Optimizing warehouse costs of degrading products using linear programming

Abstract

There is a major operational challenge to Inventory degradation for large-scale warehouses, particularly for perishable items, semi-perishable goods, and products susceptible to obsolescence. The financial effect of degradation in storage losses, increased replenishment frequency, qu deterioration, and reduced customer satisfaction. This research aims to develop a comprehensive Linear Programming (LP)-based framework to optimize warehouse inventory decisions while minimizing costs associated with product degradation. The study integrates classical inventory models, degradation functions, storage allocation constraints, and demand satisfaction requirements into a unified mathematical structure. Experimental results show that the proposed LP model significantly reduces total cost compared to traditional EOQ and Safety Stock-driven strategies. The research also explores the role of decay rates, warehouse’s zoning, cyclic replenishment cycles, and environmental parameters in decision-making. The findings contribute to operational research applications in warehouse science and offer practical implications for perishable product management.