后,提供了一个数值例子来说明所提出的方法。还讨论了网络缩减规则和网络分解过程,以减少问题的大小并提高计算效率。之后,在假设每辆车在往返的每条路上只需要加油一次的情况下,在集合覆盖模型中使用整组端点来定位多个加油站。此外,我们限制了最短路径的允许加油偏差距离。最后,提供了一个数值例子来说明所提出的方法。还讨论了网络缩减规则和网络分解过程,以减少问题的大小并提高计算效率。之后,在假设每辆车在往返的每条路上只需要加油一次的情况下,在集合覆盖模型中使用整组端点来定位多个加油站。此外,我们限制了最短路径的允许加油偏差距离。最后,提供了一个数值例子来说明所提出的方法。
更新日期:2021-03-23
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