This survey reviews the forty-year history of research on transportation revenue management (also known as yield management). We cover developments in forecasting, overbooking, seat inventory control, and pricing, as they relate to revenue management, and suggest future research directions. The survey includes a glossary of revenue management terminology and a bibliography of over 190 references.

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Revenue Management: Research Overview and Prospects

http://cepac.cheme.cmu.edu/pasilectures/sahinidis/NVSpaperthree.pdf

Optimization under uncertainty: state-of-the-art and opportunities

The benefits of dynamic pricing methods have long been known in industries, such as airlines, hotels, and electric utilities, where the capacity is fixed in the short-term and perishable. In recent years, there has been an increasing adoption of dynamic pricing policies in retail and other industries, where the sellers have the ability to store inventory. Three factors contributed to this phenomenon: (1) the increased availability of demand data, (2) the ease of changing prices due to new technologies, and (3) the availability of decision-support tools for analyzing demand data and for dynamic pricing. This paper constitutes a review of the literature and current practices in dynamic pricing. Given its applicability in most markets and its increasing adoption in practice, our focus is on dynamic (intertemporal) pricing in the presence of inventory considerations.

Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions

Customer choice behavior, such as buy-up and buy-down, is an important phenomenon in a wide range of revenue management contexts. Yet most revenue management methodologies ignore this phenomenon - or at best approximate it in a heuristic way. In this paper, we provide an exact and quite general analysis of this problem. Specifically, we analyze a single-leg reserve management problem in which the buyers' choice behavior is modeled explicitly. The choice model is very general, simply specifying the probability of purchase for each fare product as a function of the set of fare products offered. The control problem is to decide which subset of fare products to offer at each point in time. We show that the optimal policy for this problem has a quite simple form. Namely, it consists of identifying an ordered family of "efficient" subsets S 1 ,..., S m , and at each point in time opening one of these sets S k , where the optimal index k is increasing in the remaining capacity x and decreasing in the remaining time. That is, the more capacity (or less time) available, the further the optimal set is along this sequence. We also show that the optimal policy is a nested allocation policy if and only if the sequence of efficient sets is nested, that is S 1 ? S 2 ?... ? S m . Moreover, we give a characterization of when nesting by fare order is optimal. We also develop an estimation procedure for this setting based on the expectation-maximization (EM) method that jointly estimates arrival rates and choice model parameters when no-purchase outcomes are unobservable. Numerical results are given to illustrate both the model and estimation procedure.

/pdf/revenue-management-under-a-general-discrete-choice-model-of-2vdsjyi74w.pdf

Revenue Management Under a General Discrete Choice Model of Consumer Behavior

A system and method for implementing a customer tracking and recognition program that encompasses customers' gaming and non-gaming activity alike at a plurality of affiliated casino properties. Customer information is accumulated at each affiliated casino through one or more LAN-based management systems, updated to a central patron database (CPDB) that is coupled to each casino LAN through a WAN, and made available to each affiliated casino property as needed. Customer accounts are automatically activated and provided with data from the CPDB when a customer from one casino property first visits an affiliated casino property. Customer accounts are updated with new activity data whenever a management system associated with the casino receives customer data from input devices, such as card readers, workstations, and dumb terminals, located at various venues throughout the casino. Customers are awarded points, based on their tracked activity at all affiliated casino properties. The point awards have a monetary value and are redeemable for gifts, meals, cash and the like, at any of the casino properties. The point awards may embody different promotional schemes in which point awards are adjusted to target different casino properties or different venues within a casino. Summary customer data, including point levels, is regularly updated to reflect ongoing customer activity at the casino property. This data is made available to employees at any affiliated casino property, as needed, to personalize customer services.

National customer recognition system and method

This paper studies intertemporal pricing policies when selling seasonal products in retail stores. We first present a continuous time model where a seller faces a stochastic arrival of customers with different valuations of the product. For this model, we characterize the optimal pricing policies as functions of time and inventory. We use this model as a benchmark against which we compare more realistic models that consider periodic pricing reviews. We show that the structure of the optimal pricing policies in this case is consistent with the procedures observed in practice; retail stores successively discount the product during the season and promote a liquidation sale at the end of the planning horizon. We also show that the loss experienced when implementing periodic pricing reviews instead of continuous policies is small when the appropriate number of reviews is chosen. Several interesting economic insights emerge from our analysis. For example, uncertainty in the demand for new products leads to higher prices, larger discounts, and more unsold inventory. Finally, we study the effect of announced discount policies on prices and profits. We show that stores that have adopted this type of strategy usually set prices such that with high probability the merchandise is sold during the first periods and the largest discounts rarely take place.

Periodic Pricing of Seasonal Products in Retailing

Catalog sales are among the fastest growing businesses in the U.S. The most important asset a company in this industry has is its list of customers, called the house list. Building a house list is expensive, since the response rate of names from rental lists is low. Cash management therefore plays a central role in this capital intensive business.

This paper studies optimal mailing policies in the catalog sales industry when there is limited access to capital. We consider a stochastic environment given by the random responses of customers and a dynamic evolution of the house list. Given the size of real problems, it is impossible to compute the optimal solutions. We therefore develop a heuristic based on the optimal solutions of simplified versions of the problem. The performance of this heuristic is evaluated by comparing its outcome with an upper bound derived for the original problem. Computational experiments show that it behaves satisfactorily.

The methodology presented permits the evaluation of potential catalog ventures thus proving useful to entrepreneurs in this industry.

Mailing Decisions in the Catalog Sales Industry

In this paper we study optimal strategies for renting hotel rooms when there is a stochastic and dynamic arrival of customers from different market segments. We formulate the problem as a stochastic and dynamic programming model and characterize the optimal policies as functions of the capacity and the time left until the end of the planning horizon. We consider three features that enrich the problem: we make no assumptions concerning the particular order between the arrivals of different classes of customers; we allow for multiple types of rooms and downgrading; and we consider requests for multiple nights. We also consider implementations of the optimal policy. The properties we derive for the optimal solution significantly reduce the computational effort needed to solve the problem, yet in the multiple product and/or multiple night case this is often not enough. Therefore, heuristics, based on the properties of the optimal solutions, are developed to find "good" solutions for the general problem. We also derive upper bounds which are useful when evaluating the performance of the heuristics. Computational experiments show a satisfactory performance of the heuristics in a variety of scenarios using real data from a medium size hotel.

An Application of Yield Management to the Hotel Industry Considering Multiple Day Stays

In this paper we propose a methodology to set prices of perishable items in the context of a retail chain with coordinated prices among its stores and compare its performance with actual practice in a real case study. We formulate a stochastic dynamic programming problem and develop heuristic solutions that approximate optimal solutions satisfactorily. To compare this methodology with current practices in the industry, we conducted two sets of experiments using the expertise of a product manager of a large retail company in Chile. In the first case, we contrast the performance of the proposed methodology with the revenues obtained during the 1995 autumn-winter season. In the second case, we compare it with the performance of the experienced product manager in a "simulation-game" setting. In both cases, our methodology provides significantly better results than those obtained by current practices.

http://people.stern.nyu.edu/rcaldent/papers/ClearanceOR98.pdf

Susana Mondschein

Papers

Periodic Pricing of Seasonal Products in Retailing

Mailing Decisions in the Catalog Sales Industry

An Application of Yield Management to the Hotel Industry Considering Multiple Day Stays

Coordinating Clearance Markdown Sales of Seasonal Products in Retail Chains

Coordinating clear-ance markdown sales of seasonal products in retail chains