Efficient allocation of shared resources across multiple processes

Effective resource allocation is crucial for optimizing business processes. Yet, most existing methods focus solely on single-process optimization, overlooking the interdependencies present in multi-process environments. This limitation results in inefficient resource allocation, and scalability challenges. To address this gap, we propose MuProMAC (Multi-Process Multi-Agent Coordination), a novel reinforcement learning-based method designed to optimize resource allocation across multiple interdependent business processes. Unlike prior methods, MuProMAC is the first online resource allocation method that explicitly models the interdependencies between processes and dynamically balances competing resource demands to minimize global average cycle time. We evaluate our method in five multi-process scenarios with different levels of resource contention, comparing it against state-of-the-art online resource allocation methods and three simple baselines. Our results show that MuProMAC is consistently among the top-performing methods in shared-resource environments. It achieves low cycle times and stable performance across different workload conditions, outperforming existing methods through its strong adaptability to evolving business processes and increasing complexity.