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

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

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

A scenario aggregation based approach for determining a robust airline fleet composition

Strategic airline fleet planning is one of the major issues addressed through newly initiated decision support systems, designed to assist airlines and aircraft manufacturers in assessing the benefits of the emerging concept of dynamic capacity allocation. We present background research connected with such a system, which aims to explicitly account for the stochastic nature of passenger demand in supporting decisions related to the fleet composition problem. We address this problem through a scenario aggregation based approach and present results on representative case studies based on realistic data. Our investigations establish clear benefits of a stochastic approach as compared with deterministic formulations, as well as its implementation feasibility using state-of-the-art optimization software

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

MILP Model For Network Revenue Management In Airlines

Seat inventory control is an important problem in revenue management which is to decide whether to accept or reject a booking request during the booking horizon in airlines. The problem can be modeled as dynamic stochastic programs, which are computationally intractable in network settings. Various researches have been tried to solve it effectively. Even though dynamic (and stochastic) programming (DP) models can be solved it optimally, they are computationally intractable even for small sized networks. Therefore, in practice, DP models are approximated by various mathematical programming models. In this paper, we propose an approximation model for solving airline seat inventory control problem in network environments. Using Linear Approximation technique, we will transform our problem into a concave piecewise LP model. Based on the optimal solution of ours, we suggest how to implement it for airline inventory control policies such as booking limits, bid-price controls and virtual nesting controls

Stochastic programming approaches to air traffic flow management under the uncertainty of weather

As air traffic congestion grows, air traffic flow management (ATFM) is becoming a great concern. ATFM deals with air traffic and the efficient utilization of the airport and airspace. Air traffic efficiency is heavily influenced by unanticipated factors, or uncertainties, which can come from several sources such as mechanical breakdown; however, weather is the main unavoidable cause of uncertainty. Because weather is unpredictable, it poses a critical challenge for ATFM in current airport and airspace operations. Convective weather results in congestion at airports as well as in airspace sectors. During times of congestion, the decision as how and when to send aircraft toward an airspace sector in the presence of weather is difficult. To approach this problem, we first propose a two-stage stochastic integer program by emphasizing a given single sector. By considering ground delay, cancellation, and cruise speed for each flight on the ground in the first stage, as well as air holding and diversion recourse actions for each flight in the air in the second stage, our model determines how aircraft are sent toward a sector under the uncertainty of weather. However, due to the large number of weather scenarios, the model is intractable in practice. To overcome the intractability, we suggest a rolling horizon method to solve the problem to near optimal. Lagrangian relaxation and subgradient method are used to justify the rolling horizon method. Since the rolling horizon method can be solved in real time, we can apply it to actual aircraft schedules to reduce the costs incurred on the ground as well as in airspace. We then extend our two-stage model to a multistage stochastic program, which increases the number of possible weather realizations and results a more efficient schedule in terms of costs. The rolling horizon method as well as Lagrangian relaxation and subgradient method are applied to this multistage model. An overall comparison among the previously described methodologies are presented.Ph.D.Committee Chair: Johnson, Ellis; Committee Co-Chair: Clarke, John-Paul; Committee Member: Ahmed, Shabbir; Committee Member: Sokol, Joel; Committee Member: Solak, Sena