An analytics-driven economic order quantity model integrating fuzzy learning for deteriorating imperfect items in sustainable supply chains

Abstract

This study presents an advanced Economic Order Quantity inventory model that integrates intuitionistic fuzzy sets and fuzzy learning to enhance decision-making under environmental uncertainty. The model systematically incorporates green technology adoption and accounts for the uncertain impact of emerging technologies on carbon emissions. The proposed framework embeds carbon reduction incentives and tax policies into the inventory decision-making process by leveraging real-time data from environmental regulations and technological advancements. Additionally, the study explores the role of fuzzy learning in optimizing supply chain networks, enabling improved environmental performance, and minimizing carbon emissions. Integrating intuitionistic fuzzy sets, fuzzy learning, green technology, and carbon emission reduction strategies provides a mathematically rigorous approach to developing adaptive inventory models that achieve economic efficiency and environmental sustainability. Numerical experiments are validated by MATLAB software. Based on the numerical experiments, sensitivity analyses are performed on key model parameters to validate the effectiveness of the proposed methodology. The findings are further reinforced by computational simulations and mathematical insights, demonstrating the practical applicability and robustness of the model.