Agent-Based Modelling and Simulation, airline disruption management, airline operations, Airline Operations Control, Coordination, Decision Support Systems

A novel approach to developing decision support systems for airline disruption management

Airlines continuously face disturbances that disrupt their highly optimized schedule. Events like adverse weather conditions, air traffic management decisions, sick crewmembers, or aircraft mechanical problems often cause delays in the airline’s schedule. For an airline such disruptions are very costly because they tend to cause domino effects in the air transportation schedule. Over the year 2007 alone, U.S. carriers lost over $8  billion because of delays of some sort Barnhart (2009). Therefore, reducing the impact of disruptions on the airline schedule could considerably reduce these costs. An airline usually recovers from such disturbances through the role played by the Airline Operations Control (AOC) centre. If a disruption affects flight plans, then human operators at the AOC center take corrective actions in real-time in order to manage the disruption. Possible actions include cancelling or delaying of flights and swapping aircraft or crew, and are often the result of decision-making and coordination processes that involves many AOC operators.

The specific organization of an AOC center depends on multiple factors. These factors include the airline size, type of airline operations, location, and airline culture. However, despite the different organization types, it is possible to identify operators that are common to AOC centers. Fig. 1 gives an overview of a typical AOC center showing the operators, the technical systems, and the interactions between them and their external world (while the exact terminologies may vary per airline). It should be noted that in addition to the operators shown in Fig. 1, there exist other services in AOC centers which provide support for AOC operators (e.g. operational engineering). In addition, a crisis center which coordinates activities after an accident or incident is often an integrated part of an airline’s AOC center.


        Fig. 1 AOC embedded in the large air transportation system

To date, studies to improve AOC performance have mainly focused on using optimization techniques to develop decision support tools. For instance, Bratu & Barnhart (2006) propose two optimization tools that generate recovery plans for aircraft, crews, and passengers by determining which flight leg departures to postpone and which to cancel. Abdelghany et al. (2008) propose a decision-support tool that provides AOC centres with the capability to develop a proactive schedule recovery plan that integrates all flight resources. The optimization tool examines possible resource swapping and flight requoting to generate a schedule recovery that minimizes flight delays and cancellations. Petersen et al. (2012) propose a mixed-integer programming tool to solve the fully integrated airline recovery problem including the schedule, aircraft, crew, and passenger problems. In the same vein, Arikan et al. (2017) propose an optimization tool to solve the fully integrated airline recovery problem using a conic quadratic mixed integer programming formulation. Santos et al. (2017), present an integer linear programming tool to help AOC controllers decide which flights to delay and which flights to make depart on time.

From a combinatorial optimization perspective, these tools have the mathematical capability in minimizing airline operating costs and passenger costs. However, such a combinatorial optimization approach fails in capturing the complex socio-technical nature of AOC (Feigh & Pritchett, 2010; Bruce, 2011; Richters et al. 2017). According to Clausen et al. (2005), there is a gap between the support offered by IT systems and the reality faced in AOC centres. In order to address these socio-technical challenges, Delft Aviation proposes a novel approach for developing and validating decision support tools that makes it possible to learn in the early design phase from simulated disturbances, and to feedback this learning to the further improvement of the early design. Through the iterative cycle of design improvement and assessment, shown in figure 2., the resulting overall socio-technical design will become resilient against a wide range of disturbances. Agent-based modelling and simulation is chosen because it has been extensively used to model and analyse complex socio-technical systems, and address cases where agents need to coordinate and solve problems in a distributed fashion.


Fig. 2 AOC performance assessment feedback to decision-support systems designers

We can help you improve the performance of your airline operations with one of our services below:

  • Evaluate from the complex socio-technical perspective the decision-support systems on their performance and practical implementation. The evaluation is conducted using both a scenario-based and task-based analysis and aims at providing feedback to improve tool design. E.g. which activities should automation aim to support? And how should the work be split between the decision support system and human operator?
  • Evaluate your current airline disruption management policies using agent-based modelling and simulation.
  • Propose innovative disruption management policies for a wide range of scenarios based on recent advances in psychology.
  • Organize training workshops for airline controllers (especially novices) to improve their decision making and coordination processes.


