Abstract: Online resource allocation problems are at the heart of revenue management given the inherent demand uncertainty. For instance, in a typical setting customers arrive sequentially to a platform and the platform needs to make irrevocable resource allocation decisions in an online manner. We consider fundamental generalizations of these problems in order to incorporate some of the natural stochasticity in resource usage that arises in many applications.

In this talk, our main focus is on understanding the impact of reusability of resources – a key aspect of resource allocation in sharing economies. Here allocated resources are used by customers for some a priori unknown (stochastic) durations. Resources returned after use are available for re-allocation. We find bottlenecks in using classical algorithms and propose a new policy that achieves the best possible guarantee of (1-1/e) under reasonable assumptions. Further, in the process of analyzing this policy we develop a novel framework of analysis that is useful more broadly in other settings with stochastic resource consumption. Based on joint work with Vineet Goyal and Garud Iyengar.

Bio: Rajan Udwani is a postdoctoral researcher at IEOR, Columbia University, mentored by Prof. Vineet Goyal and Prof. Garud Iyengar. Broadly, his research revolves around the design of provably good and practical algorithms for optimization under uncertainty. His recent focus has been on problems at the intersection of online optimization and revenue management. Before starting at Columbia, he received his PhD in Operations Research from MIT, under the supervision of Prof. James Orlin and Prof. Andreas Schulz. Prior to this, he graduated with a B.Tech in Electrical Engineering from IIT Bombay.