Blind Queues: The Impact of Consumer Beliefs on Revenues and Congestion

In many service settings, customers have to join the queue without being fully aware of the parameters of the service provider (e.g., customers at checkout counters may not know the true service rate before joining). In such “blind queues,” customers make their joining/balking decisions based on limited information about the service provider’s operational parameters (from past service experiences, reviews, etc.) and queue lengths. We analyze a firm serving customers making decisions under arbitrary beliefs about the service parameters in an observable queue for a service with a known price. By proposing an ordering for the balking threshold distributions in the customer population, we are able to compare the effects of customer beliefs on the queue. We show that, although revealing the service information to customers improves revenues under certain conditions, it may destroy consumer welfare or social welfare. Given a market size, consumer welfare can be significantly reduced when a fast server announces its true service parameter. When revenue is higher under some beliefs, one would expect the congestion to also be higher because more customers join, but we show that congestion may not necessarily increase

Pricing of reusable resources under ambiguous distributions of demand and service time with emerging applications

Monopolistic pricing models for revenue management are widely used in practice to set prices of multiple products with uncertain demand arrivals. The literature often assumes deterministic time of serving each demand and that the distribution of uncertainty is fully known. In this paper, we consider a new class of revenue management problems inspired by emerging applications such as cloud computing and city parking, where we dynamically determine prices for multiple products sharing limited resource and aim to maximize the expected revenue over a finite horizon. Random demand of each product arrives in each period, modeled by a function of the arrival time, product type, and price. Unlike the traditional monopolistic pricing, here each demand stays in the system for uncertain time. Both demand and service time follow ambiguous distributions, and we formulate robust deterministic approximation models to construct efficient heuristic fixed-price pricing policies. We conduct numerical studies by testing cloud computing service pricing instances based on data published by the Amazon Web Services (AWS) and demonstrate the efficacy of our approach for managing revenue and risk under various distributions of demand and service time

Pricing of reusable resources under ambiguous distributions of demand and service time with emerging applications

Monopolistic pricing models for revenue management are widely used in practice to set prices of multiple products with uncertain demand arrivals. The literature often assumes deterministic time of serving each demand and that the distribution of uncertainty is fully known. In this paper, we consider a new class of revenue management problems inspired by emerging applications such as cloud computing and city parking, where we dynamically determine prices for multiple products sharing limited resource and aim to maximize the expected revenue over a finite horizon. Random demand of each product arrives in each period, modeled by a function of the arrival time, product type, and price. Unlike the traditional monopolistic pricing, here each demand stays in the system for uncertain time. Both demand and service time follow ambiguous distributions, and we formulate robust deterministic approximation models to construct efficient heuristic fixed-price pricing policies. We conduct numerical studies by testing cloud computing service pricing instances based on data published by the Amazon Web Services (AWS) and demonstrate the efficacy of our approach for managing revenue and risk under various distributions of demand and service time

Essays on Service Information, Retrials and Global Supply Chain Sourcing

In many service settings, customers have to join the queue without being fully aware of the parameters of the service provider (for e.g., customers at check-out counters may not know the true service rate prior to joining). In such blind queues\u27\u27, customers typically make their decisions based on the limited information about the service provider\u27s operational parameters from past experiences, reviews, etc. In the first essay, we analyze a firm serving customers who make decisions under arbitrary beliefs about the service parameters. We show, while revealing the service information to customers improves revenues under certain customer beliefs, it may however destroy consumer welfare or social welfare. When consumers can self-organize the timing of service visits, they may avoid long queues and choose to retry later. In the second essay, we study an observable queue in which consumers make rational join, balk and (costly) retry decisions. Retrial attempts could be costly due to factors such as transportation costs, retrial hassle and visit fees. We characterize the equilibrium under such retrial behavior, and study its welfare effects. With the additional option to retry, consumer welfare could worsen compared to the welfare in a system without retrials. Surprisingly, self-interested consumers retry too little (in equilibrium compared to the socially optimal policy) when the retrial cost is low, and retry too much when the retrial cost is high. We also explore the impact of myopic consumers who may not have the flexibility to retry. In the third essay, we propose a comprehensive model framework for global sourcing location decision process. For decades, off-shoring of manufacturing to China and other low-cost countries was a no-brainer decision for many U.S. companies. In recent years, however, this trend is being challenged by some companies to re-shore manufacturing back to the U.S., or to near-shore manufacturing to Mexico. Our model framework incorporates perspectives over the entire life cycle of a product, i.e., product design, manufacturing and delivering, and after-sale service support, and we use it to test the validity of various competing theories on global sourcing. We also provide numerical examples to support our findings from the model

Three Sojourns in Queueing Theory

In this thesis, we present three works on queues. In chapter 1, we analyze two non-work-conserving variations of the M/G/1 preemptive LIFO queue, focusing on deriving expressions for the limiting distribution of workload and related quantities. In the first model, preempted customers return to the front of the queue with a new service time, while in the second, they return with their original service time. We use queueing theory methods such as the Rate Conservation Law, PASTA, regenerative process theory and Little's Law. Our results include stability and heavy-traffic limits, as well as tail asymptotics for stationary workload. In chapter 2, we analyze a queueing model with price-sensitive customers, where the service provider aims to maximize revenue and minimize the average queue length. Customers arrive according to a Poisson process, join the queue if their willingness-to-pay exceeds the offered price, and are served in a first-in first-out manner with exponential service times. Our model is applicable to cloud computing, make-to-order manufacturing, and food delivery. We provide performance guarantees for a class of static pricing policies that can achieve a constant fraction of the optimal revenue with a small increase in expected queue length. We present results for the single-server, multi-server, and multi-class cases and provide numerical findings to demonstrate the empirical performance of our policies. In chapter 3, we analyze the Adaptive Non-deterministic Transmission Policy (ANTP), a technique addressing the Massive Access Problem (MAP) in telecommunications, which involves delaying packets at the points of origin to reduce congestion. We frame these delays as time spent at a "cafe" before proceeding to the service facility. We present sample-path results, giving conditions under which ANTP does not change the total sojourn time of packets, and results under a general stochastic framework, focusing on stability and constructing proper stationary versions of the model. We prove Harris recurrence of an underlying Markov process and find positive recurrent regeneration points under i.i.d. assumptions

Three Models for Pricing Decisions in Services or under Inventory Considerations

This dissertation studies pricing decisions under three different service settings. First, we consider a service system in which customers of two different types share the same service environment and their services are influenced by the presence of others. Specifically, when receiving services, customers interact with each other, and the effect of this interaction on the customers' utility may be positive or negative. Using a game-theoretic model, we show that in any Nash equilibrium, competing service providers will never benefit from price discrimination unless the externalities are negative and strong. With a numerical study, we find that when the two providers have small capacities, price discrimination will improve profits. However, when the two facilities have ample capacities, price discrimination might even hurt profits because of increased competition. The second setup is for a service system where competing service providers need to first perform an inspection to provide a quote to interested customers. We develop a game-theoretic model and fully characterize the equilibrium. With a numerical analysis, we find that, in equilibrium, firms might make profits mainly through the fees charged at the inspection stage. For the third setting, we consider the classical revenue management problem under inventory considerations with the additional feature that the firm has the option to bundle the product in clearance with a stable item. We prove that the optimal dynamic pricing strategy is of threshold-type, that is, it is optimal to offer a discount on the bundle when the value of an additional product is between two thresholds.Doctor of Philosoph