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Title: State Dependent Control of Ride Hailing Systems

Abstract: 

We study the design of state-dependent control for a closed queueing network model of ridesharing systems. We focus on the dispatch policy, where the platform can choose which vehicle to assign when a customer request comes in, and assume that this is the exclusive control lever available. The vehicle once again becomes available at the destination after dropping the customer. We consider the proportion of dropped demand in steady state as the performance measure.

We propose a family of simple and explicit state-dependent policies called Scaled MaxWeight (SMW) policies and prove that under the complete resource pooling (CRP) condition (analogous to the condition in Hall's marriage theorem), each SMW policy leads to exponential decay of demand-dropping probability as the number of vehicles scales to infinity. We further show that there is an SMW policy that achieves the optimal exponent among all dispatch policies, and analytically specify this policy in terms of the customer request arrival rates for all source-destination pairs. The optimal SMW policy protects structurally under-supplied locations. Simulations show substantial gains from our approach in practically relevant parameter settings.

Joint work with Sid Banerjee and Pengyu Qian.

Bio:
Yash Kanoria is an Associate Professor in Decision, Risk and Operations at Columbia Business School, working primarily on matching markets and the design and operations of marketplaces. Previously, he obtained a BTech from IIT Bombay in 2007, a PhD in Electrical Engineering from Stanford in 2012, and spent a year at Microsoft Research New England during 2012-13 as a Schramm postdoctoral fellow. He received an NSF CAREER Award in 2017, a Simons-Berkeley Research Fellowship in 2015 and an INFORMS JFIG paper competition second prize in 2014. He is a finalist for the 2018 Wagner Prize for Excellence in Operations Research Practice.