  • Abdelghany, K.F., Abdelghany, A.F., Ekollu, G. (2008). An integrated decision support tool for airlines schedule recovery during irregular operations. Eur. J. Oper. Res. 185(2):825-848
  • Arikan, U., Gurel, S., Akturk, M.S. (2017). Flight network-based approach for integrated airline recovery with cruise speed control. Transportation science. Articles in advance, pp. 1-29.
  • Barnhart, C. (2009). The Global Airline Industry, chapter Irregular Operations, pages 253–274. John Wiley & Sons, Ltd, West Sussex.
  • Bratu, S., Barnhart, C. (2006). Flight operations recovery: New approaches considering passenger recovery. J. Scheduling 9(3):279-298
  • Bruce, P. J. (2011). Decision-Making in Airline Operations: The Importance of Identifying Decision Considerations. International Journal of Aviation Management, 1(1/2):89–104.
  • Clausen, J., Larsen, A., Larsen, J., 2005. Disruption Management in the Airline Industry – Concepts, Models and Methods.
  • Feigh, K. M. (2008). Design of Cognitive Work Support Systems for Airline Operations. PhD thesis, Georgia Institute of Technology.
  • Petersen et al. (2012) propose a mixed-integer programming tool to solve the fully integrated airline recovery problem including the schedule, aircraft, crew, and passenger problems. Richters, F., Schraagen, J.M., Heerkens, H. (2017). Assessing the structure of non-routing decision processes in Airline Operations Control. Ergonomics, 59:3, 380-392.
  • Santos, B.F., Wormer, M.E.C., Achola, T.A.O., Curran, R. (2017). Airline delay management problem with airport capacity constraints and priority decisions. Journal of Air Transport Management 63. 34-44.


The cover photo in this blog post shows KLM’s AOC centre. Photo credit:


Agent-Based Modelling and Simulation, Blog, Survey

A Short Survey of Previous Agent-Based Applications in Aviation Research

Written by  Soufiane Bouarfa      @delftaviation    01/03/2017

As explained in the previous post, Agent-Based Modelling and Simulation (ABMS) has a great potential in solving challenging problems in aviation. Unfortunately, this powerful approach is not yet well exploited by the aviation community. So we thought it would be nice to provide an overview of past agent-based applications in order to give an idea about the kind of problems that can be addressed with this novel approach. This post summarizes previous work conducted by various aviation research groups using the agent-based paradigm.

After having performed a literature scan, we have noticed that there are two main approaches namely: Multi-Agent Systems (MAS) and ABMS of socio-technical systems. Although there is significant knowledge and background overlap between the two approaches (e.g. both use distributed autonomous agents) the two are used in complementary ways. The primary goal in ABMS of socio-technical systems is to search for explanatory insight into the collective behaviour of agents obeying simple rules, rather than solving specific practical or engineering problems as in MAS (Wikipedia 2017). Researchers in ABMS of socio-technical systems develop simulations that can reveal system behaviour emerging from the agent’s collective actions and interactions. In these simulations, the agent entities are used to represent actors in the real world (E.g. individuals or teams) and need not be intelligent technical system agents only. They are programmed to react to the computational environment in which they are located, where this environment is a model of the real environment in which the actors operate (Gilbert 2008). So with this comes the need for instance to represent human behaviour and social interactions. On the other hand, a technical MAS is a computerized system composed of multiple interacting intelligent agents. Here intelligence can include some methodical, procedural or algorithmic search. When running simulations of a technical MAS then this also is referred to as ABMS. In  Nikolic & Kasmire (2013), a distinction was made between ABMS and MAS, however the explicit mentioning of technical MAS and ABMS of socio-technical systems was not done. According to their distinction, the main difference between ABMS and MAS is that ABMS sets up agents believed to have crucial characteristics of real world analogs to see what happens when they do whatever they do; while in a MAS agents are set up with exactly the characteristics, connections and choices that they need to achieve certain desired emergent states.

In air transportation, agent-based models of socio-technical systems and of technical MAS have been developed and used by the aviation community. These models have been applied to fulfil several purposes, e.g. to evaluate current and future operational concepts, to assess safety risk, or optimize ATC, airline, or airports operations. Table 1 gives an overview of these models and classifies them in the two distinct categories of technical MAS and ABMS of a socio-technical system. This overview has revealed interesting findings: 1) Technical MAS have been used before ABMS of socio-technical systems; 2) ATM systems apparently are among the oldest application areas of technical MAS and have been a standard application of research in the field since the work of Cammarata et al. (1983). It is also relevant to recognize that ABMS is known by many names, e.g. ABM (agent-based modelling), IBM (individual-based modelling), ABS (agent-based systems or simulation) are all widely-used acronyms.

