arXiv subjects

Chiwei Yan

Publications and source records attributed to Chiwei Yan.

12 recordsLinked to original sources

arXiv2026

Meeting Uncertain Threats with Feedback

Air and missile defense, naval force protection, and critical-infrastructure security require rapid allocation of scarce effectors against multiple incoming threats when neutralization is uncertain and observed only after a firing round. We formulate this defensive-allocation problem as a Markov decision process in which a commander assigns a fixed amount of effector capacity each round to heterogeneous threats under three operational objectives: minimizing expected threat-clearance time, maximizing the probability of clearance by a deadline, and maximizing effective assignments before a deadline. Rather than expensive full dynamic optimization, we study simple time-oblivious policies suited to fast implementation. Fair allocation, which ignores threat difficulty and spreads fire evenly, is highly effective: it is optimal for all three objectives under homogeneous threats or low-capacity engagements. Moreover, when effector capacity scales at least linearly with the number of threats, its threat-clearance time trails that of the optimal policy by at most a constant number of rounds. We further develop threat-difficulty-aware greedy policies for each objective, including a constant-factor guarantee for effective assignment maximization. Numerically, greedy policies are near-optimal across heterogeneous instances, while fair allocation remains a principled choice when threat difficulties are unknown.

math.OC↗

arXiv2026

A Markovian Traffic Equilibrium Model for Ride-Hailing

We develop a Markovian traffic equilibrium model for ride-hailing in which vehicles, whether empty or hired, make sequential order-acceptance and link-choice decisions over a traffic network to maximize total discounted return in an infinite-horizon semi-Markov decision process. The model endogenizes both competition among empty vehicles for passenger demand and traffic congestion arising from road usage at the link level. We characterize equilibrium as the solution to a fixed-point system, establish its existence, and develop relaxed fixed-point iteration algorithms for equilibrium computation, with convergence results for specialized network structures. Computational experiments on realistic networks demonstrate the model's practical value for transportation planning. Ablation analyses reveal that ignoring either traffic congestion or drivers' forward-looking behavior can lead to potentially substantial biases in policy evaluation.

cs.GT↗

arXiv2026

On-Trip Matching and Pricing for Shared Rides

Although shared rides have the potential to increase vehicle utilization and reduce congestion and emissions, these benefits depend heavily on ridesharing platforms' ability to match riders effectively. As such, shared rides have seen limited success outside of dense urban areas -- the sparse outskirts of greater metropolitan areas remain underserved. In the literature, the dominant matching model involves collecting rider requests in a batch interval and solving a non-bipartite matching problem on the requests. However, this model neglects the ability of a rider to be matched to a future arriving rider even after she is initially dispatched solo; namely, matching is only modeled pre-trip, and the value of on-trip matching is not explicitly accounted for. We develop a dynamic, stochastic matching model, where the platform makes both pre-trip and on-trip matching decisions, and contrast the behavior of each phase of matching. Using both synthetic and real-world data from Chicago, we find that whereas pre-trip matching is well-suited to dense downtown areas with concentrated demand, on-trip matching is critical in sparser outskirts where demand is spatially dispersed, and manages a tradeoff between matching opportunity and value. We also embed the matching model in an outer pricing optimization problem to study the interaction of matching with pricing, and find that the addition of on-trip matching increases profitability and efficiency for the platform and lowers prices for riders. These effects are particularly pronounced in the sparse outskirts, where operating shared rides -- even providing access to any form of transportation -- has historically been most challenging.

math.OC↗

arXiv2025

Dispatching and Pricing in Two-Sided Spatial Queues

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 ride-hailing simulations demonstrate that our algorithm is both near-optimal and highly scalable.

math.OC↗

arXiv2025

On the Linear Programming Model for Dynamic Stochastic Matching and Its Application to Pricing

