AI Based Decision Support System for Dynamic Work Flow Optimization in Business Operations

Abstract

The growing complexity of the present-day business processes demand smart systems that can streamline the fluctuating operations in real time. The present paper describes an AI-based decision support system of the dynamic workflow optimization of business operations through Multi-Agent Deep Reinforcement Learning (MARL). The suggested model would help several agents in a coordinated effort of task assignment and resource allocation and reduce any bottlenecks in the constantly evolving environment. The development framework that is used is Ray (RLlib) and is used to facilitate training that is scalable, parallel execution, and efficient policy optimization. The system is tested within the simulated enterprise workflow setting and it is contrasted with the traditional rule-based and single-agent ones. The experiment outcomes prove significant reductions in the time of completing the workflow, resource consumption, and adaptability in unpredictable conditions. The multi-agent architecture improves performance of the global systems as well as decentralized decision-making. In general, the suggested MARL-based decision support system offers an adaptive and efficient solution with scalability to optimize complex business processes within dynamic business operating environments.