From Global Policies to Local Strategies: Multi-Objective Optimization of Resource-Specific Handover Policies
2026-06-01 • Multiagent Systems
Multiagent Systems
AI summaryⓘ
The authors address the problem of efficiently assigning tasks to people in business processes, which affects costs and how fast work gets done. They noticed that earlier methods often ignored how workers collaborate and pass tasks to each other. To fix this, the authors created a new approach that uses simulations with multiple agents and an algorithm that balances several goals at once to find the best ways for individuals to hand over tasks. Tests on both made-up and real data showed their method cuts costs and waiting times significantly compared to common strategies.
Resource AllocationBusiness Process ManagementReinforcement LearningMulti-Agent SystemsMulti-Objective OptimizationEvolutionary AlgorithmsPareto OptimalityTask HandoverProcess Simulation
Authors
Lukas Kirchdorfer, Artemis Doumeni, Han van der Aa, Hugo A. López
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.