An Autonomous, Distributed, and Collaborative Resource Combinatorial Optimization Method for Human Interaction

This paper proposes a method to optimize human resource allocation for tasks involving human interaction. The method considers task success rates under temporal and spatial constraints, human experience and skills, and contextual factors. Human interaction is modeled in three types: autonomous, distributed, and collaborative. Additionally, individual skills, past performance, and biases are incorporated as variables to reflect real-world complexity. The approach enables formalize resource allocation processes traditionally managed through human communication. This paper presents the method’s formalization and compares five computational approaches. Experimental validation was conducted within the domain of systems engineering, utilizing a sample dataset comprising 100 tasks T and 1,000 individuals P. The proposed method demonstrated high reproducibility and effectiveness in task resolution.