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A hybrid variable neighborhood search approach for the multi-depot green vehicle routing problem
Transportation Research Part E: Logistics and Transportation Review ( IF 8.3 ) Pub Date : 2021-03-31 , DOI: 10.1016/j.tre.2021.102293
Mir Ehsan Hesam Sadati , Bülent Çatay

The Multi-Depot Green Vehicle Routing Problem (MDGVRP) is an extension of the well-known Green Vehicle Routing Problem (GVRP) where a fleet of alternative fuel-powered vehicles (AFVs) are used to serve the customers. GVRP consists of determining AFV tours such that the total distance travelled is minimum. The AFVs depart from the depot, serve a set of customers, and complete their tours at the depot without exceeding their driving range and the maximum tour duration. AFVs may refuel en-route at public refueling stations. In MDGVRP, the AFVs are dispatched from different depot locations and may refuel during the day at any depot or refueling station. We formulate MDGVRP as a mixed integer linear programming model and develop a hybrid General Variable Neighborhood Search and Tabu Search approach by proposing new problem-specific neighborhood structures to solve the problem effectively. We assess the performance of our method using the GVRP dataset from the literature. Our results show that the proposed method can provide high quality solutions in short computation times. Then, we extend these instances to the multi-depot case and compare our solutions for small-size instances with the optimal solutions. We also report our results for large-size problems and investigate the trade-offs associated with operating multiple depots and adopting different refueling policies to provide further insights for both academicians and practitioners.



中文翻译:

一种多站点绿色车辆路径问题的混合变量邻域搜索方法

多站点绿色车辆路线问题(MDGVRP)是众所周知的绿色车辆路线问题(GVRP)的扩展,其中使用了一批替代燃料驱动的车辆(AFV)为客户提供服务。GVRP包括确定AFV行程,以使总行驶距离最小。AFV从仓库出发,为一组客户提供服务,并在不超出其驾驶范围和最大游览持续时间的情况下完成在该仓库的游览。AFV可能会在公共加油站途中加油。在MDGVRP中,AFV从不同的仓库地点调度,白天可能在任何仓库或加油站加油。我们将MDGVRP公式化为混合整数线性规划模型,并通过提出特定于问题的新邻域结构来有效解决问题,从而开发出混合的通用变量邻域搜索和禁忌搜索方法。我们使用文献中的GVRP数据集评估了我们方法的性能。我们的结果表明,该方法可以在较短的计算时间内提供高质量的解决方案。然后,我们将这些实例扩展到多仓库案例,并将针对小型实例的解决方案与最佳解决方案进行比较。我们还报告了针对大型问题的结果,并研究了与运营多个仓库以及采用不同的加油政策相关的权衡取舍,从而为院士和从业人员提供了进一步的见解。我们使用文献中的GVRP数据集评估了我们方法的性能。我们的结果表明,该方法可以在较短的计算时间内提供高质量的解决方案。然后,我们将这些实例扩展到多仓库案例,并将针对小型实例的解决方案与最佳解决方案进行比较。我们还报告了针对大型问题的结果,并研究了与运营多个仓库以及采用不同的加油政策相关的权衡取舍,从而为院士和从业人员提供了进一步的见解。我们使用文献中的GVRP数据集评估了我们方法的性能。我们的结果表明,该方法可以在较短的计算时间内提供高质量的解决方案。然后,我们将这些实例扩展到多仓库案例,并将针对小型实例的解决方案与最佳解决方案进行比较。我们还报告了针对大型问题的结果,并研究了与运营多个仓库以及采用不同的加油政策相关的权衡取舍,从而为院士和从业人员提供了进一步的见解。

更新日期:2021-03-31
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