airline revenue management

With the increasing interest in decision support systems and the continuous advance of computer science, revenue management is a discipline which has received a great deal of interest in recent years. Although revenue management has seen many new applications throughout the years, the main focus of research continues to be the airline industry. Ever since Littlewood (1972) first proposed a solution method for the airline revenue management problem, a variety of solution methods have been introduced. In this paper we will give an overview of the solution methods presented throughout the literature.revenue management;seat inventory control;OR techniques;mathematical programming

airline revenue management

With the increasing interest in decision support systems and the continuous advance of computer science, revenue management is a discipline which has received a great deal of interest in recent years. Although revenue management has seen many new applications throughout the years, the main focus of research continues to be the airline industry. Ever since Littlewood (1972) first proposed a solution method for the airline revenue management problem, a variety of solution methods have been introduced. In this paper we will give an overview of the solution methods presented throughout the literature

Overbooking in airline revenue management

Master'sMASTER OF SCIENC

Demand forecasting in a railway revenue management system

The research in Revenue Management has tightly focused on airline markets and somewhat neglected other similar markets. The purpose of this thesis is to offer an extensive overview on RM in the railway context. The backgrounds and concepts of RM are discussed and the applicability of RM in railway markets is evaluated. The differences between railways and airlines are also explored. I am especially focused on the demand forecasting process and different methods that can be used to forecast uncertain demand for a specific train. I also discuss how demand forecasting relates to other RM components, such as capacity allocation. Relevant RM theories and demand forecasting methods are compiled based on the existing literature. Because of the limited availability of real demand data, I use hypothetical demand data to illustrate how different forecasting methods can be applied and how the performance of each method can be evaluated. I also compile an illustrative capacity allocation example using EMSR -model. I conclude that the applicability of RM in railway markets is evident. I find four significant differences between railways and airlines that are relevant to RM. Railways tend to have more complex networks, less price differentiation, shorter booking lead times, and less competitive markets. Illustrative demand forecasting examples indicate that the evaluation of different forecasting methods is essential, since the performance of different methods might vary substantially, depending on the available data and the time horizon of desired forecast. Capacity allocation examples suggest that it is particularly important that demand forecasts would provide the accurate predictions of total demand and demand distribution between fare classes. However, it should be taken into account that the findings of illustrative examples cannot be generalized, since the hypothetical data was used in the analysis. Thus further examination with real demand data should be required. Additionally, the issues of constrained data and network effect are omitted from the analysis

Open loop policies for single-leg air-cargo revenue management

Transporting cargo is a significant source of revenue in the airline industry. It is therefore of critical importance to develop booking policies that address the unique challenges presented by the cargo business: the capacity is multi-dimensional, generally measured in terms of volume and weight, and the exact capacity requirements of a shipment are usually not known with certainty at the time of making booking decisions. Operations research methods have proven highly useful in passenger revenue management to effectively allocate a limited capacity while considering the trade-off between the penalty costs for oversold capacity and the opportunity costs for having unused capacity at the departure time. In this thesis, we develop similar methods for the capacity control problem over a single-leg flight with multiple cargo types. We study open loop policies that accept or reject a booking request for a certain type of cargo shipment based on booking limits or bid-prices. In order to compute suitable booking limits and bid-prices, we develop optimization models that incorporate off-loading costs under uncertain volume and weight requirements. We conduct a comprehensive computational study to evaluate the effectiveness of our proposed models. Numerical results demonstrate that our policies perform well compared to benchmarks established by various methods in the literature

The evolution of airline distribution channels and their effects on revenue management performance

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.Includes bibliographical references (p. 132-134).Over the past ten years, the development of more advanced computer systems and the growth in the use of the Internet have led to numerous changes in airline ticket distribution strategies. For example, the use of websites for booking and ticketing air travel continues to increase, and the Internet is often cited as the preferred model for a low-cost distribution channel. At the same time, Network Revenue Management methods are now viewed as a key tool for airlines to maximize revenue in an increasingly competitive marketplace. These new systems and tools have helped the airlines achieve record profits in the strong economy of the late 1990s, but these profits may have masked hidden costs of using the new technology. Examples of hidden costs include the added computational burden of increased search engine requests to the computer reservations system as well as the increased opportunity for automated systems to bypass the booking limits set by the revenue management system. Such costs have yet to be examined and quantified in an academic research effort. The purpose of this thesis research is to understand a variety of issues related to how the technologies of more advanced distribution channels and more sophisticated revenue management systems interact with each other and impact air travel providers.(cont.) First, an empirical analysis of ticketing data is used to demonstrate that there are significant differences in ticket purchasing behavior among customers who use different distribution channels. Second, a review of previous experiments showing the negative revenue impacts of Inventory Control Bypass are presented, together with a discussion of some of the more promising solutions to Bypass. Next, these prior results are compared to a new set of experiments covering both path-based and leg-based Caching techniques. The new experiments show that the negative revenue impacts of Caching are at least as serious as those of Bypass, and may be more serious, depending on an airline's choice of how to interface with distributors who cache.by Diana M. Dorinson.S.M

