Abstract
The Electric Vehicle Routing Problem (EVRP) aims at routing Electric Vehicles (EVs) while planning their stops at Charging Stations (CSs), due to the limited autonomy of their batteries. The majority of studies on the EVRP and its variants have considered deterministic energy consumption. However, energy consumption is subject to a great deal of uncertainty, which if ignored can lead the EV to run out of battery mid-route. In this paper, we develop a two-stage stochastic programming formulation for the electric vehicle routing problem with stochastic energy consumption. In particular, we propose a threshold recourse policy which entails that the EV will head to a charging station after a certain energy level is reached. We show the added value of the extensive formulation of our model on a set of small instances derived from the deterministic literature.
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Acknowledgements
This work has been supported by “ULTRA OPTYMAL—Urban Logistics and sustainable TRAnsportation: OPtimization under uncertainTY and MAchine Learning”, a PRIN2020 project funded by the Italian University and Research Ministry (grant number 20207C8T9M). This study was carried out within the MOST—Sustainable Mobility National Research Center and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR)—MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4—D.D. 1033 17/06/2022, CN00000023), Spoke 5 “Light Vehicle and Active Mobility”. This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.
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Bezzi, D., Jabali, O., Maggioni, F. (2024). A Threshold Recourse Policy for the Electric Vehicle Routing Problem with Stochastic Energy Consumption. In: Bruglieri, M., Festa, P., Macrina, G., Pisacane, O. (eds) Optimization in Green Sustainability and Ecological Transition. ODS 2023. AIRO Springer Series, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-031-47686-0_20
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DOI: https://doi.org/10.1007/978-3-031-47686-0_20
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