The most advanced OR work on aviation infrastructure to date is undoubtedly associated with air traffic flow management (ATFM). ATFM took on major importance in the United States and Europe during the 1980s, when rapid traffic growth made it necessary to adopt a more strategic perspective on ATM. Rather than addressing congestion through local measures (e.g., by holding arriving aircraft in the airspace near delay-prone airports) the goal of ATFM is to prevent local system overloading by dynamically adjusting the flows of aircraft on a national or regional basis. It develops flow plans that attempt to dynamically match traffic demand with available capacity over longer time horizons, typically extending from 3–12 hours in the future. The prototypical application of ATFM is in ground holding, i.e., in intentionally delaying an aircraft’s takeoff for a specified amount of time to avoid airborne delays and excessive controller workload later on. Other ATFM tactics include rerouting of aircraft and metering (controlling the rate) of traffic flows through specified spatial boundaries in airspace. An important difference in the nature of the ATFM problem in the United States and in Europe should also be noted. In the United States, ATFM is primarily driven by airport capacity constraints, whereas in Europe en route airspace acts as the principal “bottleneck.” Europe’s Central Flow Management Unit, located in Brussels, currently determines (heuristically) ground delays to ensure that no en route sector capacity constraints are violated. This difference may, however, become moot in the near future due to continuing progress in increasing en route airspace capacity in Europe. OR model development related to ATFM can be viewed as going through two distinct stages. The first stage involved problem definition and development of large-scale mathematical optimization models of an aggregate scope. Attwool (1977) was the first to cast ATFM issues in mathematical terms, whileOdoni’s (1987) detailed description of the singleairport ground holding problem (GHP) as a dynamic and stochastic optimization problem stimulated much of the subsequent work. Important advances in modeling and solving the GHP are marked by the stochastic programming models of Richetta and Odoni (1993), the extension to a multiairport setting by Vranas et al. (1994), and the inclusion of en route constraints and rerouting options by Bertsimas and Stock (1998). Many other interesting papers on various aspects of optimizing ATFM and GHP appeared in the 1990s. Good reviews of the literature and of computational results can be found in Andreatta et al. (1993) and Hoffman and Ball (2000). The one common characteristic of the models developed in this first stage is the implicit assumption of a single decision-making authority attempting to optimize a “global” objective function: The providers of ATFM services (e.g., the FAA in the United States, Eurocontrol in Europe) are responsible for the allocation of ground holding delays among individual flights and/or for the rerouting, if necessary, of flights. The objective is to optimize in the aggregate, e.g., by minimizing the overall direct operating costs associated with ground holding and rerouting decisions, summed over all airlines and aircraft. This, however, is an operating philosophy that airlines strongly disagree with. They correctly argue that only individual airlines have the information necessary to make decisions on what is best for their own flights. As an obvious example, airline A, faced with a period of delays at a given congested airport, may assign very high priority to the timely arrival of one of its flights, X, because that flight may be carrying many business-class passengers who will be connecting to other flights or because it carries crews for subsequent flights departing from that airport. The assignment of priority to flight X has, in fact, little to do with direct operating costs of aircraft and is based on the business model of airline A and on information that only A possesses. In response to such airline concerns, as well as to various complaints about the limitations of the ATFM system during the 1980s and 1990s, the FAA has been engaged for the past 10 years in developing the Collaborative Decision-Making (CDM) Program. After an
initial planning period of about five years, CDM was fielded for the first time in 1998 in connection with the FAA’s Ground Delay Programs (GDPs), which go into effect whenever long air traffic delays are anticipated at an airport due to poor weather or other reasons, thus often necessitating ground holding. CDM marks a truly fundamental innovation in the ATM system, possibly the most important one in at least 30 years. The three main elements on which it is based are: (a) a dedicated data communications network (“CDMnet”), which facilitates the continuous exchange of information between the FAA and the airlines (plus any other CDM participants) about the current and near-future states of the ATM system; (b) the use of a common database and a common set of software tools by all CDM participants; and (c) the partial decentralization of decision making. With respect to (c), it is the FAA’s responsibility to forecast the capacity that will be available at each part of the ATM system during the relevant time horizon, as well as to allocate this capacity among the individual airlines and the other ATM system users. And it is the responsibility of each individual airline to decide how it will use its allocated share of capacity at each part of the system. This is a somewhat simplistic description of what, in practice, is a complicated process that employs several types of distributed decision-making techniques, such as rationing by schedule (RBS) and schedule compression—see Wambsganss (1996) and Vossen et al. (2003) for details. The driver for the adoption and implementation of the CDM concept was a small, OR-minded team in the FAA, the U.S. Department of Transportation, and especially the Metron Corporation (Chang et al. 2001). The CDM Program has already led to major reductions in delays and missed connections for air travelers and to documented savings of hundreds of millions of dollars in airline operating costs. ATFMrelated OR research has concurrently shifted away from large-scale, aggregate optimization models and toward “real-time” decision support tools that assist air traffic managers in the FAA and Airline Operations Centers in taking maximum advantage of the massive, up-to-date information base that CDM has made available. It is important to note, however, that many of the ideas and formulations developed in the
“pre-CDM” models can still be adapted to the CDM environment, often with little modification. For example, one of the most critical problems in the planning of GDPs continues to be the determination of airport acceptance rates (AAR) for several hours into the future and in the presence of uncertainty about airport capacity and air traffic demand. The efficient stochastic integer program developed for this purpose by Ball et al. (2003) can be viewed as a direct descendant of the pre-CDM model proposed by Richetta and Odoni (1993).