Energizing Inventory Management to Optimize Energy Consumption of Handling Shortages by Neutrosophic Fuzzy Trapezoidal Number

Abstract

Embarking on the exploration of integrating environmental sustainability principles and neutrosophic fuzzy theory in inventory management, this study aims to effectively tackle shortages. It underscores the vital balance between economic efficiency and ecological responsibility in contemporary inventory management practices. Neutrosophic fuzzy theory emerges as a robust tool for navigating the inherent uncertainties in inventory optimization, offering a versatile framework for modelling intricate problems. Strategies for optimizing resource consumption and minimizing waste generation within inventory management are scrutinized, emphasizing the imperative of harmonizing economic objectives with environmental concerns. Introducing a novel framework that melds neutrosophic fuzzy with environmental metrics, the research aims to optimize inventory management processes while mitigating environmental impacts. Furthermore, it delves into the challenges of managing energy consumption, advocating for innovative approaches to address fluctuating energy prices, data limitations, and evolving regulatory requirements. Neutrosophic sets are introduced for energy consumption analysis and cost evaluation, showcasing their efficacy in managing uncertainty and variability in real-world scenarios. The study concludes with a Python-based analysis of neutrosophic mean in energy consumption, offering insights into central tendencies and uncertainties associated with energy-related costs. Utilizing visualization techniques to enhance comprehension and decision-making in energy management, this research contributes to advancing inventory management practices by integrating environmental sustainability principles and sophisticated mathematical techniques, thereby fostering more resilient and sustainable supply chain operations.