Table 1: models in air transportation using the agent-based paradigm

Publications (in chronological order) Institute Model purpose
Technical MAS
Cammarata et al. 1983 Rand Conflict resolution
Ljungberg & Lucas 1992 Australian Artificial Intelligence Institute Assisting flow managers to arrange sequence of incoming aircraft
Langerman & Ehlers 1997 Rand Afrikaans University Airline schedule development
Tomlin et al. 1998 University of California Conflict resolution
Wangermann & Stengel 1998 Princeton University Optimization of airline operations through negotiation
Nguyen-Duc et al. 2003 University of Paris 6, EUROCONTROL Real time traffic synchronization
Wollkind et al 2004 Texas A&M University Conflict resolution using cooperative and negotiation techniques
Hwang et al 2007 Purdue University Verification of collision avoidance algorithms
Sislak et al. 2007 Czech Technical University, US Air Force Research Laboratory Conflict resolution
Tumer & Agogino 2007 Oregon State University, NASA Ames Research Centre Traffic flow management
Gorodetsky et al. 2008 St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences Conflict resolution
Mao 2011 Universiteit van Tilburg Scheduling aircraft ground handling operations
Castro et al. 2014 University of Porto, MASDIMA Airline disruption management
Udluft et al 2016 Delft University of Technology Agent-based simulation of decentralized control for taxiing aircraft
ABMS of a Socio-Technical System
Blom et al. 2001 National Aerospace Laboratory NLR Accident risk assessment of advanced ATM concepts
Corker 1999 San Jose State University Evaluation of advanced ATC operational concepts
Campbell et al. 2000 The MITRE Corporation Policy analysis of collaborative traffic flow management
Callantine 2001 San Jose State University, NASA Ames Research Centre Evaluation of advanced ATC operational concepts
Blom et al 2003a National Aerospace Laboratory NLR Accident risk assessment of opposite en-route traffic lanes
Blom et al 2003b National Aerospace Laboratory NLR Accident risk assessment of simultaneous converging instrument approaches
Niedringhaus 2004 The MITRE Corporation Assessing the impact of stakeholder decisions on the NAS
Lee et al. 2005 NASA Ames Research Centre Evaluation of advanced ATC operational concepts
Mehta et al. 2006 Purdue University, Lockheed Martin and Simulex Evaluation of advanced ATC operational concepts
Blom et al 2009a National Aerospace Laboratory NLR Free flight equipped aircraft
DeLaurentis & Ayyalasomayajula 2009 Purdue University Assessing the impact of stakeholder actions on the air transport network
Stroeve et al 2009 National Aerospace Laboratory NLR Accident risk assessment of active runway crossings
Wolfe et al. 2009 NASA Ames Research Centre Evaluation of air traffic flow management concepts
DeOliveira et al 2010 Atech Tecnologias Críticas , University of São Paulo, National Aerospace Laboratory Safety risk assessment of an advanced ASAS interval management concept
Kuhn et al. 2010 University of Louisville, Louisiana Tech University, West Virginia University, Argonne National Laboratory Airline market share prediction
George et al. 2011 Intelligent Automation, University of California Santa Cruz, Raytheon Company, Sensis Corporation, Mosaic-ATM, Aerospace Computing, NASA (Ames, Glenn, and Langley) Evaluation of current and future operational concepts
Sharpanskykh & Stroeve 2011 Vrije Universiteit Amsterdam, National Aerospace Laboratory NLR Assessment of safety culture
Bouarfa et al. 2013 Delft University of Technology Safety assessment of an advanced airport ConOps
Darabi et al. 2014 Stevens Institute of Technology Studying competition and collaboration between airlines
Gurtner et al. 2014 Scuola Normale Superiore di Pisa, Deep Blue, Universita degli Studi di Siena, Santa Fe Institute Studying airspace allocation in various conditions
Molina et al. 2014 Technical University of Madrid Evaluating the impact of new concepts and regulations on the ATM network
Blom & Bakker 2015 National Aerospace Laboratory NLR Safety assessment of an advanced pure airborne TBO concept under very high traffic demand
Blom & Bakker 2016 National Aerospace Laboratory NLR Safety assessment of an advanced ground-based TBO concept under very high traffic demand
Sharpanskykh and Haest 2016 Delft University of Technology Agent-based sociotechnical modelling of aircraft turnaround processes
Bouarfa et al. 2016 Delft University of Technology Agent-based modelling of coordination by Airline Operations Control
Blom & Bouarfa 2016 Delft University of Technology Agent-based modelling of resilience
Sharpanskykh, A. (2016b) Delft University of Technology Agent-based modelling framework for adaptive resilience in air transport
Sharpanskykh, A. (2016a); Janssen and Sharpanskykh 2017 Delft University of Technology Agent-based modelling of security of airports

If you are aware of an agent-based application that was not shown in this overview, we would very much appreciate it if you email it to or leave the reference details in the comments box below.


