Efficient algorithms for electric vehicles' min-max routing problem

TL;DR

This paper introduces a new min-max routing problem for electric vehicles, addressing their limited range and charging station scarcity, and proposes an efficient solution framework with promising computational results.

Contribution

It formulates the MEVRP and develops a branch and cut framework along with a hybrid heuristic to solve it efficiently.

Findings

The proposed algorithms effectively solve various MEVRP instances.

Computational results show significant improvements over baseline methods.

Sensitivity analyses validate the robustness of the approach.

Abstract

An increase in greenhouse gases emission from the transportation sector has led companies and the government to elevate and support the production of electric vehicles (EV). With recent developments in urbanization and e-commerce, transportation companies are replacing their conventional fleet with EVs to strengthen the efforts for sustainable and environment-friendly operations. However, deploying a fleet of EVs asks for efficient routing and recharging strategies to alleviate their limited range and mitigate the battery degradation rate. In this work, a fleet of electric vehicles is considered for transportation and logistic capabilities with limited battery capacity and scarce charging station availability. We introduce a min-max electric vehicle routing problem (MEVRP) where the maximum distance traveled by any EV is minimized while considering charging stations for recharging. We propose an efficient branch and cut framework and a three-phase hybrid heuristic algorithm that can efficiently solve a variety of instances. Extensive computational results and sensitivity analyses are performed to corroborate the efficiency of the proposed approach, both quantitatively and qualitatively.