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

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

Modeling and optimization of single-leg multi-fare class overbooking problem: the case of Ethiopian Airlines

Revenue Management, also known as yield management, is a technique used by airline industries to maximize revenue by allocating the available seats to the right customers at the right price. Overbooking is an airline revenue management technique that enables airlines to sell more seats than available in order to account for the fact that some of the passengers may not show-up or cancel their flights on the departure day. The objective of this thesis is to develop an overbooking model for a single-leg multi-fare class flight considering a realistic distribution of no-show data collected from the Ethiopian airlines. The overbooking model developed considers the interaction (i.e. the transfer of an extra passenger in a lower fare classes to higher fare class empty seat) between classes that may exist during boarding time. Moreover, this work investigates the economic rationale behind the no overbooking policy used by Ethiopian airlines for some of its flights. The overbooking model developed was solved using both a closed form approach using derivatives and Monte Carlo simulation with a derivative free optimization algorithm. A comparison of the revenue generated from no-overbooking policy, the closed form solution, and the Monte Carlo simulation solution approach shows that the Monte Carlo simulation solution approach performs well. Generally, the numerical results show that the overbooking model is effective in determining the optimal number of overbooking for a number of classes and a variety of compensation cost plans

Modeling and optimization of the single-leg multi-fare class overbooking problem

This paper presents a static overbooking model for a single-leg multi-fare class flight. A realistic distribution of no-show data in modeling the cost function was considered using data collected from the Ethiopian airlines. The overbooking model developed considers the interaction (i.e. the transfer of an extra passenger in a lower fare classes to higher fare class empty seat) between classes that may exist during boarding time. Furthermore, the overbooking problem is modelled in such a way that it could be  constrained by user defined constraints such as probability of loss of the revenue. The overbooking model developed was solved using derivatives that give a closed form  expression and Monte Carlo simulation with a derivative free optimization algorithm. A comparison of the revenue generated from no-overbooking policy, the closed form  solution, and the Monte Carlo simulation solution approach shows that the Monte Carlo simulation solution approach performs better. Generally, the numerical results show that the overbooking model is effective in determining the optimal number of overbooking for a number of classes and a variety of compensation cost plans.Keywords: Overbooking, Monte Carlo Simulation, Nelder Mead algorithm, Revenue management, Ethiopian Airline, Ethiopia

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

Network revenue management with product-specific no-shows

Revenue management practices often include overbooking capacity to account for customers who make reservations but do not show up. In this paper, we consider the network revenue management problem with no-shows and overbooking, where the show-up probabilities are specific to each product. No-show rates differ significantly by product (for instance, each itinerary and fare combination for an airline) as sale restrictions and the demand characteristics vary by product. However, models that consider no-show rates by each individual product are difficult to handle as the state-space in dynamic programming formulations (or the variable space in approximations) increases significantly. In this paper, we propose a randomized linear program to jointly make the capacity control and overbooking decisions with product-specific no-shows. We establish that our formulation gives an upper bound on the optimal expected total profit and our upper bound is tighter than a deterministic linear programming upper bound that appears in the existing literature. Furthermore, we show that our upper bound is asymptotically tight in a regime where the leg capacities and the expected demand is scaled linearly with the same rate. We also describe how the randomized linear program can be used to obtain a bid price control policy. Computational experiments indicate that our approach is quite fast, able to scale to industrial problems and can provide significant improvements over standard benchmarks.Network revenue management, linear programming, simulation, overbooking, no-shows.

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 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