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

OPTIMIZATION METHODS FOR REVENUE MANAGEMENT

Η διαχείριση εσόδων αφορά αποφάσεις διαχείρισης απαίτησης, δηλαδή αποφάσεις για την διαθεσιμότητα και την τιμή του προϊόντος οι οποίες παίρνονται με σκοπό την μεγιστοποίηση του κέρδους. Αυτή η διπλωματική εργασία αναπτύσσει τις τεχνικές βελτιστοποίησης που χρησιμοποιούνται στην διαχείριση εσόδων.Revenue Management is concerned with demand-management decisions, i.e., decisions about product availability and price that are taken with the objective to maximize revenue. This master thesis is on optimazation techniques that are employed in Revenue Management

ROBUST REVENUE MANAGEMENT WITH LIMITED INFORMATION : THEORY AND EXPERIMENTS

Revenue management (RM) problems with full probabilistic information are well studied. However, as RM practice spreads to new businesses and industries, there are more and more applications where no or only limited information is available. In that respect, it is highly desirable to develop models and methods that rely on less information, and make fewer assumptions about the underlying uncertainty. On the other hand, a decision maker may not only lack data and accurate forecasting in a new application, but he may have objectives (e.g. guarantees on worst-case profits) other than maximizing the average performance of a system. This dissertation focuses on the multi-fare single resource (leg) RM problem with limited information. We only use lower and upper bounds (i.e. a parameter range), instead of any particular probability distribution or random process to characterize an uncertain parameter. We build models that guarantee a certain performance level under all possible realizations within the given bounds. Our methods are based on the regret criterion, where a decision maker compares his performance to a perfect hindsight (offline) performance. We use competitive analysis of online algorithms to derive optimal static booking control policies that either (i) maximize the competitive ratio (equivalent to minimizing the maximum regret) or (ii) minimize the maximum absolute regret. Under either criterion, we obtain closed-form solutions and investigate the properties of optimal policies. We first investigate the basic multi-fare model for booking control, assuming advance reservations are not cancelled and do not become no-shows. The uncertainty in this problem is in the demand for each fare class. We use information on lower and upper bounds of demand for each fare class. We determine optimal static booking policies whose booking limits remain constant throughout the whole booking horizon. We also show how dynamic policies, by adjusting the booking limits at any time based on the bookings already on hand, can be obtained. Then, we integrate overbooking decisions to the basic model. We consider two different models for overbooking. The first one uses limited information on no-shows; again the information being the lower and upper bound on the no-show rate. This is appropriate for situations where there is not enough historical data, e.g. in a new business. The second model differs from the first by assuming the no-show process can be fully characterized with a probabilistic model. If a decision-maker has uncensored historical data, which is often the case in reality, he/she can accurately estimate the probability distribution of no-shows. The overbooking and booking control decisions are made simultaneously in both extended models. We derive static overbooking and booking limits policies in either case. Extensive computational experiments show that the proposed methods that use limited information are very effective and provide consistent and robust results. We also show that the policies produced by our models can be used in combination with traditional ones to enhance the system performance

A static overbooking model in single leg flight revenue management

In this thesis, we present a static single leg airline revenue management model with overbooking. In this model it is assumed that the requests for different fare class tickets arrive according to independent nonhomogeneous Poisson processes. Each accepted request may cancel its reservation before the departure, and at the departure time no-shows may occur. In this setup, a static strategy is represented by a deterministic vector whose components give the closing times of the fare classes. Among those strategies we determine one with the highest expected revenue. As such this model can be seen as the static counter part of a dynamic continuous-time airline overbooking model. It can also be considered as an alternative to the well-known EMSR heuristics. In the thesis, we also study the performance of the optimal static strategy numerically and compare it with those of EMSR and dynamic strategies

New models for single leg airline revenue management with overbooking, no-shows, and cancellations

Airline revenue management (ARM) problem focuses on finding a seat allocation policy, which results in the maximum profit. Overbooking has been receiving significant attention in ARM over the years, since a major loss in revenue results from cancellations and no-shows. Basically, overbooking problem aims at maximizing the profit by minimizing the number of vacant seats. However, this problem is difficult to handle due to the demand and cancellation uncertainties and the size of the problem. In this study, we propose new models for the static and the dynamic overbooking problems. Due to the complex analytical form of the overbooking problem, in the static case we introduce models that give upper and lower bounds on the optimal expected profit. In the dynamic case, however, we propose a new dynamic programming model, which is based on two streams of arrivals; one for booking and the other one is for cancellation. This approach allows us to come up with a computationally tractable model. We also present numerical results to show the effectiveness of our models

Overbooking in airline revenue management

Master'sMASTER OF SCIENC

Web Hosting Service Level Agreements

This paper proposes a model for a relatively simple Web hosting provider. The model assumes the existence of a load-dispatcher and a finite number of Web-servers. We quantify the quality of service towards the clients of this facility based on a service level agreement between the two parts: the web hosting provider and the client. We assume that the client has the knowledge and resources to quantify its needs. Based on these quantifications, which in our model become parameters, the provider can establish a service offer. In our model, this offer covers the quality of service and the price options for it

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