Dispatching and Pricing in Two-Sided Spatial Queues

TL;DR

This paper develops a model for dispatching and pricing in spatial two-sided queues, like ride-hailing, deriving a closed-form optimal policy and proposing a scalable heuristic that performs near-optimally in complex settings.

Contribution

It introduces a closed-form optimal dispatching policy with a zigzag structure and a scalable heuristic for more general scenarios, enhancing tractability and practical applicability.

Findings

Optimal policy has a closed-form zigzag structure.

The heuristic achieves near-optimal performance.

Algorithm is scalable and effective in simulations.

Abstract

We study a dispatching and pricing problem in two-sided spatial queues with fixed supply, motivated by ride-hailing and robotaxi platforms. Idle drivers queue on one side, waiting to pick up riders, while riders queue on the other, waiting to be matched with available drivers. The platform seeks to maximize net profit, penalized by rider waiting penalties, by jointly optimizing state-dependent dispatching and pricing decisions. We formulate this problem as a Markov decision process with state-dependent service times that capture key features of spatial matching. We show that, under mild assumptions, the optimal dispatching policy admits a closed-form expression with a zigzag structure. This policy significantly improves the tractability of pricing optimization due to the resulting closed-form stationary distribution and a substantially reduced state space. Building on this insight, we propose an efficient and scalable dynamic programming heuristic to approximate the optimal zigzag policy in more general settings. Extensive numerical experiments with both the analytical model and a ride-hailing simulation demonstrate that our algorithm is both near-optimal and highly scalable.