Optimal Policies and Simulation-Based Optimization for Capacitated Production Inventory Systems

Abstract

The motivation for this stream of research has come from problems faced by diverse set of companies, such as IBM, AMD, Allegheny Ludlum, GE, Proctor and Gamble, Westinghouse, Intel, American Standard, McDonald's, and Caterpillar. Smaller local (to Pittsburgh) companies such as Sintermet, Blazer Diamond, ASKO and Northside Packing have also provided several interesting issues to pursue. At the heart of many of the problems is the interaction between demand variability and non-stationarity, available production capacity, holding costs of inventory (at different locations), lead times and desired service levels. The central goal of this research stream is to understand the interactions in simple single and multiple stage settings and to provide insights and implementable solutions for managing inventories in a cost-effective manner for complex systems. The goal of this chapter is to introduce in a systematic manner some recent advances in `Discrete-time, Capacitated Production-Inventory Systems facing Stochastic Demands' and we limit ourselves to single product setting. The material here is collected from papers that have appeared in the literature: [31, 22, 23, 48, 49].