New method optimizes resource handover policies using evolutionary algorithms

Researchers have developed a new method for optimizing resource allocation in business processes by creating resource-specific handover policies. This approach combines a multi-agent system simulator with a multi-objective evolutionary algorithm to generate Pareto-optimal policies. Experiments on synthetic and real-world data demonstrated significant improvements, reducing costs by an average of 37% and waiting times by 58% compared to existing methods. AI

IMPACT Introduces a novel approach to optimize business process resource allocation, potentially improving efficiency and reducing costs in real-world applications.

RANK_REASON The cluster contains a research paper detailing a new methodology for multi-objective optimization. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.MA (Multiagent) →

• arXiv
• Multi-Agent System
• Multi-Objective Optimization
• Reinforcement Learning

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