From Global Policies to Local Strategies: Multi-Objective Optimization of Resource-Specific Handover Policies
For business process management, this work provides a novel method to optimize inter-resource collaboration patterns, addressing a known bottleneck in adaptive resource allocation.
This paper introduces a multi-objective optimization approach for resource-specific handover policies in business processes, achieving 37% cost reduction and 58% waiting time reduction on average, outperforming heuristic baselines.
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.