Revenue Optimization for a Make-to-Order Queue in an Uncertain Market Environment

We consider a revenue-maximizing make-to-order manufacturer that serves a market of price- and delay-sensitive customers and operates in an environment in which the market size varies stochastically over time. A key feature of our analysis is that no model is assumed for the evolution of the market size. We analyze two main settings: (i) the size of the market is observable at any point in time; and (ii) the size of the market is not observable and hence cannot be used for decision making. We focus on high-volume systems that are characterized by large processing capacities and market sizes, and where the latter fluctuate on a slower timescale than that of the underlying production system dynamics. We develop an approach to tackle such problems that is based on an asymptotic analysis and that yields near-optimal policy recommendations for the original system via the solution of a stochastic fluid model

Revenue Management of Reusable Resources with Advanced Reservations

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137568/1/poms12672_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137568/2/poms12672.pd

Revenue management and the welfare gap In the US airline industry

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 129-133).This thesis explores the potential improvements in Revenue Management from two distinct perspectives. The first piece of work is to explore the potential operational improvements, in which we aim to develop competitive and robust dynamic pricing rules in a market whose evolution can be highly volatile and hardly predictable. We do specifically by considering the 'classical' single product dynamic pricing problem allowing the 'scale' of demand intensity to be modulated by an exogenous 'market size' stochastic process. This is a natural model of dynamically changing market conditions. We show that for a broad family of Gaussian market size processes, simple dynamic pricing rules that are essentially agnostic to the specification of this market size process perform provably well. The pricing policies we develop are shown to compensate for forecast imperfections (or a lack of forecast information altogether) by frequent re-optimization and re-estimation of the 'instantaneous' market size. The second piece of work is to understand the potential first order changes. We choose US airline industry to investigate and measure its resource allocative efficiency. The past decade has been a difficult one for the US airline industry. On the one hand, airline profits have been highly variable with net losses over the last ten years standing in the tens of billions of dollars. On the other hand, consumers continue to complain of predatory pricing and other such tactics (See Wirtz et al. [2003]). Our goal here will simply be to get an estimate of what is possible moving forward. We approach this task from an econometric perspective: we produce a status-quo dollar estimate of total welfare in the US airline industry. We then compute a number of benchmarks that we posit are conservative estimates of what optimal welfare in the industry might look like under mechanisms resembling existing dynamic pricing practice. Our benchmark estimates will leverage a unique, proprietary data set on ticket purchases via the 'micro' BLP approach Berry et al. [2004]. We will show that the welfare gap is surprisingly large, raising the possibility that a combination of innovative selling mechanisms and legislation can make a dramatic difference to airline profitability and consumer satisfaction alike.by Yiwei Chen.Ph.D

Efficient Real-time Policies for Revenue Management Problems

This dissertation studies the development of provably near-optimal real-time prescriptive analytics solutions that are easily implementable in a dynamic business environment. We consider several stochastic control problems that are motivated by different applications of the practice of pricing and revenue management. Due to high dimensionality and the need for real-time decision making, it is computationally prohibitive to characterize the optimal controls for these problems. Therefore, we develop heuristic controls with simple decision rules that can be deployed in real-time at large scale, and then show theirs good theoretical and empirical performances. In particular, the first chapter studies the joint dynamic pricing and order fulfillment problem in the context of online retail, where a retailer sells multiple products to customers from different locations and fulfills orders through multiple fulfillment centers. The objective is to maximize the total expected profits, defined as the revenue minus the shipping cost. We propose heuristics where the real-time computations of pricing and fulfillment decisions are partially decoupled, and show their good performances compared to reasonable benchmarks. The second chapter studies a dynamic pricing problem where a firm faces price-sensitive customers arriving stochastically over time. Each customer consumes one unit of resource for a deterministic amount of time, after which the resource can be immediately used to serve new customers. We develop two heuristic controls and show that both are asymptotically optimal in the regime with large demand and supply. We further generalize both of the heuristic controls to the settings with multiple service types requiring different service times and with advance reservation. Lastly, the third chapter considers a general class of single-product dynamic pricing problems with inventory constraints, where the price-dependent demand function is unknown to the firm. We develop nonparametric dynamic pricing algorithms that do not assume any functional form of the demand model and show that, for one of the algorithm, its revenue loss compared to a clairvoyant matches the theoretic lower bound in asymptotic regime. In particular, the proposed algorithms generalize the classic bisection search method to a constrained setting with noisy observations.PHDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145995/1/leiyz_1.pd