Dynamic Electric Vehicle Routing: Heuristics and Dual Bounds

Nicholas D Kullman (1) , Justin Goodson (2) , Jorge E. Mendoza (3)

1 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours
2 Saint Louis University
3 HEC Montréal - HEC Montréal

We introduce the electric vehicle routing problem with public-private recharging strategy in which vehicles may recharge en-route at public charging infrastructure as well as at a privately-owned depot. To hedge against uncertain demand at public charging stations, we design routing policies that anticipate station queue dynamics. We leverage a decomposition to identify good routing policies, including the optimal static policy and fixed-route-based rollout policies that dynamically respond to observed queues. The decomposition also enables us to establish dual bounds, providing a measure of goodness for our routing policies. In computational experiments, we show the value of our policies to be within 4.7 percent of the value of an optimal policy in most instances. Further, we demonstrate that our policies significantly outperform the industry-standard routing strategy in which vehicle recharging generally occurs at a central depot. More broadly, we offer examples for how operations research tools classically employed in static and deterministic routing can be adapted for dynamic and stochastic routing problems.

Domaines: Recherche opérationnelle [math.OC]

Nicholas D Kullman, Justin Goodson, Jorge E. Mendoza. Dynamic Electric Vehicle Routing: Heuristics and Dual Bounds. 2018. ⟨hal-01928730v1⟩