Route planning of electric freight vehicles by considering internal and environmental conditions

Official URL: https://risc01.sabanciuniv.edu/record=b2486370 _(Table of contents)

Electric freight vehicles have strong potential to reduce emissions stemming from logistics operations; however, their limited range still causes critical limitations. Range anxiety is directly related to the total amount of energy consumed during trips. There are several operational factors that affect the energy consumption of electric vehicles and should be considered for accurate route planning. In this thesis, we investigate the effect of ambient temperature, cargo weight, road gradient, and regenerative braking process on the fleet composition, energy consumption, and routing decisions in last-mile delivery operations. First, we consider the influence of ambient temperature on the energy consumption of the vehicle. Cabin heating or cooling may significantly increase the energy discharged from the battery during the trip and reduce the driving range. Additionally, cold temperatures decrease battery efficiency and cause performance losses. We formulate this problem as a mixed-integer linear program and solve the small-size instances using a commercial solver. For the large-size instances we resort to an Adaptive Large Neighborhood Search method. We also provide a case study based on the real data provided by Ekol Logistics in their Adana operations. Then, we propose new preprocessing techniques to reduce the problem size and enhance the computational performance of the solution methods. Furthermore, we develop an algorithm that can be used to identify if a problem instance is infeasible. Our experimental study validates the performance of the proposed preprocessing techniques and feasibility check algorithm. Next, we take into account the effect of cargo weight on the energy consumption and routing decisions. We formulate three alternartive mathematical models and investigate their effectiveness. We also develop a Large Neighborhood Search (LNS) method by using an exact method to repair the partial solution. Finally, we tackle the problem involving cargo weight and road gradient by considering regenerative braking. Considering the road gradient, a loaded vehicle going uphill will consume significantly more energy. On the other hand, when it travels downhill it can recharge its battery through recuperation. For this problem, we introduce a new dataset generated using the benchmark data form the literature. We adapt our LNS and perform an extensive computational study using the generated data. Overall, our results show that the route plans made without considering any of these factors may lead to inefficiencies, unforeseen costs, and disruptions in logistics operations