Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes

TL;DR

This paper presents a deep learning-based method to optimize electric vehicle routing and scheduling, significantly reducing computation time while maintaining near-optimal solutions in dynamic fleet scenarios.

Contribution

It introduces a neural network-assisted approach with a padding mechanism to efficiently solve a complex EV routing and scheduling problem with variable fleet sizes.

Findings

Reduced runtime by 97.8% compared to traditional solvers

Achieved 99.5% feasibility of solutions

Deviation from optimal solutions was less than 0.01%

Abstract

Electric Vehicles (EVs) are becoming increasingly prevalent nowadays, with studies highlighting their potential as mobile energy storage systems to provide grid support. Realising this potential requires effective charging coordination, which are often formulated as mixed-integer programming (MIP) problems. However, MIP problems are NP-hard and often intractable when applied to time-sensitive tasks. To address this limitation, we propose a deep learning assisted approach for optimising a day-ahead EV joint routing and scheduling problem with varying number of EVs. This problem simultaneously optimises EV routing, charging, discharging and generator scheduling within a distribution network with renewable energy sources. A convolutional neural network is trained to predict the binary variables, thereby reducing the solution search space and enabling solvers to determine the remaining variables more efficiently. Additionally, a padding mechanism is included to handle the changes in input and output sizes caused by varying number of EVs, thus eliminating the need for re-training. In a case study on the IEEE 33-bus system and Nguyen-Dupuis transportation network, our approach reduced runtime by 97.8% when compared to an unassisted MIP solver, while retaining 99.5% feasibility and deviating less than 0.01% from the optimal solution.