Two-echelon vehicle planning for parcel E-commerce delivery: an application in a megacity using electric three-wheelers

Abstract

To identify the operational practices of logistics service providers delivering to e-commerce stores, in order to determine their key challenges and assess the economic feasibility of using low-cost electric vehicles. Operational logistics requirements were identified through interviews with two fleet planners and used to develop a test instance representing parcel delivery operations in the Bogotá Metropolitan Area, based on population density and income distribution. Four fleet strategies were evaluated: gasoline vans, electric vans, a heterogeneous internal combustion fleet with a transshipment point and range-limited three-wheelers and an equivalent electric fleet. A Python module integrated an existing vehicle routing algorithm, incorporating heterogeneous fleet constraints, customer clustering and economic and environmental analyses. For heterogeneous strategies, the module identifies the optimal transshipment point by minimizing fleet size, determining minimum-distance routes and estimating delivery costs. Large logistics operators require significant space for warehousing, fulfillment, and distribution activities, which typically leads to facilities being located on the outskirts of cities and results in long travel distances for vehicles. This challenge can be mitigated through the use of transshipment facilities. The deployment of electric three-wheelers requires advanced planning processes that account for service area constraints and limited driving range; however, it offers potential emission reductions of up to 76% compared to gasoline vans. While electric vans involve higher initial investment costs, electric three-wheelers present lower acquisition and operating costs. While much of the existing literature on fleet planning focuses on algorithm development, this research identifies the operational needs of logistics service providers and proposes a methodology that leverages existing routing algorithms. The approach emphasizes planning strategies to demonstrate how logistics costs and emissions can be reduced without modifying underlying routing algorithms in a cost-effective way.