Electric Vehicle Fleets: Scalable Route and Recharge Scheduling through Column Generation

TL;DR

This paper presents a scalable column generation method for electric vehicle fleet routing and scheduling, effectively handling battery constraints and large-scale operations to optimize passenger transportation.

Contribution

It introduces a novel multigraph reformulation, efficient bi-directional pricing, sparsification techniques, and a diving heuristic for improved large-scale EV fleet optimization.

Findings

Outperforms previous algorithms in accuracy and speed

Handles problems with several hundred requests efficiently

Provides near-optimal solutions rapidly

Abstract

The rise of battery-powered vehicles has led to many new technical and methodological hurdles. Among these, the efficient planning of an electric fleet to fulfill passenger transportation requests still represents a major challenge. This is due to the specific constraints of electric vehicles, bound by their battery autonomy and necessity of recharge planning, and the large scale of the operations, which challenges existing optimization algorithms. The purpose of this paper is to introduce a scalable column generation approach for routing and scheduling in this context. Our algorithm relies on four main ingredients: (i) a multigraph reformulation of the problem based on a characterization of non-dominated charging arcs, (ii) an efficient bi-directional pricing algorithm using tight backward bounds, (iii) sparsification approaches permitting to decrease the size of the subjacent graphs dramatically, and (iv) a diving heuristic, which locates near-optimal solutions in a fraction of the time needed for a complete branch-and-price. Through extensive computational experiments, we demonstrate that our approach significantly outperforms previous algorithms for this setting, leading to accurate solutions for problems counting several hundreds of requests.