Leveraging Reinforcement Learning and Genetic Algorithms for Enhanced AI-Driven Procurement Optimization

Abstract

This research paper explores the potential of combining reinforcement learning (RL) and genetic algorithms (GA) to optimize procurement processes, a crucial component of supply chain management that directly impacts a company's profitability and operational efficiency. Traditional procurement systems often rely on static rule-based mechanisms, which struggle to adapt to dynamic market conditions. Our approach leverages the adaptability of RL, which enables systems to learn from interactions with the environment to improve decision-making over time, and the evolutionary techniques of GA, which optimize complex systems by simulating the process of natural selection. The paper details the design and implementation of a novel AI-driven procurement optimization framework that integrates RL for dynamic learning and GA for strategic solution evolution. Extensive simulations were conducted using real-world procurement data, demonstrating the framework's ability to significantly improve procurement performance metrics such as cost reduction, supplier selection efficiency, and risk management compared to traditional methods. The hybrid RL-GA model showed increased adaptability to fluctuating market environments and improved robustness in handling complex procurement variables. These findings suggest that the synergistic use of RL and GA offers a compelling advancement in AI-driven procurement optimization, providing a scalable solution that could be applied across various industries. Future research directions will explore the integration of additional AI techniques to further enhance system efficacy and the application of this framework in real-time procurement scenarios.