Abstract: Resource pooling is a fundamental concept in supply chain management for reducing and hedging uncertainty. An important problem in resource pooling is to decide (1) the capacity level of pooled resources in anticipation of random demand of multiple customers and (2) how the capacity should be allocated to fulfill customer demands after demand realization. In this talk, I present a general framework to study this two-stage problem when customers require individual and possibly different service levels. The optimal rationing policy for any fixed feasible capacity level is developed via tools from online optimization. The optimality follows from a semi-infinite linear programming formulation of the problem and a strong duality with its dual problem, which also enables us to compute the optimal capacity levels. I discuss extensions of our approaches to more complex environments in supply chain management.

Bio: Jiashuo Jiang obtained his PhD degree from the Operations group at NYU Stern School of Business, in January 2023. He is advised by Prof. Jiawei Zhang from NYU Stern and Prof. Will Ma from Columbia Business School. His research develops new methodologies to stochastic optimization, online optimization and data-driven decision making, with applications to real-life problems arising from supply chain management, inventory management, revenue management, among others. He has finished an internship at Amazon SCOT, where he applied online optimization techniques developed during his PhD study to solve the supply chain management problem at Amazon. Prior to joining NYU Stern, he obtained his bachelor degree in mathematics from Peking University in June 2017, advised by Prof. Zaiwen Wen