Amedeo R. Odoni

The global airline industry

List of Contributors. Series Preface. Notes on Contributors. Acknowledgements. 1 Introduction and Overview (P eter P. Belobaba and Amedeo Odoni). 1.1 Introduction: The Global Airline Industry. 1.2 Overview of Chapters. References. 2 The International Institutional and Regulatory Environment (Amedeo Odoni). 2.1 Introduction. 2.2 Background on the International Regulatory Environment. 2.3 Airline Privatization and International Economic Regulation. 2.4 Airports. 2.5 Air Traffic Management. 2.6 Key Organizations and Their Roles. 2.7 Summary and Conclusions. References. 3 Overview of Airline Economics, Markets and Demand (Peter P. Belobaba). 3.1 Airline Terminology and Definitions. 3.2 Air Transportation Markets. 3.3 Origin-Destination Market Demand. 3.4 Air Travel Demand Models. 3.5 Airline Competition and Market Share. 3.6 Chapter Summary. References. 4 Fundamentals of Pricing and Revenue Management ( Peter P. Belobaba). 4.1 Airline Prices and O-D Markets. 4.2 Airline Differential Pricing. 4.3 Airline Revenue Management. References. 5 Airline Operating Costs and Measures of Productivity ( Peter P. Belobaba). 5.1 Airline Cost Categorization. 5.2 Operating Expense Comparisons. 5.3 Comparisons of Airline Unit Costs. 5.4 Measures of Airline Productivity. References. 6 The Airline Planning Process (Peter P. Belobaba). 6.1 Fleet Planning. 6.2 Route Planning. 6.3 Airline Schedule Development. 6.4 The Future: Integrated Airline Planning. References. 7 Airline Schedule Optimization (Cynthia Barnhart). 7.1 Schedule Optimization Problems. 7.2 Fleet Assignment. 7.3 Schedule Design Optimization. 7.4 Crew Scheduling. 7.5 Aircraft Maintenance Routing and Crew Pairing Optimization. 7.6 Future Directions for Schedule Optimization. References. 8 Airline Flight Operations (Alan H. Midkiff, R. John Hansman and Tom G. Reynolds). 8.1 Introduction. 8.2 Regulation and Scheduling. 8.3 Flight Crew Activities During a Typical Flight. 8.4 Summary. 8.5 Appendix: List of Acronyms. References. 9 Irregular Operations: Schedule Recovery and Robustness (Cynthia Barnhart). 9.1 Introduction. 9.2 Irregular Operations. 9.3 Robust Airline Scheduling. 9.4 Directions for Ongoing and Future Work on Schedule Recovery from Irregular Operations. References. 10 Labor Relations and Human Resource Management in the Airline Industry (Jody Hoffer Gittell, Andrew von Nordenflycht, Thomas A. Kochan, Robert McKersie and Greg J. Bamber). 10.1 Alternative Strategies for the Employment Relationship. 10.2 Labor Relations in the US Airline Industry. 10.3 Labor Relations in the Airline Industry in Other Countries. 10.4 Human Resource Management at Airlines. 10.5 Conclusions. References. 11 Aviation Safety and Security (Arnold Barnett). 11.1 Safety. 11.2 Security. References. 12 Airports (Amedeo Odoni). 12.1 Introduction. 12.2 General Background. 12.3 Physical Characteristics. 12.4 Capacity, Delays and Demand Management. 12.5 Institutional, Organizational and Economic Characteristics. References. 13 Air Traffic Control (R. John Hansman and Amedeo Odoni). 13.1 Introduction. 13.2 The Generic Elements of an ATC System. 13.3 Airspace and ATC Structure. 13.4 ATC Operations. 13.5 Standard Procedures. 13.6 Capacity Constraints. 13.7 Congestion and Air Traffic Management. 13.8 Future ATC Systems. References. 14 Air Transport and the Environment (Karen Marais and Ian A. Waitz). 14.1 Introduction. 14.2 Limiting Aviations Environmental Impact: The Role of Regulatory Bodies. 14.3 Airport Water Quality Control. 14.4 Noise. 14.5 Surface Air Quality. 14.6 Impact of Aviation on Climate. 14.7 Summary and Looking Forward. References. 15 Information Technology in Airline Operations, Distribution and Passenger Processing (Peter P. Belobaba, William Swelbar and Cynthia Barnhart). 15.1 Information Technology in Airline Planning and Operations. 15.2 Airline Distribution Systems. 15.3 Distribution Costs and E-commerce Developments. 15.4 Innovations in Passenger Processing. References. 16 Critical Issues and Prospects for the Global Airline Industry (William Swelbar and Peter P. Belobaba). 16.1 Evolution of US and Global Airline Markets. 16.2 Looking Ahead: Critical Challenges for the Global Airline Industry. References. Index.

THE FLOW MANAGEMENT PROBLEM IN AIR TRAFFIC CONTROL

A system of flow management is one of the most promising short-term approaches to alleviating the severe network-wide congestion problems that air traffic in the United States and in Europe is currently experiencing. To design such a system one must address the flow management problem (FMP), a description and discussion of which is the subject of this paper. Even simplified versions of the FMP, such as the “generic FMP” which is based on a “macroscopic” model, are very challenging. The problem is inherently stochastic and dynamic and requires a discretized representation of flows. Additional complications are caused by the need to consider the distributive effects of flow management strategies as well as by certain peculiar characteristics of the capacity/demand and flow conservation relationships associated with elements of the ATC network. A brief literature review indicates that research on the FMP is still in its very early stages.

