A biased-randomized variable neighborhood search for sustainable multi-depot vehicle routing problems

Abstract

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.