arXiv subjects

Garrett van Ryzin

Publications and source records attributed to Garrett van Ryzin.

4 recordsLinked to original sources

arXiv2026

Supply Chain Coordination Mechanism Design: Consensus Planning Protocol Meets Vickrey-Clarke-Groves Mechanism

This paper introduces the theoretical framework for combining Vickrey-Clarke-Groves (VCG) mechanisms with the Consensus Planning Protocol (CPP) to enable truthful and efficient collaboration between a retailer and vendors to lower joint Cost to Serve. We demonstrate how this integration preserves both dominant-strategy incentive compatibility and efficiency in high-dimensional environments. We further introduce an activity fee design to improve its revenue property for the retailer while maintaining the mechanism's desirable properties. This CPP-VCG framework serves as the theoretical foundation for designing collaborative mechanisms to coordinates distributed, agent-based optimization between retailers and suppliers.

econ.TH↗

arXiv2025

Online Allocation of Throughput-Constrained Resources Using Proxy Assignments

We study a variation of the canonical online resource allocation problem in which resources are throughput, rather than budget, constrained. As in the classical setting, the decision-maker must assign sequentially arriving jobs to one of multiple available resources. However, in addition to the assignment costs incurred from these decisions, the decision-maker is also penalized for deviating from exogenous, time-varying target assignment rates for each resource, which represent the resources' respective throughput capacities throughout the horizon. The goal is to minimize the total expected assignment and deviation penalty costs incurred throughout the horizon when the distribution of assignment costs is unknown. We first show that naive extensions of state-of-the-art algorithms for classical budget-constrained resource allocation problems can fail dramatically when applied to throughput-constrained resource allocation. We then propose a novel ``proxy assignment" primal-dual algorithm that uses current arrivals to simulate the effect of future arrivals. We prove that our algorithm achieves the optimal $O(\sqrt{T})$ regret bound when the assignment costs of the arriving jobs are drawn i.i.d. from a fixed distribution. We demonstrate the practical performance of our approach by conducting numerical experiments on synthetic datasets, as well as real-world datasets from retail fulfillment operations.

math.OC↗

arXiv2025

Multiple Approximate-Response Agents (MARA): Fast Near-Optimal Primal Recovery for Distributed Optimization

Dual methods are useful for distributed optimization because they allow agent-level subproblems to be solved in parallel. However, achieving primal feasibility with dual methods is a challenge; it can take many iterations to find prices that recover primal feasibility, and even with optimal dual prices primal feasibility is not guaranteed unless special conditions like strict convexity hold. To address this limitation, we propose a simple primal recovery method, multiple approximate-response agents (MARA), that is able to rapidly reduce primal infeasibility, tolerating some degree of suboptimality. The method is agnostic to how dual prices are computed, so MARA can be applied to enhance any dual algorithm. Rather than returning a single primal response to each price query, MARA requires agents to generate multiple primal responses, each of which has bounded suboptimality. Because these multiple responses can be computed in parallel, there is no increase in the wall-clock time of the underlying dual algorithm. MARA then constructs a convex combination of the multiple responses by minimizing the sum of the primal and complementary slackness residuals to produce a high-quality primal solution. Tests of MARA using both a price localization method and a dual subgradient method show that it typically converges to a feasible, near-optimal solution in a few tens of iterations. Moreover, hyperparameters of MARA can be flexibly tuned to control the trade-off among speed, computational budget, and degree of suboptimality of the feasible solutions.

math.OC↗

arXiv2023

A Better Match for Drivers and Riders: Reinforcement Learning at Lyft

To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning approach that estimates the future earnings of drivers in real time and use this information to find more efficient matches. This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time. We evaluated the new approach during weeks of switchback experimentation in most Lyft markets, and estimated how it benefited drivers, riders, and the platform. In particular, it enabled our drivers to serve millions of additional riders each year, leading to more than $30 million per year in incremental revenue. Lyft rolled out the algorithm globally in 2021.

cs.LG↗