Applications of Operations Research in the Air Transport Industry

This paper presents an overview of several important areas of operations research applications in the air transport industry. Specific areas covered are: the various stages of aircraft and crew schedule planning; revenue management, including overbooking and leg-based and network-based seat inventory management; and the planning and operations of aviation infrastructure (airports and air traffic management). For each of these areas, the paper provides a historical perspective on OR contributions, as well as a brief summary of the state of the art. It also identifies some of the main challenges for future research.

SOLVING OPTIMALLY THE STATIC GROUND-HOLDING POLICY PROBLEM IN AIR TRAFFIC CONTROL

As air traffic congestion grows, ground-holding (or “gate-holding”) of aircraft is becoming increasingly common. The “ground-holding policy problem” (GHPP) consists of developing strategies for deciding which aircraft to hold on the ground and for how long. In this paper we present a stochastic linear programming solution to the static GHPP for a single airport. The computational complexity of existing solutions requires heuristic approaches in order to solve practical instances of the problem. The advantage of our solution is that, even for the largest airports, problem instances result in linear programs that can be solved optimally using just a personal computer. We present a set of algorithms and compare their performance to a deterministic solution and to the passive strategy of no ground-holds (i.e., to the strategy of taking all delays in the air) under different weather scenarios.

An Integer Optimization Approach to Large-Scale Air Traffic Flow Management

This paper presents a new integer programming (IP) model for large-scale instances of the air traffic flow management (ATFM) problem. The model covers all the phases of each flight---i.e., takeoff, en route cruising, and landing---and solves for an optimal combination of flow management actions, including ground-holding, rerouting, speed control, and airborne holding on a flight-by-flight basis. A distinguishing feature of the model is that it allows for rerouting decisions. This is achieved through the imposition of sets of “local” conditions that make it possible to represent rerouting options in a compact way by only introducing some new constraints. Moreover, three classes of valid inequalities are incorporated into the model to strengthen the polyhedral structure of the underlying relaxation. Computational times are short and reasonable for practical application on problem instances of size comparable to that of the entire U.S. air traffic management system. Thus, the proposed model has the potential of serving as the main engine for the preliminary identification, on a daily basis, of promising air traffic flow management interventions on a national scale in the United States or on a continental scale in Europe.

A Stochastic Integer Program with Dual Network Structure and Its Application to the Ground-Holding Problem

In this paper, we analyze a generalization of a classic network-flow model. The generalization involves the replacement of deterministic demand with stochastic demand. While this generalization destroys the original network structure, we show that the matrix underlying the stochastic model is dual network. Thus, the integer program associated with the stochastic model can be solved efficiently using network-flow or linear-programming techniques. We also develop an application of this model to the ground-holding problem in air-traffic management. The use of this model for the ground-holding problem improves upon prior models by allowing for easy integration into the newly developed ground-delay program procedures based on the Collaborative Decision-Making paradigm.

An Empirical Investigation of the Transient Behavior of Stationary Queueing Systems

This paper examines the transient behavior of infinite-capacity, single-server, Markovian queueing systems. It estimates Q ( t ), the expected number of customers in queue at time t , by numerically solving the sets of simultaneous, first-order differential equations that describe these systems. Empirical results have been drawn from these observations. For small values of t , the behavior of Q ( t ) is strongly influenced by the initial state of the queueing system. For systems with deterministic initial conditions, one can roughly predict which of a small set of patterns this behavior will follow. After an initial period of time and independently of initial conditions, Q ( t ) approaches Q (∞) in a manner that can be approximated through a decaying exponential function. On the basis of experimental evidence, we have developed an expression that provides a good approximation to the observed values of the time constant associated with this exponential function. This expression can also be used to determine an upper bound for the amount of time required until Q ( t ) is close to Q (∞).

Dynamic Ground-Holding Policies for a Network of Airports

The yearly congestion costs in the U.S. airline industry are estimated to be of the order of

On Finding the Maximal Gain for Markov Decision Processes

2 billion. In P. B. Vranas, Dimitris J. Bertsimas, and A. R. Odoni, The multi-airport ground-holding problem in air traffic control, Operations Research , Vol. 42, pp. 249–261, 1994, we introduced and studied generic integer programming models for the static multi-airport ground-holding problem (GHP), the problem of assigning optimal ground holding delays in a general network of airports, so that the total (ground plus airborne) delay cost of all flights is minimized. The present paper is the first attempt to address the multi-airport GHP in a dynamic environment. We propose algorithms to update ground-holding decisions as time progresses and more accurate weather (hence capacity) forecasts become available. We propose several pure IP formulations (most of them 0–1), which have the important advantages of being remarkably compact while capturing the essential aspects of the problem and of being sufficiently flexible to accommodate various degrees of modeling detail. For example, one formulation allows the dynamic updating of the mix between departure and arrival capacities by modifying runway use. These formulations enable one to assign and dynamically update ground holds to a sizeable portion of the network of the major congested U.S. or European airports. We also present structural insights on the behavior of the problem by means of computational results, and we find that our methods perform much better than a heuristic which may approximate, to some extent, current ground-holding practices.

The European Air Traffic Flow Management Problem

The method of successive approximations for solving problems on single-chain Markovian decision processes has been investigated by White and Schweitzer. This paper shows that Whites scheme not only converges, but also can be modified so that monotonic upper and lower bounds on the maximal gain can be obtained.