Optimal Task Assignment in Holonic Multi-Agent Systems by Resolving Performative Inconsistencies

Optimal task assignment in holonic multi-agent systems has emerged as a pivotal problem in modern distributed systems. Despite substantial gains in agent coordination, many large-scale systems still suffer from poor job allocation, resulting in performance bottlenecks and resource waste. Effective task assignment is critical for these systems since it influences individual agent performance and overall adaptability. A significant challenge within holonic multi-agent systems is ensuring optimal task assignment while resolving performative inconsistencies, such as role conflicts and coordination failures among agents. This research proposes a novel optimization framework to address these inconsistencies, enabling more efficient task allocation in holonic multi-agent systems. An objective function that minimizes task completion time, resource usage, and task priority while accounting for performative inconsistencies is presented. The effectiveness of the approach is demonstrated through real-world scenarios of smart transportation systems. Simulation results show that the proposed task allocation framework enables holons to achieve improved overall system performance through optimal distribution of task sets and effective resolution of performative inconsistencies.