In this dissertation, we study inventory and revenue management problems for perishable products with customer choice considerations. This dissertation is composed of six chapters. In Chapter 1, we provide an overview and the motivation of problems. Subsequently, in Chapter 2, we propose a joint inventory and pricing problem for a perishable product with two freshness levels. After a stochastic time, a fresh item turns into a non-fresh item, which will expire after another random duration. Under an (r, Q) ordering policy and a markdown pricing strategy for non-fresh items, we formulate a model that maximizes the long-run average profit rate. We then reduce the model to a mixed-integer bilinear program (MIBLP), which can be solved efficiently by state-of-the-art commercial solvers. We also investigate the value of using a markdown strategy by establishing bounds on it under limiting regimes of some parameters such as large market demand. Further, we consider an Economic Order Quantity (EOQ)-type heuristic and bound the optimality gap asymptotically. Our results reveal that although the clearance strategy is always beneficial for the retailer, it may hurt customers who are willing to buy fresh products. In Chapter 3, we extend this model to the dynamic setting with multiple freshness levels of perishable products. Due to the complexity of the problem, we study the structural properties of value function and characterize the structure of the optimal policies by using the concept of anti-multimodularity. The structural analysis enables us to devise three novel and efficient heuristic policies. We further extend the model by considering donation policy and replenishment system. Our results imply that freshness-dependent pricing and dynamic pricing are two substitute strategies, while freshness-dependent pricing and donation strategy are two complement strategies for matching supply with demand. Also, high variability in product quality under dynamic pricing benefits the firm, but it may result in significant losses with a static pricing strategy. In Chapter 4, we study a joint inventory-pricing model for perishable items with fixed shelf lives to examine the effectiveness of different markdown policies, including single-stage, multiple-stage, and dynamic markdown policies both theoretically and numerically. We show that the value of multiple-stage markdown policies over single-stage ones asymptotically vanishes as the shelf life, market demand, or customers’ maximum willingness-to-pay increase. In chapter 5, with a focus on blood products, we optimize blood supply chain structure along with the operations optimization. Specifically, we study collection, production, replenishment, issuing, inventory, wastage, and substitution decisions under three different blood supply chain channel structures, i.e., the decentralized, centralized, and coordinated. We propose a bi-level optimization program to model the decentralized system and use the Karush–Kuhn–Tucker (KKT) optimality conditions to solve that. Although centralized systems result in a higher performance than decentralized systems, it is challenging to implement them. Thus, we design a novel coordination mechanism to motivate hospitals to operate in a centralized system. We also extend the model to the case with demand uncertainty and compare different issuing and replenishment policies. Analysis of a realistic case-study indicates that integration can significantly improve the performance of the system. Finally, Chapter 6 concludes this dissertation and proposes future research directions. / Dissertation / Doctor of Philosophy (PhD)

The State and Local Fiscal Assistance Act of 1972, otherwise known as the revenue sharing act, distributes $30.2 billion to nearly 39,000 state and local governments over a five year period. The purpose of this study is to determine whether or not the actual workings of the program are consistent with the goals its supporters set for it. Other formula studies of revenue sharing begin by selecting a goal, or qoals, that revenue sharing is supposed to advance (or should advance), and then proceed to develop alternative distribution formulas which are more responsive to the predeternined goal(s). This study, by examining the structure of the distribution formula, uses a revealed preference technique to ascertain four program goals inherent in the structure of the distribution formula. / Ph. D.

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Video games represent the largest and fastest-growing segment of the entertainment industry. Despite its popularity in practice, it has received limited attention from the operations community. Managing product monetization and engagement presents unique challenges due to the characteristics of gaming platforms, where players and the gaming platform have repeated (and endogenously controlled) interactions. These practices have also led to new customer concerns and thus regulation challenges. In this thesis, we describe a body of work that provides the first analytical results for revenue management and matchmaking problems in video games, as well as the fairness issues in many e-commerce platforms. In the first part, we discuss a prevailing selling mechanism in online gaming known as a loot box. A loot box can be viewed as a random bundle of virtual items, whose contents are not revealed until after purchase. We consider how to optimally price and design loot boxes from the perspective of a revenue-maximizing video game company, and provide insights on customer surplus and protection under such selling strategies. In the second part, we consider how to manage player engagement in a game where players are repeatedly matched to compete against one another. Players have different skill levels which affect the outcomes of matches, and the win-loss record influences their willingness to remain engaged. Leveraging optimization and real data, we provide insights on how engagement may increase with optimal matchmaking policies and adding AI bots. In the third part, we consider an increasingly important concern in many e-commerce platforms: the inequality induced by price discrimination. While the practice of discriminatory pricing is generally widespread, it can result in disparate impact against protected groups. We consider the problem of setting prices for different groups of customers under fairness regulations, which limit the differences of various metrics (such as price and demand) across the groups. We show that different types of fairness constraints may not coexist in general, and the impact of fairness levels on social welfare could be non-monotonic and non-trivial.