Revenue management in airline operations : booking systems and aircraft maintenance services

Although the principles of Revenue Management (RM) have vaguely been used in business for a long time, an increasing number of organizations are implementing well structured RM systems in the last few decades due to the developments in science and technology, especially in economics, statistics, operations research and computer science. The improvements in information and telecommunication technologies, wide use of Internet, rise of e-commerce and successful supply chain management strategies have enabled organizations to model and solve complex RM problems. This dissertation research concentrates on airlines, the earliest and leading user of RM. Today, airlines face serious financial problems due to the increasing costs and competition. They continuously explore new opportunities especially in terms of RM to make profit and survive. In this study, two problems are analyzed within this scope; airline booking process with adapted options approach and aircraft maintenance order control through RM. First; a new approach, financial options approach, is proposed to sell tickets in airline reservation systems. The options are used to overcome the uncertainty in air travel demand and competitors' actions. The seat inventory control problem is formulated with overbooking and embedded options respectively. Then a simulation study is conducted the potential of using options in airlines booking process. Accordingly, empirical results show that they present an opportunity both to utilize capacity more efficiently and to value seats more precisely compared to overbooking approach. Secondly; a peak load pricing concept is applied for aircraft maintenance order control problem. Aircraft maintenance centers face with peak loads in some seasons and the capacity is underutilized in other seasons. A peak load pricing model is proposed to shift some of the price elastic demand from peak seasons to off-peak seasons to balance demand and supply around the year. A dynamic programming algorithm is developed to solve the model and a code is written in C++. Results show that the model improves both annual capacity loading factors and revenues without causing a discomfort from the perspective of the customers. The details of both studies are presented in this dissertation research. [PUBLICATION ABSTRACT

Dynamic pricing under customer choice behavior for revenue management in passenger railway networks

Revenue management (RM) for passenger railway is a small but active research field with an increasing attention during the past years. However, a detailed look into existing research shows that most of the current models in theory rely on traditional RM techniques and that advanced models are rare. This thesis aims to close the gap by proposing a state-of-the-art passenger railway pricing model that covers the most important properties from practice, with a special focus on the German railway network and long-distance rail company Deutsche Bahn Fernverkehr (DB). The new model has multiple advantages over DB’s current RM system. Particularly, it uses a choice-based demand function rather than a traditional independent demand model, is formulated as a network model instead of the current leg-based approach and finally optimizes prices on a continuous level instead of controlling booking classes. Since each itinerary in the network is considered by multiple heterogeneous customer segments (e.g., differentiated by travel purpose, desired departure time) a discrete mixed multinomial logit model (MMNL) is applied to represent demand. Compared to alternative choice models such as the multinomial logit model (MNL) or the nested logit model (NL), the MMNL is significantly less considered in pricing research. Furthermore, since the resulting deterministic multi-product multi-resource dynamic pricing model under the MMNL turns out to be non- linear non-convex, an open question is still how to obtain a globally optimal solution. To narrow this gap, this thesis provides multiple approaches that make it able to derive a solution close to the global optimum. For medium-sized networks, a mixed-integer programming approach is proposed that determines an upper bound close to the global optimum of the original model (gap < 1.5%). For large-scale networks, a heuristic approach is presented that significantly decreases the solution time (by factor up to 56) and derives a good solution for an application in practice. Based on these findings, the model and heuristic are extended to fit further price constraints from railway practice and are tested in an extensive simulation study. The results show that the new pricing approach outperforms both benchmark RM policies (i.e., DB’s existing model and EMSR-b) with a revenue improvement of approx. +13-15% over DB’s existing approach under a realistic demand scenario. Finally, to prepare data for large-scale railway networks, an algorithm is presented that automatically derives a large proportion of necessary data to solve choice-based network RM models. This includes, e.g., the set of all meaningful itineraries (incl. transfers) and resources in a network, the corresponding resource consumption and product attribute values such as travel time or number of transfers. All taken together, the goal of this thesis is to give a broad picture about choice-based dynamic pricing for passenger railway networks

Re-Solving Stochastic Programming Models for Airline Revenue Management

We study some mathematical programming formulations for the origin-destination model in airline revenue management. In particular, we focus on the traditional probabilistic model proposed in the literature. The approach we study consists of solving a sequence of two-stage stochastic programs with simple recourse, which can be viewed as an approximation to a multi-stage stochastic programming formulation to the seat allocation problem. Our theoretical results show that the proposed approximation is robust, in the sense that solving more successive two-stage programs can never worsen the expected revenue obtained with the corresponding allocation policy. Although intuitive, such a property is known not to hold for the traditional deterministic linear programming model found in the literature. We also show that this property does not hold for some bid-price policies. In addition, we propose a heuristic method to choose the re-solving points, rather than re-solving at equally spaced times as customary. Numerical results are presented to illustrate the effectiveness of the proposed approach