From Global Policies to Local Strategies: Multi-Objective Optimization of Resource-Specific Handover Policies

2026-06-01

AI Summary

This work addresses the limitations of traditional resource allocation approaches, which often neglect inter-resource collaboration and struggle to balance multiple objectives such as cost, waiting time, and utilization. To overcome this, the authors propose a resource-granularity multi-objective handover strategy optimization framework that, for the first time, translates global collaboration awareness into personalized local policies. By integrating multi-agent process simulation with a multi-objective evolutionary algorithm, the method explicitly models collaborative relationships among resources and generates Pareto-optimal strategies. Experimental results on both synthetic and real-world datasets demonstrate substantial improvements over heuristic baselines, achieving an average cost reduction of 37% and a 58% decrease in waiting time, thereby significantly enhancing overall multi-objective optimization performance.

Abstract

Efficient resource allocation is a key challenge in business process management, with direct implications for cost, throughput time, and utilization. While recent Reinforcement Learning (RL) approaches have shown promise in deriving adaptive allocation policies, they typically neglect inter-resource collaboration patterns that can strongly influence real-world task handovers. Recognizing this, this paper introduces the first approach for multi-objective optimization of resource-level decision-making, enabling the recommendation of person-specific handover policies. To achieve this, our work combines an existing Multi-Agent System-based process simulator with a multi-objective evolutionary algorithm. The resulting approach produces Pareto-optimal, resource-specific policies that optimize the process across multiple objectives. Experimental results on synthetic and real-world datasets show that our approach reduces costs by an average of 37% and waiting time by 58%, consistently outperforming heuristic baselines and demonstrating the potential of leveraging collaboration-aware optimization to improve process performance.

Problem

• resource-specific handover policies
• multi-objective optimization
• inter-resource collaboration
• business process management
• task handover

Innovation

• multi-objective optimization
• resource-specific handover policies
• multi-agent simulation
• collaboration-aware allocation
• Pareto-optimal policies