Important pricing problems in centralized matching markets -- such as carpooling, food delivery and freight shipping platforms -- often exhibit a bi-level structure. At the upper level, the platform sets prices for heterogeneous demand types (e.g., rides across origin-destination pairs, food delivery orders across restaurant-customer pairs, or less-than-truckload shipments). The lower level subsequently matches converted demands to minimize operational costs; for example, by pooling riders into shared vehicles or consolidating multiple orders into single courier or trailer routes. Motivated by these applications, we study the optimal value (cost) function of a linear programming model with respect to demand arrival rates, originally proposed by Aouad and Saritac (2022) for cost-minimizing dynamic stochastic matching under limited time. In particular, we study the concavity properties of this cost function. We show that it suffices for every optimal basic feasible solution of the linear program to be nondegenerate in order to guarantee weak concavity. Leveraging this insight, we further establish that weak concavity holds when all demand types have strictly positive unmatched rates -- a natural condition in stochastic environments when demands have limited patience -- and characterize conditions under which this property is satisfied in the fluid linear program. Building on these theoretical insights, we develop a Minorization-Maximization (MM) algorithm that exploits the resulting difference-of-concave structure of the pricing problem. The algorithm requires little stepsize tuning and delivers substantial performance improvements over projected gradient methods on a large-scale, real-world ridesharing dataset with thousands of rider types (origin-destination pairs). This makes it a compelling algorithmic choice for solving such pricing problems in practice.

math.OC↗

arXiv2025

On-Off Systems with Strategic Customers

Motivated by applications such as urban traffic control and make-to-order systems, we study a fluid model of a single-server, on-off system that can accommodate multiple queues. The server visits each queue in order: when a queue is served, it is "on", and when the server is serving another queue or transitioning between queues, it is "off". Customers arrive over time, observe the state of the system, and decide whether to join. We consider two regimes for the formation of the on and off durations. In the exogenous setting, each queue's on and off durations are predetermined. We explicitly characterize the equilibrium outcome in closed form and give a compact linear program to compute the optimal on-off durations that maximizes total reward collected from serving customers. In the endogenous setting, the durations depend on customers' joining decisions under an exhaustive service policy where the server never leaves a non-empty queue. We show that an optimal policy in this case extends service beyond the first clearance for at most one queue. Using this property, we introduce a closed-form procedure that computes an optimal policy in no more than 2n steps for a system with n queues.

cs.GT↗

arXiv2025

The Role of Prescreening in Auctions with Predictions

Sellers often prescreen potential bidders, restricting participation to a select group of capable participants. Recent advances in machine learning and generative AI make this strategy increasingly viable by enabling the cost-effective identification of high-quality bidders. However, the practice departs from classic auction theory, which usually favors broad competition over selective exclusion. In this paper, we examine whether and under what conditions bidder prescreening can be justified. We analyze a setting in which bidders have independent and identically distributed private valuations, and the seller observes noisy signals generated by a valuation predictor. The seller determines how many top bidders to admit and, after receiving signals, selects exactly that many with the highest signal-based rankings. We demonstrate that an auction with prescreening is equivalent to a standard auction (i.e., without prescreening) but with correlated valuations. Our analysis shows that, although admitting fewer bidders leads to revenue losses in both second-price and first-price auctions, a more accurate predictor can mitigate or even fully offset these losses. In contrast, prescreening can significantly boost revenue in all-pay auctions; notably, when the predictor is perfect, admitting only two bidders is optimal. All results remain valid in the presence of reserve prices.

cs.GT↗

arXiv2022

Restricting Entries to All-Pay Contests

We study an all-pay contest in which players with low abilities are filtered out before competing for prizes. We consider a setting where the designer admits a certain number of top players. The admitted players update their beliefs based on the signal that their abilities are among the top, which leads to posterior beliefs that, even under i.i.d. priors, are correlated and depend on each player's private ability. We find that all effects of this elimination mechanism -- including the reduction in the number of admitted players and the resulting updated beliefs -- are captured by an \textit{inflated ability}. A symmetric and strictly increasing equilibrium strategy exists if and only if this inflated ability is increasing in the player's true ability. Under this condition, we explicitly characterize the unique strictly increasing Bayesian equilibrium strategy. Focusing on a winner-take-all prize structure, we find that each admitted player's effort strictly decreases as the admitted number increases. As a result, it is optimal to admit only two players in terms of maximizing the expected highest effort. Finally, in a two-stage extension, we find that there does not exist a symmetric and strictly increasing equilibrium strategy.

