Browsing by Subject "Queueing Theory"

This dissertation considers practical operational settings, in which a decision maker needs to either coordinate preferences or to align incentives among different parties. We formulate these issues into stochastic optimization problems and use a variety of techniques from the theories of applied probability, queueing and dynamic programming.

First, we study a stochastic matching problem. We consider matching over time with short and long-lived players who are very sensitive to mismatch, and propose a novel method to characterize the mismatch. In particular, players' preferences are uniformly distributed on a circle, so the mismatch between two players is characterized by the one-dimensional circular angle between them. This framework allows us to capture matching processes in applications ranging from ride sharing to job hunting. Our analytical framework relies on threshold matching policies, and is focused on a limiting regime where players demonstrate low tolerance towards mismatch. This framework yields closed-form optimal matching thresholds. If the matching process is controlled by a centralized social planner (e.g. an online matching platform), the matching threshold reflects the trade-off between matching rate and matching quality. The corresponding optimal matching threshold is smaller than myopic matching threshold, which helps building market thickness. We further compare the centralized system with decentralized systems, where players decide their matching partners. We find that matching controlled by either side of the market may achieve optimal social welfare.

Second, we consider a dynamic incentive management problem in which a principal induces effort from an agent to reduce the arrival rate of a Poisson process of adverse events. The effort is costly to the agent, and unobservable to the principal, unless the principal is monitoring the agent. Monitoring ensures effort but is costly to the principal. The optimal contract involves monetary payments and monitoring sessions that depend on past arrival times. We formulate the problem as a stochastic optimal control model and solve the problem analytically. The optimal schedules of payment and monitoring demonstrate different structures depending on model parameters. Overall, the optimal dynamic contracts are simple to describe, easy to compute and implement, and intuitive to explain.

This dissertation studies dynamic pricing in service systems where the system state is defined as the wait time. The first essay studies a single-server queue where customers arrive according to a Poisson process. The service provider announces the price rate and current system wait time to incoming customers, who decide whether to join the queue and determine their service duration. The objective is to maximize either the long-run average revenue or social welfare. The problem is formulated as a continuous-time control model, and we develop an innovative method to obtain the optimal control policy. The optimal dynamic pricing policy reveals the compensation effect, where the service provider lowers the price rate when the wait time exceeds a threshold, in addition to the usual congestion effect. A numerical study demonstrates the superiority of the revenue-maximizing pricing policy over static pricing policies, especially for low arrival rates and impatient customers. The extension to nonlinear pricing and heterogeneous customers yields similar policy insights, showcasing the value of considering customer characteristics in dynamic pricing models. The proposed model can be utilized to design dynamic pricing schemes for fast-charging stations.

The second essay addresses a mechanism design problem for a single-server queue with customers arriving according to a Poisson process and possessing private information about their wait time sensitivity. Following a direct mechanism, where the service provider announces the system wait time and offers a menu of options to each arriving customer. By choosing an option or opting out, customers aim to maximize their utility. The objective is to design a mechanism that maximizes the long-run average revenue. The optimal mechanism is wait-time dependent and admits customers with lower wait-time sensitivities. The model reveals strategic complementarity between admission decisions and service times which became the admission threshold, and offered service time decreases as the wait time increases. Comparisons with simpler heuristic mechanisms quantify the value of the optimal mechanism, showing significantly higher revenue generation, particularly for moderate service costs and arrival rates. Modifying service times becomes crucial when considering the different customer types and their interaction with wait time.

The third essay investigates a queueing system where the firm strategically determines the release time of each arriving request. We consider a first-come-first-serve single-server system, with customer requests arriving according to a Poisson process. The base model includes two types of customers: impatient and patient, characterized by their privately known service valuations and time sensitivities. The chapter explores the potential of strategically delaying the release of products to improve system performance. It reveals that such a delay occurs when the proportion of impatient customers is high and the system wait time is shorter than the threshold. Importantly, the optimal inflated release time does not vary with the system wait time, facilitating practical implementation. The extension to continuous-type customers confirms the tangible impact of strategic delay on revenue improvement, particularly when faced with uncertainty in the types of arriving requests.