Traditional Revenue Management Research Articles

Optimization of mixed fare structures: Theory and applications

This paper develops a theory for optimizing revenue through seat inventory control that can be applied in a variety of airline fare structures, including those with less restricted and fully undifferentiated fare products that have become more common in the recent past. We describe an approach to transform the fares and the demand of a general discrete choice model to an equivalent independent demand model. The transformation and resulting fare adjustment approach is valid for both static and dynamic optimization and extends to network revenue management applications. This transformation allows the continued use of the optimization algorithms and seat inventory control mechanisms of traditional revenue management systems, developed more than two decades ago under the assumption of independent demands for fare classes.

A Column Generation Algorithm for Choice-Based Network Revenue Management

During the past few years, there has been a trend to enrich traditional revenue management models built upon the independent demand paradigm by accounting for customer choice behavior. This extension involves both modeling and computational challenges. One way to describe choice behavior is to assume that each customer belongs to a segment, which is characterized by a consideration set, i.e., a subset of the products provided by the firm that a customer views as options. Customers choose a particular product according to a multinomial-logit criterion, a model widely used in the marketing literature. In this paper, we consider the choice-based, deterministic, linear programming model (CDLP) of Gallego et al. (2004) [Gallego, G., G. Iyengar, R. Phillips, A. Dubey. 2004. Managing flexible products on a network. Technical Report CORC TR-2004-01, Department of Industrial Engineering and Operations Research, Columbia University, New York], and the follow-up dynamic programming decomposition heuristic of van Ryzin and Liu (2008) [van Ryzin, G. J., Q. Liu. 2008. On the choice-based linear programming model for network revenue management. Manufacturing Service Oper. Management 10(2) 288–310]. We focus on the more general version of these models, where customers belong to overlapping segments. To solve the CDLP for real-size networks, we need to develop a column generation algorithm. We prove that the associated column generation subproblem is indeed NP-hard and propose a simple, greedy heuristic to overcome the complexity of an exact algorithm. Our computational results show that the heuristic is quite effective and that the overall approach leads to high-quality, practical solutions.

Application of revenue management practices to the theme park industry

Revenue management (RM) has been an essential strategy to maximize revenue for many capacity-limited service industries. Considering the common industry characteristics of traditional RM industries, the nature of the theme park industry suggests potential for enhancing revenue by exercising a variety of RM techniques. This study suggests practices for theme park operators for successful RM application. In addition, this study examines how customers perceive RM practice in the theme park industry compared to a traditional RM industry, hotel industry. The findings indicate that customers seem to perceive RM practice in the theme park industry as relatively fair practices as similarly perceived for the hotel industry. The findings are encouraging for the theme park industry because a relatively similar level of its customers’ perceived fairness of the RM practice compared to the hotel industry suggests that adoption and implementation of the RM practice has great potential to become successful as it has been in traditional RM industries, such as hotels.

On the Choice-Based Linear Programming Model for Network Revenue Management

Gallego et al. [Gallego, G., G. Iyengar, R. Phillips, A. Dubey. 2004. Managing flexible products on a network. CORC Technical Report TR-2004-01, Department of Industrial Engineering and Operations Research, Columbia University, New York.] recently proposed a choice-based deterministic linear programming model (CDLP) for network revenue management (RM) that parallels the widely used deterministic linear programming (DLP) model. While they focused on analyzing “flexible products”—a situation in which the provider has the flexibility of using a collection of products (e.g., different flight times and/or itineraries) to serve the same market demand (e.g., an origin-destination connection)—their approach has broader implications for understanding choice-based RM on a network. In this paper, we explore the implications in detail. Specifically, we characterize optimal offer sets (sets of available network products) by extending to the network case a notion of “efficiency” developed by Talluri and van Ryzin [Talluri, K. T., G. J. van Ryzin. 2004. Revenue management under a general discrete choice model of consumer behavior. Management Sci. 50 15–33.] for the single-leg, choice-based RM problem. We show that, asymptotically, as demand and capacity are scaled up, only these efficient sets are used in an optimal policy. This analysis suggests that efficiency is a potentially useful approach for identifying “good” offer sets on networks, as it is in the case of single-leg problems. Second, we propose a practical decomposition heuristic for converting the static CDLP solution into a dynamic control policy. The heuristic is quite similar to the familiar displacement-adjusted virtual nesting (DAVN) approximation used in traditional network RM, and it significantly improves on the performance of the static LP solution. We illustrate the heuristic on several numerical examples.

A revenue management system which can learn by itself

Most traditional revenue management systems do not directly learn from past mistakes in setting future inventory control policies. Instead, these systems rely on feedback loops created by forecasting and optimisation models and the resulting impacts on observed passenger behaviour. In this paper, we propose to apply the concept of ‘reinforcement learning’, borrowed from the field of industrial engineering, to create a revenue management system that can learn as it goes.

Table-Games Revenue Management

The application of survival analysis to create a revenue management scheme for a casino’s table games is illustrated. Although logic suggests that table games revenue can easily be increased by manipulating minimum wagers, the matter is complicated by the nature of games, which allow “partial sales” (i.e., any number of hands) but do not allow a bet of zero, and in which actual demand is censored (when people are waiting to play). Survival analysis accommodates the time-played parameter while recognizing an inherent issue with capacity constrained supply. Optimization is then achieved by using traditional revenue-management tools, as demonstrated using a simulated data set.

