The Electric Vehicle Routing Problem (EVRP) aims at routing Electric Vehicles (EVs) while planning their stops at Charging Stations (CSs), due to the lim-ited 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 consump-tion. 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.

(2024). A Threshold Recourse Policy for the Electric Vehicle Routing Problem with Stochastic Energy Consumption . Retrieved from https://hdl.handle.net/10446/268750

A Threshold Recourse Policy for the Electric Vehicle Routing Problem with Stochastic Energy Consumption

Maggioni, Francesca
2024-04-02

Abstract

The Electric Vehicle Routing Problem (EVRP) aims at routing Electric Vehicles (EVs) while planning their stops at Charging Stations (CSs), due to the lim-ited 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 consump-tion. 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.
2-apr-2024
Bezzi, Dario; Jabali, Ola; Maggioni, Francesca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/268750
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