Dynamic Pricing and Matching for Online Marketplaces

Abstract:  Motivated by applications from the gig economy and online marketplaces, we study a bipartite matching network under joint pricing and matching controls. The objective is to maximize the long-run average profit and minimize the delay for the system. In the first part of the talk, we propose a two-price policy and max-weight matching policy and show that it exhibits a η1/3 optimality rate when all the arrival rates are scaled by η. We also demonstrate the advantage of max-weight matching with respect to the number of server and customer types by proving and exploiting state space collapse. In the second part of the talk, we consider the special case of single customer and server type. The focus is on obtaining the entire queue length distribution in heavy traffic. A key observation is that, unlike a classical queue, the limiting distribution of a matching queue exhibits a phase transition. These results are established by generalizing the characteristic function method.

Bio:  Sushil Varma is a 5th-year Ph.D. student in the Industrial and Systems Engineering department at Georgia Tech, advised by Prof. Siva Theja Maguluri. His research interests include queueing theory, game theory, and revenue management with applications in online marketplaces like ride-hailing, load balancing, and stochastic processing/matching networks. Sushil has won the Stephen. S. Lavenberg Best Student Paper Award in IFIP Performance 2021 and the Alice and John Jarvis Best Student Paper Award in 2022.

***Event will take place in hybrid format. The location for in-person attendance will be room 1690 Beyster Building.   Attendance will also be available via Zoom.

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Zoom Passcode information is also available upon request to Michele Feldkamp ([email protected]).