An Approach for Finding Multiple Area of Specialization Configuration Advisories

Abstract

of specialization supervisors dynamically configure a set of air traffic control resources so that air traffic in the area can operate safely and effi ciently. These resources include airspace sectors, air traffic control positions staffed by controllers, and physical air traffic control equipment. In this paper, we motivate and demonstrate an approach for finding multiple advisories that can assist area supervisors as they accomplish this task. The first motivating factor is that a preference for multiple good and also distinct advisories has been documented in similar contexts, including some air traffic management problems. The second factor that motivates our approach is that the model, problem statement, and algorithm used to generate a single advisory are incomplete and do not perfectly represent reality. The third factor, which we speculate is primarily a result of the second factor, is that area supervisors have indicated a preference for multiple (usually two or three) advisories over a single advisory. Area supervisors have further indicated that each proposed advisory should be different from the other proposed advisories. We investigate the set of advisories that perform best according to a particular objective function for some realistic problem instances. The best few advisories are typically not meaningfully different and therefore should not be presented together to supervisors, and this is the fourth and final factor that motivates our approach. Based on these motivating factors, we define a problem statement which requests multiple good advisories that are all sufficiently different from each other. We briefly describe a heuristic algorithm that was developed for this problem. To more concretely illustrate and motivate the proposed approach, we present the advisories provided by this algorithm for a sample problem instance. We also demonstrate that the proposed heuristic can find feasible second advisories for as many realistic problem instances (15 of 18) as a nearly-exhaustive search. When executed on a desktop workstation computer, the proposed heuristic returns advisories for these realistic problem instances in less than one second per problem instance.