Selected Supply Chain Cluster Research Projects

Based on these properties, we will characterize the optimal control policies of the systems, such as warehouse space rental, inventory replenishment, product assortment. Meanwhile, we will study the properties of the optimal policies to obtain managerial insights. For some of the problems that have complicated optimal policy that are difficult to implement in practice, we will further develop simple and effective heuristics. More importantly, we will collect real data and use them to validate our models and analyze practical implications of our research outcomes.

We expect the project will not only contribute to the warehousing and inventory management literature but also provide solutions and guidelines on recently emerging problems in warehouse management.

Based on these properties, we will characterize the optimal control policies of the systems, such as warehouse space rental, inventory replenishment, product assortment. Meanwhile, we will study the properties of the optimal policies to obtain managerial insights. For some of the problems that have complicated optimal policy that are difficult to implement in practice, we will further develop simple and effective heuristics. More importantly, we will collect real data and use them to validate our models and analyze practical implications of our research outcomes.

We expect the project will not only contribute to the warehousing and inventory management literature but also provide solutions and guidelines on recently emerging problems in warehouse management.

The aim of this project is to study approximation algorithms for dynamic remanufacturing systems with stochastic product returns and customer demands. The key feature is that demands and product returns in different periods are correlated. A major domain of applications is remanufacturing systems where product returns in one period depend on demands in the previous periods, which are often observed in practice. The prior studies on dynamic remanufacturing systems mostly assume independent demands and returns to avoid the well-known curse of dimensionality of the dynamic programming formulation, i.e., the state space becomes so large that the optimal policy is computationally intractable. But such simplification greatly hinders the practical applicability of the results. Therefore, we will exploit novel approaches to formulate and analyze such remanufacturing systems. Specifically, we will evaluate decisions and construct approximation policies and algorithms based on alternative cost accounting schemes. The policies aim to balance various costs such as inventory holding, demand backlogging, and remanufacturing/manufacturing costs. More importantly, these policies are easily computable and implementable and will be proved to have constant worst-case performance guarantees.

We will firstly analyze a basic model where a remanufacturing firm determines manufacturing and remanufacturing quantities periodically to minimize its costs under correlated demands and product returns. And then we will study more complex systems with product return disposal, manufacturing/remanufacturing capacity constraints, and multi-type of product returns. Each of these extensions will require novel ideas to design cost accounting schemes and derive provably good approximation algorithms. To further demonstrate the value of our approximation policies, we will compare their performance to the myopic policies that are widely adopted as heuristics for problems with a large state space as well as those that ignore the correlation between returns and demands. This project is the first attempt to develop computationally efficient policies with constant performance guarantees for remanufacturing systems.