Title: Stochastic Matching Networks: Theory and Applications to Matching Platforms.

In-Person Location: Groseclose 402

Online Link: https://gatech.zoom.us/j/8543540167?omn=96789989066

Time: 12 pm - 2 pm, May 17, 2024 (Friday)

Thesis Committee Members

Dr. Siva Theja Maguluri (Advisor, ISyE, Georgia Tech)

Dr. Alan Erera (ISyE, Georgia Tech)

Dr. Robert Foley (ISyE, Georgia Tech)

Dr. He Wang (ISyE, Georgia Tech)

Dr. Ramandeep Randhawa (USC Marshall)

Dr. Amy Ward (U-Chicago Booth)

Abstract

Traditional service-based marketplaces have now moved online with the emergence of platform economies. Examples include ride-hailing systems, meal and grocery delivery platforms, and EV-based transportation systems. In addition to such software-based platforms, recent technological breakthroughs are leading to networked matching platforms that match various virtual or physical entities—for example, matching payments in peer-to-peer payment channel networks. The focus of this thesis is on studying such matching platforms. 

While matching is a classical problem with rich literature in Economics and CS theory, throughput and delay in matching platforms with dynamic matching is not fully understood. We take the stochastic network viewpoint to model matching platforms as stochastic matching networks composed of matching queues. In contrast to classical queues with dedicated servers, both customers and servers arrive and depart in matching queues. It is a lot harder to analytically study such two-sided behavior, and classical queueing theory cannot be directly applied. In this thesis, we develop a theory of stochastic matching networks, providing a comprehensive understanding of throughput and delay in matching platforms. Hence, allowing us to design optimal control, e.g., matching decisions, that optimizes these objectives. 

This thesis is divided into three parts:

• The first part, comprising three chapters, sets up the stage by developing the fundamentals of stochastic matching networks. This part of the thesis highlights our fundamental contributions, both theoretical and modeling, to stochastic matching networks.
• The second part, comprising four chapters, develops optimal control for matching platforms by analyzing the models introduced in the previous part. Specifically, we consider applications to online marketplaces, EV-based transportation systems, and payment channel networks. Our results unravel fundamental trade-offs, providing key insights into the operations of these matching platforms.
• The third part of the thesis shifts the focus to analyzing classical queueing analogs of the proposed stochastic matching networks. In particular, some of the novel aspects of stochastic matching networks motivated us to investigate new facets of their classical equivalents. These results contribute to and build upon the fundamentals of queueing theory.