A Sustainable AI-Integrated Inventory Optimization Model for Deteriorating Items Under Time-Dependent Demand and Inflationary Conditions

Abstract

The high pace of global supply chain development requires the incorporation of sustainable operations and innovative predictive measures. This study constructs a bi-objective mathematical inventory model that is designed to deal with non-instantaneous non-deteriorating items. It also takes into consideration the demand over time, the inflationary economic factors as well as the urgent requirement to cut down the carbon emission. Artificial Intelligence (AI) algorithms, namely, Long Short-Term Memory (LSTM) networks are implemented to forecast demand patterns and optimize ordering cycles because of the dynamic nature of the market needs. Numerical simulations using Particle Swarm Optimization (PSO) indicate an optimal policy yielding a Maximum Present Value of Total Profit of $8,450.60, with a recommended green technology investment of $1,200 and a dynamic selling price of $25.50. The results demonstrate that AI integration reduces stock outs by 14% compared to traditional models. The model combines product degradation in storage and transportation, which is crucial to perishable products. Moreover, it includes environmental taxes and green investments so as to be in line with the modern sustainable development targets. In a very complex mathematical formulation, the research compares the overall cost reduction to the reduction of emission. Sensitivity analysis is performed on the major parameters to show the strength of the suggested framework. The results indicate that the combination of AI-based demand forecasting and conventional Economic order quantities (EDOQ) models can considerably reduce the risks linked to wear and tear and unstable inflation rates. The study offers practical solutions to supply chain managers that are interested in realizing efficiency in their operations without compromising strict adherence to environmental standards.

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Novelty Statement

The study presents a new framework that combines AI-based demand forecasting and sustainable inventory modeling of depreciating products, which is the first to consider transportation deterioration, carbon taxes, and economic dynamics.