cs.GT↗

arXiv2021

Efficiency of ETA Prediction

Modern mobile applications such as navigation services and ride-sharing platforms rely heavily on geospatial technologies, most critically predictions of the time required for a vehicle to traverse a particular route, or the so-called estimated time of arrival (ETA). There are various methods used in practice, which differ in terms of the geographic granularity at which the predictive model is trained -- e.g., segment-based methods predict travel time at the level of road segments (or a combination of several adjacent road segments) and then aggregate across the route, whereas route-based methods use generic information about the trip, such as origin and destination, to predict travel time. Though various forms of these methods have been developed, there has been no rigorous theoretical comparison regarding their accuracies, and empirical studies have, in many cases, drawn opposite conclusions. We provide the first theoretical analysis of the predictive accuracy of various ETA prediction methods and argue that maintaining a segment-level architecture in predicting travel time is often of first-order importance. Our work highlights that the accuracy of ETA prediction is driven not just by the sophistication of the model but also by the spatial granularity at which those methods are applied.

stat.AP↗

arXiv2021

Randomized FIFO Mechanisms

We study the matching of jobs to workers in a queue, e.g. a ridesharing platform dispatching drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in trip earnings incentivizes drivers to cherry-pick, increasing riders' waiting time for a match and resulting in a loss of efficiency and reliability. We first present the direct FIFO mechanism, which offers lower-earning trips to drivers further down the queue. The option to skip the rest of the line incentivizes drivers to accept all dispatches, but the mechanism would be considered unfair since drivers closer to the head of the queue may have lower priority for trips to certain destinations. To avoid the use of unfair dispatch rules, we introduce a family of randomized FIFO mechanisms, which send declined trips gradually down the queue in a randomized manner. We prove that a randomized FIFO mechanism achieves the first best throughput and the second best revenue in equilibrium. Extensive counterfactual simulations using data from the City of Chicago demonstrate substantial improvements of revenue and throughput, highlighting the effectiveness of using waiting times to align incentives and reduce the variability in driver earnings.

cs.GT↗

arXiv2020

Matching Queues, Flexibility and Incentives

Problem definition: In many matching markets, some agents are fully flexible, while others only accept a subset of jobs. For example, ridesharing drivers can specify on the platform the destinations they are willing to accept. Conventional wisdom suggests reserving flexible agents, but this can backfire: anticipating higher matching chances, agents may misreport as specialized, reducing overall matches. We ask how platforms can design simple matching policies that remain effective when agents act strategically. Methodology/results: We model job allocation as a bipartite matching queueing system and analyze equilibrium throughput performance under different policies when agents choose which queue to join. We show that flexibility reservation is optimal under full information but can perform poorly with private information, sometimes substantially worse than random assignment. To address this, we propose a new policy -- flexibility reservation with fallback -- that guarantees robust performance across settings, without requiring precise knowledge of system parameters or agent utility functions. Managerial implications: Our results underscore the importance of accounting for strategic reporting in the design of matching policies: the proposed fallback policy both preserves flexibility and exploits latent flexibility when explicitly flexible agents are exhausted. Its simplicity and parameter-free nature also make it practical to implement in platforms such as ridesharing and affordable housing allocation.

cs.GT↗

arXiv2020

Matching Queues with Reneging: a Product Form Solution

Motivated by growing applications in two-sided markets, we study a parallel matching queue with reneging. Demand and supply units arrive to the system and are matched in an FCFS manner according to a compatibility graph specified by an N-system. If they cannot be matched upon arrival, they queue and may abandon the system as time goes by. We derive explicit product forms of the steady state distributions of this system by identifying a partial balance condition.

math.PR↗