Service Placement in Dynamic Multi-AGV Environments for Minimized Energy Consumption
Claudia Torres-Pérez, Estela Carmona-Cejudo, Cristina CervellÃ3-Pastor, Maryam Masoumi, EstefanÃa Coronado, Muhammad Shuaib Siddiqui
AI summary
Problem
Unpredictable service arrivals and neglected on-device computation costs in multi-AGV systems cause excessive energy drain and system saturation. Existing placement strategies fail to balance computational load, energy constraints, and dynamic communication requirements.
Approach
EMSPA uses a constrained exhaustive search to assign each incoming task to the AGV that minimizes total system power consumption while respecting real-time CPU/GPU, memory, and data rate limits.
Key results
- Reduces normalized energy consumption by up to 2.34% vs. random selection
- Improves mean service acceptance rates by up to 16.09%
- Lowers processing power ratio by over 58.94% compared to QASDMS
- Maintains linear execution time overhead across varying fleet sizes
Why it matters
Provides a practical, energy-aware scheduling framework for sustainable smart manufacturing and autonomous fleet management.
Abstract
In multi-automated guided vehicle (AGV) environ- ments, inefficient service placement increases energy consump- tion, and charging cycles, lowering battery lifespan. Conse- quently, minimizing energy consumption is key for maintaining operational efficiency and sustainability. Additionally, the un- predictable 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%.