A robust multi-objective routing problem for heavy-duty electric trucks with uncertain energy consumption
In the Vehicle Routing Problem, using Battery Electric Vehicles (BEVs) presents challenges such as limited driving range and extended recharging times. The energy consumption of BEVs plays a critical role in determining their distance capability and the frequency of required recharges. This study proposes a novel approach to address these challenges, specifically in the context of short-haul deliveries using Heavy-duty Battery Electric Trucks. The primary focus of the study is on two key objectives: minimizing transportation costs and ensuring customer satisfaction with on-time deliveries. The problem was initially formulated using a mathematical approach. To address it, two solution methods were devised—Nondominated Sorting Genetic Algorithm II (NSGA-II) and Adaptive Large Neighborhood Search (ALNS). These methods employ various techniques to find high-quality solutions. Numerical results indicate that the ALNS method, combined with the weighted-sum approach, outperformed other methods. To ensure the robustness of these solutions across different scenarios, a simulation study was conducted, assessing the plan's effectiveness under varying levels of energy consumption uncertainty. Simulation outcomes reveal that decision-makers can strike a balance between cost and the risk of power depletion in delivery operations by selecting an appropriate level of uncertainty for a robust model. Implementing the proposed model also allows companies to provide robust and environmentally friendly logistics, leading to reduced carbon emissions and costs.
Amiri, A., Zolfagharinia, H., & Hassanzadeh Amin, S. (2023). A robust multi-objective routing problem for heavy-duty electric trucks with uncertain energy consumption (external link, opens in new window) . Computers & Industrial Engineering, volume 178, 109108.
