Review of Mathematical Approaches on Deteriorating Inventory Models

Abstract

Deteriorating inventory models have garnered significant attention due to their relevance in various industries where products degrade over time. This review surveys mathematical approaches employed in tackling challenges posed by deteriorating inventory management.The first approach commonly employed is deterministic modeling, where continuous or discrete-time models are utilized to capture deterioration rates and demand patterns. These models often rely on differential equations or difference equations to describe inventory dynamics over time.Stochastic modeling is another avenue explored to address uncertainty in demand and deterioration rates. Stochastic differential equations or Markov processes are commonly used to model random variations, providing a more realistic representation of real-world inventory systems.Optimization techniques play a crucial role in determining optimal inventory policies under deteriorating conditions. Dynamic programming, stochastic optimization, and heuristic algorithms are frequently employed to find optimal ordering policies that minimize costs while ensuring adequate inventory levels.recent advancements in machine learning and data-driven approaches have led to the integration of predictive analytics in inventory management. Techniques such as neural networks and time series forecasting are applied to predict future demand and deterioration patterns, aiding in better decision-making.