Exploiting the Opportunities of Collaborative Decision Making: A Model and Efficient Solution Algorithm for Airline Use

• Published in: Transportation science Vol. 34; no. 4; pp. 381 - 393
• Main Author: Carlson, Paul M
• Format: Journal Article
• Language: English
• Published: Linthicum, MD INFORMS 01.11.2000; Transportation Science Section of the Operations Research Society of America; Institute for Operations Research and the Management Sciences
• Subjects: Air traffic; Air transportation and traffic; Air travel; Aircraft; Airlines; Airports; Algorithms; Applied sciences; Aviation; Civil aviation; Collaboration; Cost control; Cost efficiency; Cost functions; Decision making; Exact sciences and technology; Flight crews; Ground, air and sea transportation, marine construction; Modeling; Objective functions; Passengers; Real time; Schedules; Transportation planning, management and economics; Vehicular flight; United States; North America; America; Script; Decision making; Scheduling; Real time; Algorithm; Result; Case study; Traffic flow; Formulation; Traffic management; Airport; Air transportation; Large scale; Mathematical model; Comparative study
• ISSN: 0041-1655; 1526-5447
• DOI: 10.1287/trsc.34.4.381.12323

Collaborative decision making (CDM) is a joint Federal Aviation Administration (FAA)/industry initiative aimed at improving traffic flow management when inclement weather reduces an airport's arrival capacity. CDM replaces the present Ground Delay Program and is expected to be fully implemented by or around the year 2000. Under CDM, during periods of undersupply, the FAA's role shifts from centralized decision-maker to information gatherer and resource arbiter. Filling the decision-making void are the airlines, now given the freedom to make rescheduling decisions according to their own priorities and objectives. In this paper, we present an integer model and a real-time solution algorithm that assist an airline in making these rescheduling decisions at its hub airport, the location with the largest number of operations and therefore the greatest opportunity for improvement. Our research improves the existing state-of-the-art by representing the real world more thoroughly and intuitively than existing models (a modeling contribution) and by exploiting the structure of our model to achieve optimal solutions to large-scale scenarios in real time (an algorithmic contribution). Furthermore, we present four different formulations of the model. Although the different formulations are equivalent in that they have identical integer feasible solution sets and optimal objective function values, they exhibit widely-varying optimization times when tested on large-scale scenarios, allowing us to compare the characteristics and desirability of the alternative formulation techniques.