Expert opinions: Current pricing and revenue management practice across U.S. industries

On May 18, 2006, the second annual Revenue Management and Price Optimization conference was held at the Georgia Institute of Technology. The theme of the conference was on how the Internet is changing traditional revenue management and pricing practices. The conference brought together experts and thought leaders from more than 30 companies spanning airlines, hotels, car rentals, cruise lines, apartment rentals, aircraft manufacturing, retailing, distribution, e-mail marketing, on-line travel, logistics, sports, performing arts, software providers, and others. This paper summarises the key discussions from this conference and synthesises experts' perspectives on near-term opportunities and challenges facing their industries.

(Realtime) dynamic pricing in an integrated revenue management and pricing environment: An approach to handling undifferentiated fare structures in low-fare markets

This paper compares the traditional pricing procedure of network carriers using fare products defined by rules and restriction with the concept of dynamically adjusting prices based on current market conditions. It describes why traditional revenue management procedures and algorithms fail in a fenceless environment typical of low-fare carriers. The paper attempts to explain how a dynamic pricing concept based on willingness to pay could form the basis of a long-term revenue management solution for various types of airlines, from low-fare carriers to network carriers. The concept requires significantly more integrated business processes between pricing and revenue management and therefore demands a new and future-oriented approach to revenue optimisation.

Practice Papers: The science of revenue management when passengers purchase the lowest available fare

The changing business environment is making ticket price an ever more important consideration in passenger purchasing behaviour. This paper discusses how traditional revenue management models change as the ability to segment passengers deteriorates and passengers focus on price. The concepts are applicable to most industries where revenue management is practised.

Bmi's response to the changing European airline marketplace

The purpose of this paper is to demonstrate bmi's pricing and revenue management response to the changing European airline marketplace. Prompted by the low-cost carriers, bmi challenged the traditional full service airline pricing and revenue management practices and, as a result, changed its business to a one-way, restriction-free environment in spring 2002. Traditional revenue management optimisation techniques and performance measurement are no longer valid under such a pricing model — classes are no longer independent entities; rather there is a single fare on a flight at any one time. The revenue management problem is now one of how to calculate the optimum fare to charge at any point pre-departure, and various methods have been adopted by bmi to manage this process.

Integrating customer relationship management and revenue management: A hotel perspective

A number of authors have advocated a move towards integrating customer relationship management (CRM) and revenue management (RM). The implications of integrating CRM and RM strategies in the context of the hotel environment, however, have received little attention. The key questions that need to be addressed are: who should be targeted with CRM efforts and how will those efforts affect the RM process? This paper examines the relationship between CRM and RM. By means of the lifetime/profitability approach to customer segmentation proposed by Reinartz and Kumar in 2002, the appropriate customer segments to target with CRM efforts are identified and a supporting RM strategy is outlined for each segment. These include traditional RM, lifetime value-based pricing, availability guarantees and short-term and ad hoc promotions. The impact of these RM strategies on business processes in relation to customer segmentation, demand forecasting, information systems management and human resource management is addressed.

Optimal Dynamic Auctions for Revenue Management

We analyze a dynamic auction, in which a seller with C units to sell faces a sequence of buyers separated into T time periods. Each group of buyers has independent, private values for a single unit. Buyers compete directly against each other within a period, as in a traditional auction, and indirectly with buyers in other periods through the opportunity cost of capacity assessed by the seller. The number of buyers in each period, as well as the individual buyers' valuations, are random. The model is a variation of the traditional single leg, multiperiod revenue management problem, in which consumers act strategically and bid for units of a fixed capacity over time. For this setting, we prove that dynamic variants of the first-price and second-price auction mechanisms maximize the seller's expected revenue. We also show explicitly how to compute and implement these optimal auctions. The optimal auctions are then compared to a traditional revenue management mechanism—in which list prices are used in each period together with capacity controls—and to a simple auction heuristic that consists of allocating units to each period and running a sequence of standard, multiunit auctions with fixed reserve prices in each period. The traditional revenue management mechanism is proven to be optimal in the limiting cases when there is at most one buyer per period, when capacity is not constraining, and asymptotically when the number of buyers and the capacity increases. The optimal auction significantly outperforms both suboptimal mechanisms when there are a moderate number of periods, capacity is constrained, and the total volume of sales is not too large. The benefit also increases when variability in the dispersion in buyers' valuations or in the number of buyers per period increases.

Optimal Dynamic Auctions for Revenue Management

We analyze a dynamic auction, in which a seller with C units to sell faces a sequence of buyers separated into T time periods. Each group of buyers has independent, private values for a single unit. Buyers compete directly against each other within a period, as in a traditional auction, and indirectly with buyers in other periods through the opportunity cost of capacity assessed by the seller. The number of buyers in each period, as well as the individual buyers’ valuations, are random. The model is a variation of the traditional singleleg, multiperiod revenue management problem, in which consumers act strategically and bid for units of a fixed capacity over time. For this setting, we prove that dynamic variants of the first-price and second-price auction mechanisms maximize the seller’s expected revenue. We also show explicitly how to compute and implement these optimal auctions. The optimal auctions are then compared to a traditional revenue management mechanism—in which list prices are used in each period together with capacity controls—and to a simple auction heuristic that consists of allocating units to each period and running a sequence of standard, multiunit auctions with fixed reserve prices in each period. The traditional revenue management mechanism is proven to be optimal in the limiting cases when there is at most one buyer per period, when capacity is not constraining, and asymptotically when the number of buyers and the capacity increases. The optimal auction significantly outperforms both suboptimal mechanisms when there are a moderate number of periods, capacity is constrained, and the total volume of sales is not too large. The benefit also increases when variability in the dispersion in buyers’ valuations or in the number of buyers per period increases. (Optimal Auction; Strategic Behavior; Revenue Management; Dynamic Programming; Mechanism Design)