Artificial Intelligence (AI) Monitored EOQ Model for Deteriorating Items under Limited Storage Facility, Partial Backlogging and Time Dependent Demand
DOI:
https://doi.org/10.13052/jgeu0975-1416.1424Keywords:
Artificial intelligence, machine learning, reorder point prediction, inventory control, demand forecasting, time-dependent deterioration, partial backloggingAbstract
In today’s highly competitive markets, the rising cost of warehouse space has become a critical challenge for effective inventory management. This necessitates inventory replenishment policies that explicitly account for limited storage capacity. In this study, we develop an inventory model designed to optimize inventory levels and improve ordering decisions under storage-space constraints. Motivated by the growing influence of Artificial Intelligence (AI) and Machine Learning (ML) in supply chain management implementing AI in this domain requires a structured approach beginning with the collection of high-quality data from diverse sources such as sales records, customer interactions, production logs, and real-time inventory levels. This data must undergo rigorous pre-processing and cleaning to remove inconsistencies and ensure reliability for subsequent analysis. Based on operational needs, suitable AI models – such as machine learning algorithms for demand forecasting, natural language processing for customer inquiries, and computer vision for product identification – can then be selected and trained on historical datasets to identify actionable patterns The data collected support more accurate and timely reorder point decisions. Demand is assumed to be time-dependent, while deterioration is also considered as a time-varying phenomenon. The production rate is finite, shortages are permitted with partial backlogging and the backlogging rate is inversely related to customer waiting time. The objective of the model is to minimize the total inventory cost by jointly determining the optimal cycle length, production rate and order quantity. The effectiveness of the proposed model is demonstrated through a numerical example and a comprehensive sensitivity analysis is conducted to examine the impact of key parameters. The results indicate that incorporating storage constraints and AI-supported reorder decisions can significantly enhance inventory performance and cost efficiency.
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