Managing Perishable Inventory Systems: New Algorithms and Approximations

点击翻译按钮获取中文摘要

This grant provides funding for the development of simple, efficient, and near-optimal approximation algorithms for perishable stochastic inventory systems. Perishable products, such as fresh food, pharmaceuticals, and blood banks are ubiquitous and an indispensable part of our society, and spoilage and outdating represent a major threat to the profitability of companies such as grocery retailers. Thus, finding effective inventory management policies for perishable products is of significant importance. However, the analysis of dynamic perishable inventory systems is notoriously difficult in both theory and computation due to the high-dimensional nature. Indeed, the optimal control policies are very complex even in the case of independent and identically distributed demands, and the computation of optimal policies using dynamic program is in general intractable due to the "curse-of-dimensionality." The models studied allow general non-stationary and correlated demand processes, capturing the seasonality nature of the economy, and the approximation algorithms developed admit theoretical worst-case performance guarantees. If successful, the results of this research will lead to efficient tools for inventory managers to effectively incorporate demand forecast, such as advance demand information (ADI), martingale models of forecast evolution (MMFE), autoregressive moving average (ARMA) demand models, and Markov modulated demand process (MMDP), in making inventory replenishment decisions for perishable inventory systems. It will allow inventory managers to make use of available and reliable data, such as forecast updating, for inventory planning. The research will advance the science of stochastic inventory systems and provide deeper understanding of the subject area. The outcome of the project will help firms reduce waste, increase revenue, and even save lives (e.g., in blood bank applications).