Reverse Auction in Pricing Model

Dynamic price discrimination adjusts prices based on the option value of future sales, which varies with time and units available. This paper surveys the theoretical literature on dynamic price discrimination, and confronts the theories with new data from airline pricing behavior, Consider a multiple booking class airline-seat inventory control problem that relates to either a single flight leg or to multiple flight legs. During the time before the flight, the airline may face the problems of (1) what are the suitable prices for the opened booking classes, and (2) when to close those opened booking classes. This work deals with these two problems by only using the pricing policy. In this paper, a dynamic pricing model is developed in which the demand for tickets is modeled as a discrete time stochastic process. An important result of this work is that the strategy for the ticket booking policy can be reduced to sets of critical decision periods, which eliminates the need for large amounts of data storage

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

Stochastic programming for multiple-leg network revenue management

Airline seat inventory control is a very profitable tool in the airline industry. Mathematical programming models provide booking limits or bid-prices for all itineraries and fare classes based on demand forecasts. But the actual revenue generated in the booking process fails to meet expectations. Simple deterministic models based on expected demand generate better revenue than more advanced probabilistic models. Recently suggested models put even more effort into demand forecasting. We will show that the dynamic booking process rather than the demand forecasts needs to be addressed. In particular the nesting strategies applied in booking control will counter-effect the profitability of advanced estimation of booking limits and bid-prices.simulation;revenue management;mathematical programming

Airline Revenue Management with Shifting Capacity

Airline revenue management is the practice of controlling the booking requests such that the planes are filled with the most profitable passengers. In revenue management the capacities of the business and economy class sections of the plane are traditionally considered to be fixed and distinct capacities. In this paper, we give up this notion and instead consider the use of convertible seats. A row of these seats can be converted from business class seats to economy class seats and vice versa. This offers an airline company the possibility to adjust the capacity configuration of the plane to the demand pattern at hand. We show how to incorporate the shifting capacity opportunity into a dynamic, network-based revenue management model. We also extend the model to include cancellations and overbooking. With a small test case we show that incorporating the shifting capacity opportunity into the revenue management decision indeed provides a means to improve revenues.convertible seats;dynamic capacity management;revenue management;seat inventory control;shifting capacity

Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights

Overbooking is one of the most vital revenue management practices that is used in the airline industry. Identification of an overbooking level is a challenging task due to the uncertainties associated with external factors, such as demand for tickets, and inappropriate overbooking levels which may cause revenue losses as well as loss of reputation and customer loyalty. Therefore, the aim of this paper is to propose a fuzzy linear programming model and Genetic Algorithms (GAs) to maximize the overall revenue of a large-scale multi-leg flight network by minimizing the number of empty seats and the number of denied passengers. A fuzzy logic technique is used for modeling the fuzzy demand on overbooking flight tickets and a metaheuristics-based GA technique is adopted to solve large-scale multi-leg flights problem. As part of model verification, the proposed GA is applied to solve a small multi-leg flight linear programming model with a fuzzified demand factor. In addition, experimentation with large-scale problems with different input parameters’ settings such as penalty rate, show-up rate and demand level are also conducted to understand the behavior of the developed model. The validation results show that the proposed GA produces almost identical results to those in a small-scale multi-leg flight problem. In addition, the performance of the large-scale multi-leg flight network represented by a number of KPIs including total booking, denied passengers and net-overbooking profit towards changing these input parameters will also be revealed

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

Single-leg airline revenue management with overbooking

Airline revenue management is about identifying the maximum revenue seat allocation policies. Since a major loss in revenue results from cancellations and no-show passengers, over the years overbooking has received a significant attention in the literature. In this study, we propose new models for static and dynamic single-leg overbooking problems. In the static case, we introduce computationally tractable models that give upper and lower bounds for the optimal expected revenue. In the dynamic case, we propose a new dynamic programming model, which is based on two streams of arrivals. The first stream corresponds to the booking requests and the second stream represents the cancellations. We also conduct simulation experiments to illustrate the proposed models and the solution methods