Abstract
Widespread adoption of electric vehicles (EVs) is expected to accelerate in the near future, and logistics and supply chain companies are compelled to keep up with this transformation. The challenges posed by adopting EVs for delivery systems include battery capacity, driving ranges, and charging durations. Companies should address these challenges effectively to continue fulfilling customer demands on time and keep their operational costs under control. A critical task for service companies in this context is to develop and implement efficient EV routing plans that account for charging policies and vehicles' technical features. This study proposes a mathematical model for the EV routing problem (EVRP) that incorporates heterogeneous vehicles and partial re-charging policy options. The proposed model aims to identify the best delivery routes, re-charging locations, and schedules that minimize total operational costs under soft time windows and varying charger types. Notably, this model considers a single depot and a set of customers, each with specific demand and time windows. EVs' energy consumption rates, charging speeds, load capacities, and battery capacities are explicitly incorporated into the routing and scheduling decisions under the proposed model. The proposed model aims to minimize the sum of operating costs for vehicles, penalty costs for unscheduled delivery times, and heterogeneous energy costs. Moreover, the usage of different charger types in charging stations is considered as a factor that can help decrease routing times and operation costs. By the inclusion of heterogeneous fleets and different charger types, the proposed model can provide effective plans for real-life settings.