Optimization of Fuzzy Electric Vehicle Routing Problem

Abstract 

This research project addresses the electric vehicle routing problem with fuzzy demands, soft time windows, and fuzzy real-time traffic conditions. A mixed-integer programming model was developed, and a two-phase solution approach was proposed. The first phase examined the effect of recharging, while the second phase studied the impact of real-time traffic conditions. To overcome the challenges posed by recharging and congestion, a novel fuzzy real-time adaptive optimizer (FRTAO) was introduced. The numerical case example and benchmark tests demonstrated that FRTAO can effectively reduce the total cost compared to the genetic algorithm, especially in cases where recharging was required or real-time traffic conditions affected the pre-planned routing. This research contributes to the sustainable development goals of making cities more inclusive, safe, resilient, and sustainable, as well as ensuring access to affordable, reliable, and sustainable energy.

Partners 

Dr. Sally Kassem