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@article{183632,
        author = {Dr. Gopichand Agnihotram and Joydeep Sarkar},
        title = {Dynamic Risk Balancing in Manufacturing using Agentic Reinforcement Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {2395-2401},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183632},
        abstract = {In modern manufacturing, achieving a dynamic equilibrium between producer and consumer risks is critical for sustainable profitability and market competitiveness. This paper introduces a novel framework using Agentic Reinforcement Learning (RL) to address this complex optimization challenge. We formulate the problem by defining an RL agent designed to navigate the manufacturing environment by making sequential decisions on production, pricing, and inventory. The core of our approach is a comprehensive reward function that maximizes profitability while explicitly penalizing risks for both producers and consumers. Producer risks, such as inventory holding costs, stockouts, and production inefficiency, are balanced against consumer risks like price volatility, product unavailability, and quality dissatisfaction. By training the agent to optimize a policy  that maximizes the cumulative discounted reward, the framework enables autonomous, data-driven decisions that adapt to real-time market dynamics. This methodology provides a robust solution for enhancing operational efficiency and resilience in complex manufacturing systems.},
        keywords = {Agentic Framework for Operations, AI in Production Scheduling, Autonomous Decision-Making, Dynamic Risk Balancing, Producer and Consumer Risk, Reinforcement Learning in Manufacturing, Smart Manufacturing, Supply Chain Optimization.},
        month = {August},
        }