Sampling-based Approximation Schemes for Serial Inventory System; Optimal Product Acquisition and Selling Strategies for Recycling Businesses

1) Sampling-based Approximation Schemes for Serial Inventory System
We study the classical finite horizon serial inventory control problem in a data-driven settings. The optimal echelon base-stock levels can be ob-tained in terms of only probability distributions of leadtime demands and other system primitives. Instead of assuming full knowledge of the demand distributions, we assume that the demand distributions can only be accessed through drawing random samples. Such data-driven models are ubiquitous in practice, where the cumulative distribution functions of the underlying random demand are either unavailable or too complicated to work with.
We aim to apply the Sample Average Approximation (SAA) method to the serial inventory control problem and establish upper bounds on the num-bers of samples needed for the SAA method to achieve a near-optimal ex-pected cost, under any level of required accuracy and pre-specified confidence probability. Related works in the literature will be reviewed and some pre-liminary results will be presented.

2) Optimal Product Acquisition and Selling Strategies for Recycling Businesses
With growing concerns on environmental protection and sustainable development, recycling businesses attract more and more public attention. We develop and analyze mathematical models that could help recycling businesses improve their efficiency and profits in both their acquisition and selling processes. Relevant literature will be reviewed. Several models and preliminary results will be presented and future direction will be discussed.