Urban freight transport is becoming increasingly complex due to a boost in the volume of products distributed and the associated number of delivery services. In addition, stakeholders’ preferences and city logistics dynamics affect the freight flow and the efficiency of the delivery process in downtown areas. In general, transport activities have a significant and negative impact on the environment and citizens’ welfare, which motivates the need for sustainable transport planning. This work proposes a metaheuristic-based approach for tackling an enriched multi-depot vehicle routing problem in which economic, environmental, and social dimensions are considered. Our approach integrates biased-randomization strategies within a variable neighborhood search framework in order to better guide the searching process. A series of computational experiments illustrates how the aforementioned dimensions can be integrated in realistic transport operations. Also, the paper discusses how the cost values change as different dimensions are prioritized.

中文翻译：

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偏向随机变量邻域搜索方法求解可持续多站点车辆路径问题

由于分销产品数量的增加和相关服务数量的增加，城市货运变得越来越复杂。此外，利益相关者的偏好和城市物流动态会影响市区的货运量和交付过程的效率。一般而言，运输活动对环境和公民福利产生重大和负面影响，这激发了对可持续运输计划的需求。这项工作提出了一种基于元启发式的方法来解决考虑了经济，环境和社会维度的丰富的多站点车辆路径问题。我们的方法在可变邻域搜索框架内集成了偏向随机化策略，以便更好地指导搜索过程。一系列计算实验说明了如何将上述尺寸集成到实际的运输操作中。此外，本文还讨论了如何根据不同的维度确定成本值的优先级。