  • Bouarfa, S, Blom, H.A.P., Curran, R., Everdij, M.H.C. Agent-Based Modeling and Simulation of Emergent Behavior in Air Transportation. Journal of Complex Adaptive Systems Modeling, 1:15, 2013. DOI 10.1186/2194-3206-1-15.
  • Bouarfa, S., Blom, H.A.P., Curran, R., 2016. Agent-Based Modelling and Simulation of Coordination by Airline Operations Control. IEEE Transactions on Emerging Topics in Computing, Volume:PP, Issue:99, February.DOI 10.1109/TETC.2015.2439633.
  • Blom, H.A.P., Bakker, G.J., Blanker, P.J.G., Daams, J., Everdij, M.H.C., Klompstra, M.B., 2001. Accident Risk Assessment for Advanced ATM. In: Donohue, G.L., Zelweger, A.G. eds. Air Transport Systems Engineering: AIAA, p 463-80.
  • Blom, H.A.P., Stroeve, S.H., Everdij, M.H.C., van der Park, M.N.J, 2003a. Human cognition performance model to evaluate safe spacing in air traffic, Human Factors and Aerospace Safety, Vol. 3, pp. 59-82.
  • Blom, H.A.P., Klompstra, M.B., Bakker, G.J., 2003b. “Accident risk assessment of simultaneous converging instrument approaches,” Air Traffic Control Quarterly, Vol. 11, pp. 123-155.
  • Blom, H.A.P., Obbink, B.K., Bakker, G.J., 2009a. Simulated Safety Risk of an Uncoordinated Airborne Self Separation Concept of Operation. ATC-Quarterly, 17, 63-93.
  • Blom, H.A.P., Bakker, G. J., 2015. “Safety Evaluation of Advanced Self-Separation Under Very High En Route Traffic Demand”, Journal of Aerospace Information Systems, Vol. 12, No. 6, pp. 413-427. doi: 10.2514/1.I010243
  • Blom, H.A.P., Bakker, G.J., 2016. “Agent-Based Modelling and Simulation of Trajectory Based Operations under Very High Traffic Demand”, Proc. of 6th SESAR Innovation Days, Delft, 8-10 November 2016, Ed: D. Schaefer, Eurocontrol, Brussels, ISSN 0770-1268, pp. 1-9.
  • Blom, H.A.P., Bouarfa, S., 2016. Resilience. In Complexity Science in Air Traffic Management, eds. Cook, A., & Rivas, D., Ashgate publishing, Chapter 5, ISBN 978-1-4724-6037-0.
  • Callantine, T.J., 2001. Agents for Analysis and Design of Complex Systems. in Systems, Man, and Cybernetics, 2001 IEEE International Conference, vol. 1, 567-573.
  • Cammarata, S., McArthur, D., Steeb, R., 1983. Strategies of Cooperation in Distributed Problem Solving. N-2031-ARPA, the defense advanced research projects agency.
  • Campbell, K.C., Cooper, W.W.jr., Greenbaum, D.P., Wojcik, L.A., 2000. Modeling Distributed Human Decision-Making in Traffic Flow Management Operations. 3rd USA/Europe Air Traffic Management R&D Seminar, Napoli, 13-16 June.
  • Castro, A.J.M., Rocha, A.P., Oliveira, E., 2014. “A new approach for disruption management in airline operations control,” Studies in Computational Intelligence, vol. 562, Springer, Berlin.
  • Corker, K.M., 1999. Human Performance Simulation in the Analysis of Advanced Air Traffic Management. Proc. of the 1999 Winter Simulation Conference.
  • Darabi, H.R., Mostashari, A., Mansouri, M., 2014. Modelling Competition and Collaboration in the Airline Industry using Agent-Based Simulation. Int. J. Industrial and Systems Engineering, Vol. 16, No. 1.
  • DeLaurentis, D.A., Ayyalasomayajula, S. 2009. Exploring the Synergy between Industrial Ecology and System of Systems to understand Complexity’, Journal of Industrial Ecology, Vol. 13, No. 2, pp.247–263.
  • DeOliveira, I.R., L.F. Vismari, P.S. Cugnasca, J.B. Camargo Jr, G.J. Bakker, H.A.P. Blom, 2010. “A case study of advanced airborne technology impacting air traffic management”. Eds. Weigang, L., et al., Computational models, software engineering and advanced technologies in air transportation, Engineering Science Reference, Hershey, pp. 177-214.
  • George, S.E., Satapathy, G., Manikonda, V., Wieland, F., Refai, M.S., Dupeee, R., 2011. Build 8 of the Airspace Concept Evaluation system. AIAA 2011-6373, AIAA Modeling and Simulation Technologies Conference, 08-11 August, Portland, Oregon.
  • Gilbert, N., 2008. Agent-Based Models. Sage Publications Ltd, UK.
  • Gorodetsky, V., Karsaev, O., Samoylov, V., Skormin, V., 2008. Multi-Agent Technology for Air Traffic Control and Incident Management in Airport Airspace. In proceedings of the International Workshop Agents in Traffic and Transportation; Portugal, 119-125.
  • Gurtner, G., Valori, L., Lillo, F., 2014. Competitive Allocation of Resources on a Network: an Agent-Based Model of Air Companies Competing for the Best Routes. arXiv:1411.5504v1 [physics.soc-ph], 20 Nov.
