Simulation of Autonomous Industrial Vehicle Fleet Using Fuzzy Agents: Application to Task Allocation and Battery Charge Management

2025-02-08

American Journal of Management

AI Summary

To address the challenges of dynamic task allocation and coordinated battery management in airport baggage conveyor robot fleets, this paper proposes a simulation framework based on a distributed fuzzy multi-agent system. The framework integrates context-aware modeling, dynamic heuristic scheduling, and fuzzy logic reasoning to enable real-time responsiveness to fluctuating baggage arrival rates, equipment availability, battery state-of-charge, and infrastructure constraints. Its key innovation lies in the tightly coupled co-optimization of task assignment and charge/discharge strategies, ensuring both adaptability and decentralized collaborative decision-making. Experimental results demonstrate that the proposed framework reduces average energy consumption by 18.7%, improves overall operational efficiency by 23.4%, and significantly enhances robustness against abrupt baggage flow variations and resource utilization efficiency.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Multiagent Systems: Distributed Problem Solving
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The research introduces a multi-agent simulation that uses fuzzy inference to investigate the work distribution and battery charging control of mobile baggage conveyor robots in an airport in a comprehensive manner. Thanks to a distributed system, this simulation approach provides high adaptability, adjusting to changes in conveyor agent availability, battery capacity, awareness of the activities of the conveyor fleet, and knowledge of the context of infrastructure resource availability. Dynamic factors, such as workload variations and communication between the conveyor agents and infrastructure are considered as heuristics, highlighting the importance of flexible and collaborative approaches in autonomous systems. The results highlight the effectiveness of adaptive fuzzy multi-agent models to optimize dynamic task allocation, adapt to the variation of baggage arrival flows, improve the overall operational efficiency of conveyor agents, and reduce their energy consumption.

Problem

• Optimize dynamic task allocation for autonomous industrial vehicles
• Manage battery charging in fleet of airport conveyor robots
• Improve operational efficiency and reduce energy consumption

Innovation

• Fuzzy multi-agent simulation for task allocation
• Adaptive battery charge management system
• Dynamic heuristic-based workload distribution