[PAST EVENT] Mathematics Colloquium and EXTREEMS-QED Lecture: Anh Ninh (Rutgers University)

February 13, 2015
2pm - 3pm
Jones Hall, Room 301
200 Ukrop Way
Williamsburg, VA 23185Map this location
Abstract: We explore a general class of inventory control problem - the recruitment stocking problem, which can be found in clinical trials, marketing research/new product launch, as well as inventory management for end-of-life-cycle products. The goal is to recruit a target number of individuals through designated outlets. As soon as the recruits of all outlets add up to the target number, the recruitment is done and no more individuals will be admitted. The arrivals of individuals at each outlet are random. To recruit an individual upon his/her arrival, we must provide a pack of materials. We order the packs of materials in advance and hold them in the outlets. Outlets can neither transfer recruits nor cross-ship materials among themselves. If an outlet runs out of stock, any further arrival at the outlet will be lost.

The recruitment stocking problem differs from previous research conducted in the existing inventory management literature due to the finite target number which connects all outlets in such a way that the recruitment is done as soon as the recruits at all outlets reach the target. In most existing inventory models, we should satisfy demand as much as supply allows. In other words, demand should be satisfied if inventory is available. This is not true in the recruitment stocking problem, where as soon as the target is met, no more demand will be satisfied even if we have stock available. With this unique feature under consideration, performance evaluation and inventory allocation for this system are not known in the literature.

The goal of our research is twofold. First, we propose both exact and approximation methods to measure key performance metrics for the system: Type 1 and 2 service levels and recruitment time. We then leverage these results to allocate inventory efficiently for the supply chain network while minimizing the recruitment time.

Sarah Day