  • Hwang, I., Kim, J., Tomlin, C. 2007. Protocol-based conflict resolution for air traffic control. Air Traffic Control Quarterly 15(1), 1–34.
  • Janssen, S., Sharpanskykh, A., 2017. Agent-based Modelling for Security Risk Assessment (forthcoming)
  • Kuhn, J.R. Jr., Courtney, J.F., Morris, B., Tatara, E.R., 2010. Agent-Based Analysis and Simulation of the Consumer Airline Market Share for Frontier Airlines, Knowledge-Based Systems (23), 875–882.
  • Langerman, J., Ehlers, E.M., 1997. Agent-Based Airline Scheduling. Computers ind. Engng, Vol. 33, Nos 3-4, 849-852.
  • Lee, S.M., Ravinder, U., Johnston, J.C., 2005. Developing an Agent Model of Human Performance in Air Traffic Control Operations Using APEX Cognitive Architecture. Proc. of the 2005 Winter Simulation Conference.
  • Ljungberg, M., Lucas, A., 1992. The OASIS Air Traffic Management System. Technical Note 28, August.
  • Mao, X., 2011. Airport under Control: Multi-agent Scheduling for Airport Ground Handling. PhD thesis, May, Tilburg University.
  • Mehta, S., Nedelescu, L., Nolan, M., Krull, K., Whitford, J., Pfleiderer, M., Cheemun, F. 2006. Using Agent-based Simulation to Evaluate Technology and Concepts for the National Airspace System, IEEEAC paper #1227, Version 2.
  • Molina, M., Carrasco, S., Martin, J., 2014. Agent-Based Modeling and Simulation for the Design of the Future European Air Traffic Management System: The Experience of CASSIOPEIA. In Corchado, J.M. et al. (Eds.): PAAMS 2014 Workshops, CCIS 430, pp. 22-33.
  • Nguyen-Duc, M., Briot, JP, Drogoul, A., Duong, V., 2003. An Application of Multi-Agent Coordination Techniques in Air Traffic Management. Proc. Of the IEEE/WIC International Conference on Intelligent Agent Technology (IAT’03).
  • Niedringhaus, W.P., 2004. The Jet:Wise Model of National Air Space System Evolution. Simulation 80:45.
  • Nikolic, I., Kasmire, J., 2013. Theory. Van Dam, K.H., Nikolic, I., Lukszo, Z., eds. Agent-Based Modelling of Socio-Technical Systems, Chapter 2, Agent-Based Social Systems 9.
  • Sharpanskykh, A., Stroeve S.H., 2011. An Agent-Based Approach for Structured Modeling Analysis and Improvement of Safety Culture. Comput Math Organ Theory 17:77–117, Springer.
  • Sharpanskykh, A., 2016a. A Complex Systems Approach to Modeling and Analysis of Security and Resilience in Air Transport, Conference on Complex Systems, Amsterdam, the Netherlands.
  • Sharpanskykh, A., 2016b. Unravelling and Improving Adaptive Resilience of Complex Air Transport Systems, Conference on Complex Systems, Amsterdam, the Netherlands.
  • Sharpanskykh, A., Haest, R., 2016. An Agent-based Model to Study Compliance with Safety Regulations at an Airline Ground Service Organization. Applied Intelligence, Volume 45, Issue 3, pp. 881–903.
  • Sislak, D., Pechoucek, M., Volf, P., Pavlicek, D., Samek, J., Marik, V., Losiewicz, P., 2007. AGENTFLY: Towards Multi-Agent Technology in Free Frlight Air Traffic Control. Whitestein Series in Software Agent Technologies, 73-96, Birkhauser Verlag Baser/Switzerland.
  • Stroeve, S.H., Blom, H.A.P., Bakker, G.J., 2009. Systemic accident risk assessment in air traffic by Monte Carlo simulation. Safety Science 47(2), 238-249.
  • Tomlin, C., Pappas, G.J., Sastry, S., 1998. Conflict Resolution for Air Traffic Management: A Study in Multiagent Hybrid Systems. IEEE Transactions on Automatic Control, Vol. 43, No. 4, April.
  • Tumer, K., Agogino, A., 2007. Distributed Agent-Based Air Traffic Flow Management. AAMAS’07, May 14-18, Honolulu, Hawaii, USA.
  • Udluft, H., Sharpanskykh, A., Curran, R., Clarke, JP., 2016. Agent-Based Simulation of Decentralized Control for Taxiing Aircraft. The Sixth SESAR Innovation Days.
  • Wangermann, J.P., Stengel, R.F., 1998. Principled Negotiation between Intelligent Agents: A Model for Air Traffic Management. Artificial Intelligence in Engineering 12, 177-187.
  • Wikipedia, 2017. Agent-Based Model.
  • Wolfe, S.R., Jarvis, P.A., Enomoto, F.Y., Sierhuis, M., van Putten, B.J., Sheth, K.S., 2009. A Multi-Agent Simulation of Collaborative Air Traffic Flow Management. In: Bazzan, A.L.C, Klugl, F. (Eds.), Multi-Agent Systems for Traffic and Transportation Engineering, Chapter 18, IGI Global Publishing, 357–381.
  • Wollkind, S., Valasek, J., Ioerger, T.R., 2004. Automated Conflict Resolution for Air Traffic Management Using Cooperative Multiagent Negotiation. In proc. AIAA Guidance, Navigation, Control Conference. 1078-1088.
Agent-Based Modelling and Simulation, Blog

