Service Placement in Dynamic Multi-AGV Environments for Minimized Energy Consumption
Claudia Torres-Pérez, Estela Carmona Cejudo, Cristina Cervelló-Pastor, Maryam Masoumi, Estefanía Coronado, Muhammad Shuaib Siddiqui
Abstract
In multi-automated guided vehicle (AGV) environments, inefficient service placement increases energy consumption, and charging cycles, lowering battery lifespan. Consequently, minimizing energy consumption is key for maintaining operational efficiency and sustainability. Additionally, the unpredictable arrival of service requests in multi-AGV systems can lead to system saturation. However, previous research overlooked the energy costs of on-device computation, especially under dynamic service arrivals. To address these challenges, this work proposes an energy minimization service placement algorithm (EMSPA). The results demonstrate that EMSPA outperforms a baseline random selection (RS) algorithm for different numbers of AGVs, services, and tasks per service, reducing normalized energy consumption by up to 2.34% and improving mean service acceptance rates by up to 16.09% with lineal execution time overhead. Further, EMSPA outperforms a queue-aware scheduling and deadlock mitigation strategy (QASDMS) in terms of processing power ratio by over 58.94%.
BibTeX
@inproceedings{ral2026_serviceplacement,
title = {Service Placement in Dynamic Multi-AGV Environments for Minimized Energy Consumption},
author = {Claudia Torres-Pérez and Estela Carmona Cejudo and Cristina Cervelló-Pastor and Maryam Masoumi and Estefanía Coronado and Muhammad Shuaib Siddiqui},
booktitle = {RA-L 2026},
year = {2026}
}