Using Reinforcement Learning to Solve a Dynamic Orienteering Problem with Random Rewards Affected by the Battery Status
Juan, Ángel A (Universitat Politècnica de València. Research Center on Production Management and Engineering)
Marugan, C.A. (Universitat Politècnica de València. Research Center on Production Management and Engineering)
Ahsini, Y. (Universitat Politècnica de València. Research Center on Production Management and Engineering)
Fornes, R. (Universitat Politècnica de València. Research Center on Production Management and Engineering)
Panadero, Javier (Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius)
Martin, Xabier A (Universitat Politècnica de València. Research Center on Production Management and Engineering)

Data:	2023
Resum:	This paper discusses an orienteering optimization problem where a vehicle using electric batteries must travel from an origin depot to a destination depot while maximizing the total reward collected along its route. The vehicle must cross several consecutive regions, with each region containing different types of charging nodes. A charging node has to be selected in each region, and the reward for visiting each node-in terms of a 'satisfactory' charging process-is a binary random variable that depends upon dynamic factors such as the type of charging node, weather conditions, congestion, battery status, etc. To learn how to efficiently operate in this dynamic environment, a hybrid methodology combining simulation with reinforcement learning is proposed. The reinforcement learning component is able to make informed decisions at each stage, while the simulation component is employed to validate the learning process. The computational experiments show how the proposed methodology is capable of design routing plans that are significantly better than non-informed decisions, thus allowing for an efficient management of the vehicle's battery under such dynamic conditions.
Ajuts:	European Commission 101092612 European Commission 101057294
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Orienteering problem ; Battery management ; Electric vehicle ; Reinforcement learning ; Simulation
Publicat a:	Batteries, Vol. 9, Issue 8 (August 2023) , art. 416, ISSN 2313-0105

DOI: 10.3390/batteries9080416

16 p, 2.1 MB

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