Agent-Based Modelling and Simulation in Air Transportation

Written by  Soufiane Bouarfa      @delftaviation      20/02/2017

In order to improve the safety, capacity, economy, and sustainability of air transportation, revolutionary changes are required. These changes might range from the introduction of new technology and operational procedures to unprecedented roles of human operators and the way they interact. Implementing such changes can introduce both negative and positive emergent behavior. i.e. behavior that arises from the interactions between system entities as proposed in innovative concepts. Currently, the inability to understand and control such behavior prevents us from avoiding undesired negative emergent behaviours and promoting positive ones. In order to address this problem, Delft Aviation aims to understand emergent behavior in the complex socio-technical air transportation system.

Delft Aviation proposes Agent-Based Modelling and Simulation (ABMS) as a method for capturing emergent behavior of the socio-technical air transportation system, and evaluating novel system designs. The popularity of ABMS is driven by its capability of handling the increasing complexity of real world socio-technical systems that exhibit emergent behavior. Several case studies have been conducted by our experts using ABMS. E.g. : the identification of emergent safety risk of an active runway crossing operation; the evaluation of the role of coordination in Airline Operations Control (AOC); the evaluation of a new automation concept at a major European airline; and security assessment of an airport.

ABMS has emerged as a key method because it is widely used in complexity science to understand how interactions give rise to emergent behavior. The agent-based models include all relevant human and technical agents, such as pilots, controllers, passengers, and the decision support systems involved. Simulation of these agents interacting together is conducted to predict the impact of both existing and future concepts of operation.

The case studies conducted by Delft Aviation highlight that ABMS has the capability to reveal unexpected emergent behaviour and provide novel insights in air transportation. For instance, it was possible to understand the potential of agents in restricting the risk in off-nominal scenarios. Also, novel insights were gained about the role of coordination in airline operations control helping airlines to improve their disruption management plans and refine their airline controller trainings.

ABMS of air transport operations is a viable approach in gaining knowledge about emergent behavior which was unknown before. This knowledge includes both bottlenecks of system designs and identified opportunities, and hence can be used to control and further optimize the socio-technical air transportation system.  This also implies that ABMS can be a cost-effective method for evaluating new concepts during the early design phase of air transport operations.