arXiv subjects

Donghao Zhu

Publications and source records attributed to Donghao Zhu.

4 recordsLinked to original sources

arXiv2026

A Learning-Based Hybrid Decision Framework for Matching Systems with User Departure Detection

In matching markets such as kidney exchanges and freight exchanges, delayed matching has been shown to improve overall market efficiency. The benefits of delay are highly sensitive to participants' sojourn times and departure behavior, and delaying matches can impose significant costs, including longer waiting times and increased market congestion. These competing effects make fixed matching policies inherently inflexible in dynamic environments. We propose a learning-based Hybrid framework that adaptively combines immediate and delayed matching. The framework continuously collects data on user departures over time, estimates the underlying departure distribution via regression, and determines whether to delay matching in the subsequent period based on a decision threshold that governs the system's tolerance for matching efficiency loss. The proposed framework can substantially reduce waiting times and congestion while sacrificing only a limited amount of matching efficiency. By dynamically adjusting its matching strategy, the Hybrid framework enables system performance to flexibly interpolate between purely greedy and purely patient policies, offering a robust and adaptive alternative to static matching mechanisms.

cs.LG↗

arXiv2026

Huge-Scale Assortment Optimization with Customer Choice: A Parallel Primal-Dual Approach

We study huge-scale assortment optimization problems to maximize expected revenue under customer choice, addressing a fundamental challenge in industries such as transportation, retail, and healthcare. The choice-based linear programming (CBLP) formulation provides a powerful framework for optimizing sales allocations across customer segments, yet traditional approaches often fail to solve CBLPs of huge scale (involving millions of customer choices) due to the lack of algorithmic designs that exploit problem structure. To overcome this computational bottleneck, we propose a first-order primal-dual method, SPFOM, which requires only a small computational cost per iteration, achieves a provably near-optimal convergence rate, and can be readily extended to parallel computing environments. Computational experiments demonstrate the computational and practical superiority of SPFOM over state-of-the-art solvers for large-scale linear programs. The framework is extended to a multi-period assortment optimization setting with inventory constraints, where SPFOM estimates global shadow prices that enhance classical bid-price control policies compared with benchmark methods such as market segment decomposition. Numerical experiments and a case study using real-world data from the ZOZOTOWN platform validate the practical effectiveness of SPFOM, highlighting its advantages in improving revenue performance while maintaining balanced inventory levels.

math.OC↗

arXiv2025

Superiority of Instantaneous Decisions in Thin Dynamic Matching Markets

We study a dynamic matching setting where homogeneous agents arrive at random according to a Poisson process and randomly form edges yielding a sparse market. Agents stay in the market according to a certain sojourn time and wait to be matched with a compatible agent by a matching algorithm. When their maximum sojourn time is reached, they perish unmatched. The primary objective is to maximize the number of matched agents. Our main result is to show that a uniformly guaranteed sojourn time suffices to get almost optimal performance of instantaneous matching. Interestingly, this matching policy essentially keeps as few agents in the market as possible. Hence, in contrast to the common paradigm that market thickness is the crucial property for obtaining strong matching performance, we show that the agents' sojourn behavior can be an equally powerful factor. In addition, instantaneous matching is close to optimal with respect to minimizing waiting time. We develop new techniques for proving our results going beyond commonly adopted methods for Markov processes.

cs.DS↗

arXiv2021

Dynamic Bipartite Matching Market with Arrivals and Departures

In this paper, we study a matching market model on a bipartite network where agents on each side arrive and depart stochastically by a Poisson process. For such a dynamic model, we design a mechanism that decides not only which agents to match, but also when to match them, to minimize the expected number of unmatched agents. The main contribution of this paper is to achieve theoretical bounds on the performance of local mechanisms with different timing properties. We show that an algorithm that waits to thicken the market, called the $\textit{Patient}$ algorithm, is exponentially better than the $\textit{Greedy}$ algorithm, i.e., an algorithm that matches agents greedily. This means that waiting has substantial benefits on maximizing a matching over a bipartite network. We remark that the Patient algorithm requires the planner to identify agents who are about to leave the market, and, under the requirement, the Patient algorithm is shown to be an optimal algorithm. We also show that, without the requirement, the Greedy algorithm is almost optimal. In addition, we consider the $\textit{1-sided algorithms}$ where only an agent on one side can attempt to match. This models a practical matching market such as a freight exchange market and a labor market where only agents on one side can make a decision. For this setting, we prove that the Greedy and Patient algorithms admit the same performance, that is, waiting to thicken the market is not valuable. This conclusion is in contrast to the case where agents on both sides can make a decision and the non-bipartite case by [Akbarpour et al.,$~\textit{Journal of Political Economy}$, 2020].

cs.DS↗