This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
Optimizing Maintenance of Energy Supply Systems in City Logistics with Heuristics and Reinforcement Learning
1
Baobab Soluciones, Jose Abascal 55, 28003 Madrid, Spain
2
Research Center on Production Management and Engineering, Universitat Politècnica de València, Plaza Ferrandiz-Carbonell, 03801 Alcoy, Spain
3
Department of Organization Engineering, Business Administration and Statistics, Universidad Politécnica de Madrid, Jose Abascal 2, 28006 Madrid, Spain
*
Author to whom correspondence should be addressed.
Mathematics 2024, 12(19), 3140; https://doi.org/10.3390/math12193140 (registering DOI)
Submission received: 28 August 2024
/
Revised: 24 September 2024
/
Accepted: 5 October 2024
/
Published: 7 October 2024
Abstract
In urban logistics, effective maintenance is crucial for maintaining the reliability and efficiency of energy supply systems, impacting both asset performance and operational stability. This paper addresses the scheduling and routing plans for maintenance of power generation assets over a multi-period horizon. We model this problem as a multi-period team orienteering problem. To address this multi-period challenge, we propose a dual approach: a novel reinforcement learning (RL) framework and a biased-randomized heuristic algorithm. The RL-based method dynamically learns from real-time operational data and evolving asset conditions, adapting to changes in asset health and failure probabilities to optimize decision making. In addition, we develop and apply a biased-randomized heuristic algorithm designed to provide effective solutions within practical computational limits. Our approach is validated through a series of computational experiments comparing the RL model and the heuristic algorithm. The results demonstrate that, when properly trained, the RL-based model is able to offer equivalent or even superior performance compared to the heuristic algorithm.
Share and Cite
MDPI and ACS Style
Guerrero, A.; Juan, A.A.; Garcia-Sanchez, A.; Pita-Romero, L.
Optimizing Maintenance of Energy Supply Systems in City Logistics with Heuristics and Reinforcement Learning. Mathematics 2024, 12, 3140.
https://doi.org/10.3390/math12193140
AMA Style
Guerrero A, Juan AA, Garcia-Sanchez A, Pita-Romero L.
Optimizing Maintenance of Energy Supply Systems in City Logistics with Heuristics and Reinforcement Learning. Mathematics. 2024; 12(19):3140.
https://doi.org/10.3390/math12193140
Chicago/Turabian Style
Guerrero, Antoni, Angel A. Juan, Alvaro Garcia-Sanchez, and Luis Pita-Romero.
2024. "Optimizing Maintenance of Energy Supply Systems in City Logistics with Heuristics and Reinforcement Learning" Mathematics 12, no. 19: 3140.
https://doi.org/10.3390/math12193140
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article metric data becomes available approximately 24 hours after publication online.