ABSTRACT The Niger Delta region of Nigeria has in recent years been a hotbed of conflict. The region has been embroiled in turmoil over the struggle for the control of the vast resources of the region. There has arisen a controversy over the cause of the struggle. Closely associated with this controversy is the confusion over the terms ‘resource control’ and ‘increase in revenue’. Though the Supreme Court, the apex court in Nigeria has delivered a landmark judgment on the issue of resource control, the last is yet to be heard on the matter. There is a lacuna in the law as an appropriate revenue allocation formula is yet to be fixed for Nigeria. This work examines whether the people of the Niger Delta region of Nigeria are entitled to the right to resource control. It will examine the Supreme Court’s decision in A.G. Federation v. A.G. Abia & 35 ors. and discuss on the possibility of fashioning out an acceptable revenue allocation formula for Nigeria.

In this dissertation, I study two topics in the context of revenue management. The First topic involves building a mathematical model to analyze the competition between many retailers who can change the price of their respective products in real time. I develop a game-theoretic model for the dynamic price competition where each retailer's objective is to maximize its own expected total revenue. I use the Nash equilibrium to predict market equilibrium and provide managerial insights into how each retailer should take into account its competitors' behavior when setting the price. The second topic involves working with Amtrak, the national railroad passenger corporation, to develop a revenue management model. The revenue management department of Amtrak provides the sales data of Auto Train, a service of Amtrak that allows passengers to bring their vehicles on the train. I analyze the demand structure from sales data and build a mathematical model to describe the sales process for Auto Train. I further develop an algorithm to calculate the optimal pricing strategy that yields the maximum revenue. Because of the distinctive service provided by Auto Train, my findings make important contribution to the revenue management literature. / Ph. D.

Section 103(b)(4)(A) the Internal Revenue Code was examined to determine its e££ectiveness in helping to achieve the goal of the federal government's low income housing policy--"a decent home and a suitable living environment for every American family." A theoretical analysis of the general excise subsidy model on which this program is based highlighted certain empirical factors on which to focus to determine the potential effectiveness of the program. A theoretical analysis of the particular mechanism used resulted in a measure of effectiveness of providing a subsidy through tax-exempt bond financing. Empirical analysis basically showed that the mechanism is ineffective. Therefore, recent recommendations to abolish Section 103(b)(4)(A) are sound. / M.A.

This thesis presents a dynamic pricing model where a seller offers two types of a generic product to a random number of customers. Customers show up sequentially. When a customer arrives, he will ---depending on the prices---either purchase one unit of type 1 product or one unit of type 2 product, or will leave empty-handed. The sale ends either when the entire stock is sold out, or when the customers are exhausted. The seller's task is to post the optimal prices for the two product types to each customer to maximize the expected total revenue. We use dynamic programming to formulate this problem, and derive the optimal policy for special cases. For general cases, we develop an algorithm to approximate the optimal policy and use numerical examples to demonstrate the efficiency of the algorithm. Finally, we apply the results to a continuous-time model where customers arrive according to a Poisson process. We develop a heuristic policy and use numerical examples to show the heuristic policy is very effective. / Master of Science

The subscription economy — wherein firms offer products and services for recurring fees — has witnessed substantial growth in the last two decades. When valuing firms that rely on recurring revenue (hereafter recurring revenue firms), investors adopt valuation methods that prioritize future revenue over current performance, altering the earnings management incentives for these firms. I first document fundamental differences in recurring revenue firms: they tend to be smaller and younger, and they have greater revenue persistence, investment efficiency, and profitability. They experience more pronounced stock market reactions to revenue and earnings, but only when future revenue indicators (deferred revenue) are high. To align with growth-focused investor valuation methods, recurring revenue firms avoid premature revenue recognition to maintain a high level of deferred revenue. Instead, they cut discretionary expenses to meet earnings targets and excessively defer revenue to enhance their valuation. These insights underscore how earnings management incentives evolve in response to the changing economy.