IE SEMINAR:Electric Vehicle Routing Problem with On-Demand Charging...

Guest: İhsan Sadati, Sabanci University

Title: Electric Vehicle Routing Problem with On-Demand Charging System

Time: March 27, 2024, 13:40 - 14:30 

Location: FENS G035

Abstract: Many logistics operators rely on diesel-powered vehicles for their delivery services. However, the negative environmental impacts associated with internal combustion engine vehicles have prompted governments and city authorities to enact stricter regulations governing freight transportation within urban areas. These initiatives advocate for the transition to alternative fuel vehicles, with a particular emphasis on Electric Vehicles (EVs). Consequently, it is expected that EVs will constitute a significant portion of logistics operators' vehicle fleets. One major obstacle to the widespread adoption of EVs has been the lack of adequate charging infrastructure. Charging at the depot emerges as a practical solution in real-world logistics operations due to limited recharging infrastructure in many regions and uncertainty surrounding charger availability. While numerous public recharging stations may exist in the area, not all are suitable for commercial vehicles, especially trucks. Moreover, some companies prefer charging their EVs at their depots due to factors like high energy costs at public stations. Nonetheless, this approach can lead to operational inefficiencies, particularly for deliveries in peri-urban and rural areas. To address this issue, offering charging services for EVs, such as deploying Mobile Chargers (MCs), can enhance the efficiency of EV usage. In other words, a flexible recharging system with an on-demand mobile charging approach can be recommended. To tackle these challenges, we introduce the Electric Vehicle Routing Problem with On-Demand Charging System. MCs recharge EV batteries at customer locations along their routes when necessary. The primary aim is to minimize overall operational costs while maintaining the smallest fleet size possible. Initially, we present the mathematical model and subsequently, we propose a matheuristic approach that integrates Variable Neighborhood Search with an exact method to address the problem. Following this, we conduct an extensive numerical analysis to validate the efficiency of our proposed solution methodology and present superior solutions compared to two related problems discussed in existing literature. Additionally, we explore the potential advantages of utilizing MCSs and offer various trade-off analyses.

Bio: İhsan Sadati is an Instructor in the Industrial Engineering Program at Sabanci University. He holds a Ph.D. in Industrial Engineering and Operations Management (IEOM) from Koç University. He received his BS and MS in Industrial Engineering from the University of Tabriz and Urmia University, Iran. Prior to his current role, he served as a postdoctoral research fellow at Sabanci University for three years (2019-2022). Additionally, he gained teaching experience as an assistant professor in the Industrial Engineering Department at Istanbul Kültür University for one academic semester. Alongside his instructional duties, İhsan Sadati contributes as a Researcher at the Smart Mobility and Logistics Lab at Sabanci University. 

His research pursuits span various domains within Industrial Engineering, focusing primarily on Operations Research, Mathematical Optimization, Vehicle Routing problems, Green Logistics, and Heuristic Optimization. Currently, he dedicates his efforts to tackling contemporary environmental challenges in transportation and logistics, with a particular emphasis on Green and Electric Vehicle Routing Problems.