Optimal Vehicle Dispatching Schemes via Dynamic Pricing
This addresses the key operational challenge for ride-sharing platforms to maximize revenue or GMV through dynamic pricing, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of revenue-optimal pricing and vehicle dispatching in ride-sharing platforms by developing an efficient algorithm that computes exact optimal randomized pricing schemes, showing advantages over fixed and surge-based pricing in empirical evaluations with real data.
Over the past few years, ride-sharing has emerged as an effective way to relieve traffic congestion. A key problem for these platforms is to come up with a revenue-optimal (or GMV-optimal) pricing scheme and an induced vehicle dispatching policy that incorporate geographic and temporal information. In this paper, we aim to tackle this problem via an economic approach. Modeled naively, the underlying optimization problem may be non-convex and thus hard to compute. To this end, we use a so-called "ironing" technique to convert the problem into an equivalent convex optimization one via a clean Markov decision process (MDP) formulation, where the states are the driver distributions and the decision variables are the prices for each pair of locations. Our main finding is an efficient algorithm that computes the exact revenue-optimal (or GMV-optimal) randomized pricing schemes. We characterize the optimal solution of the MDP by a primal-dual analysis of a corresponding convex program. We also conduct empirical evaluations of our solution through real data of a major ride-sharing platform and show its advantages over fixed pricing schemes as well as several prevalent surge-based pricing schemes.