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 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

APPLICATIONS OF REVENUE MANAGEMENT IN HEALTHCARE

Most profit oriented organizations are constantly striving to improve their revenues while keeping costs under control, in a continuous effort to meet customers‟ demand. After its proven success in the airline industry, the revenue management approach is implemented today in many industries and organizations that face the challenge of satisfying customers‟ uncertain demand with a relatively fixed amount of resources (Talluri and Van Ryzin 2004). Revenue management has the potential to complement existing scheduling and pricing policies, and help organizations reach important improvements in profitability through a better management of capacity and demand. The work presented in this thesis investigates the use of revenue management techniques in the service sector, when demand for service arrives from several competing customer classes and the amount of resource required to provide service for each customer is stochastic. We look into efficiently allocating a limited resource (i.e., time) among requests for service when facing variable resource usage per request, by deciding on the amount of resource to be protected for each customer and surgery class. The capacity allocation policies we develop lead to maximizing the organization‟s expected revenue over the planning horizon, while making no assumption about the order of customers‟ arrival. After the development of the theory in Chapter 3, we show how the mathematical model works by implementing it in the healthcare industry, more specifically in the operating room area, towards protecting time for elective procedures and patient classes. By doing this, we develop advance patient scheduling and capacity allocation policies and apply them to scheduling situations faced by operating rooms to determine optimal time allocations for various types of surgical procedures. The main contribution is the development of the methodology to handle random resource utilization in the context of revenue management, with focus in healthcare. We also develop a heuristics which could be used for larger size problems. We show how the optimal and heuristic-based solutions apply to real-life situations. Both the model and the heuristic find applications in healthcare where demand for service arrives randomly over time from various customer segments, and requires uncertain resource usage per request

Scheduling, inventory management and production planning: Formulations and solution methods

This thesis presents formulations and solution methods for three types of problems in operations management that have received major attention in the last decade and arise in several applications. We focus on the use of mixed integer programming theory, robust optimization, and decomposition-based methods to solve each of these three problems. We first study an online scheduling problem dealing with patients’ multiple requests for chemotherapy treatments. We propose an adaptive and flexible scheduling procedure capable of handling both the dynamic uncertainty arising from appointment requests that appear on waiting lists in real time and capable of dealing with unexpected changes. The proposed scheduling procedure incorporates several circumstances prevalent at oncology clinics such as specific intervals between two consecutive appointments and specific time slots and chairs. Computational experiments show the proposed procedure achieves consistently better results for all considered objective functions compared to those of the scheduling system in use at the cancer centre of a major metropolitan hospital in Canada. We next present an inventory management problem that integrates perishability, demand uncertainty, and order modification decisions. We formulate the problem as a two-stage robust integer optimization model and develop an exact column-and-row generation algorithm to solve it. Based on computational results, we show that considering order modification can significantly reduce the total cost. Moreover, comparing the results obtained by the proposed robust model to those obtained from the deterministic and stochastic variants, we note that their performances are similar in the risk-neutral setting while solutions from the robust models are significantly superior in the risk-averse setting. Finally, we study decomposition strategies for a class of production planning problems with multiple items, unlimited production capacity and, inventory bounds. Based on a new mixed integer programming formulation, we proposed a Lagrangian relaxation for the problem. We propose a deflected subgradient method and a stabilized column generation algorithm to solve the Lagrangian dual problem. Computational results confirm that the proposed formulation outperforms the previously proposed models and methods. Further analysis shows the impact of using decomposition techniques in providing tighter bounds