1552 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 42, NO. 6, NOVEMBER 2012 Correspondence Formulation of Reduced-Taskload Optimization Models for Conflict Resolution Adan Vela, Karen M. Feigh, Senay Solak, William Singhose, and John-Paul Clarke Abstract—This paper explores methods to include aspects of controller taskload into conflict-resolution programs through a parametric approach. We are motivated by the desire to create conflict-resolution decision- support tools that operate within a human-in-the-loop control architecture by actively accounting for, and moderating, controller taskload. Specifi- cally, we introduce two conflict-resolution programs with the objective of managing controller conflict-resolution taskload, i.e., the number of ma- neuvers used to separate air traffic. Managing conflict-resolution taskload is accomplished by penalizing aircraft maneuvers through their L 1 norm in the cost function or constraining the number of maneuvers directly. Analysis of the programs reveals that both approaches are successful at managing controller conflict-resolution taskload and minimizing fuel burn. Directly constraining conflict-resolution taskload is more successful at minimizing the variation in the number of aircraft maneuvers issued and returning the aircraft to their desired exit point. Penalizing maneu- vers through L 1 norm costs is more successful at reducing controller conflict-resolution taskload at lower traffic volumes. Ultimately, results demonstrate that the inclusion of such parametric models can successfully regulate controller conflict-resolution taskload. Index Terms—Air traffic control, conflict resolution, controller taskload. I. I NTRODUCTION In many regions, the projected growth in air transportation demand is likely to exceed the capacity of both the airspace and the air traffic control system. Consequently, air navigation service providers have made several efforts to improve the capacity and throughput of existing airspaces through airspace redesign, trajectory-based operations, and incorporation of new traffic flow management tools [1], [2]. Addition- ally, to reduce the air traffic controller workload, the past two decades have seen significant investment into the study and development of aircraft conflict-detection and resolution (CDR) algorithms. Early examples include [3] and [4], with a more comprehensive survey of the proposed methods presented in [5]. The goal of implementing any conflict-resolution algorithm is twofold: to increase capacity and to improve safety. A number of researchers and air navigation service providers hypothesize that the inclusion of CDR tools into air traffic control systems will reduce or transform controller workload by decreasing the amount of time and mental effort that air traffic controllers spend resolving conflicts [6]–[9]. Unfortunately, many of the CDR algorithms attempt to replace air traffic controllers, moving them from their current tactical role to a Manuscript received November 7, 2010; revised June 1, 2011 and January 19, 2012; accepted April 6, 2012. Date of current version October 12, 2012. This work was supported in part by the National Aeronautics and Space Administration Grant NNX08AY52A; by the Federal Aviation Administration Award 07-C-NE-GIT, Amendment 005, 010, and 020; and by the Air Force Contract FA9550-08-1-0375. This paper was recommended by Associate Edi- tor N. Sarter. A. Vela, K. M. Feigh, W. Singhose, and J.-P. Clarke are with the Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail: aevela@gatech.edu; karen.feigh@gatech.edu; Singhose@gatech.edu; johnpaul@gatech.edu). S. Solak is with the University of Massachusetts, Amherst, MA 01003 USA (e-mail: solak@som.umass.edu). Digital Object Identifier 10.1109/TSMCA.2012.2202106 more strategic traffic flow management role. An example discussion of this type of proposal is provided in [10]. However, it is most probable that the transformation from human-based tactical control to that of automation will not occur in the near future. To date, there are still concerns about the safety and realizability of automated tactical conflict-resolution algorithms in governing traffic in support of this transition. Despite the significant effort expended to design automated conflict-resolution algorithms that provide prov- ably safe solutions with realistic aircraft trajectories [11], [12], these formulations have only demonstrated the ability to find safe trajectory solutions for a fixed set of aircraft. The associated proofs do not guar- antee safe solutions for a changing set of aircraft, e.g., when aircraft are entering and exiting an airspace. Hence, the arrival of new aircraft can potentially result in an infeasible conflict situation for which no safe solution exists. Not only is proving the safety of proposed systems a limitation, but so is providing the enabling technology. The transition to fully automated CDR will likely be hindered by the slow uptake of the advanced avionics required on the air and ground to fully support automated tactical air traffic control. Before the conversion to a fully automated CDR, there will likely be a significant period where the demand for air traffic services will require the creation of partially automated CDR algorithms that aid the air traffic controller’s tactical role. The inclusion of such advisory decision-support tools in human-based air traffic control op- erations requires a fundamentally different approach to the design and implementation of CDR algorithms. The algorithms must explicitly acknowledge the role of controllers and accommodate their abilities. One possible concept of operation would be to design CDR algo- rithms to suggest solutions to the controller at regular intervals. Along these lines, a goal of the CDR advisory tool would be to generate solutions that maintain controller involvement (i.e., keep them from becoming bored or complacent) while keeping the workload within a manageable region. Under this paradigm, the constraints incorpo- rated into the algorithm would be based upon a model of controller workload. The research question then becomes how to best achieve the goals of bounding the controller workload in a CDR algorithm and what the potential operational consequences (e.g., aircraft fuel burn) of such bounds are. This paper presents two formulations within an optimization frame- work to bound or manage workload. One method is able to regulate aircraft maneuvers through a cost penalty, while the other directly constrains the number of maneuvers that the optimization may suggest within a time window. By extension, a more general framework is proposed that is able to both penalize and constrain the number of proposed resolution maneuvers. Here, workload is approximated using the number of resolution commands required to manage traffic, hereby defined in this paper as conflict-resolution taskload. The authors acknowledge the limitations of such an approach [13] but believe that it is sufficient to demonstrate the idea of incorporating some measure of workload limitations into control algorithms. Future research in this area may address more sophisticated and accurate workload measures. Ultimately, the methods presented here seek to design decision- support tools to generate resolution solutions that are both optimal and compatible with controller work practice. To that end, we de- velop and utilize a parametric modeling procedure to account for controller conflict-resolution taskload by modifying a representative CDR algorithm with the two different methods. The resulting class of CDR algorithms is referred to as reduced-taskload conflict-resolution 1083-4427/$31.00 © 2012 IEEE