Creating private criteria for A-CDM effectiveness to take into account the interests of decision-making participants in a dynamic environment
https://doi.org/10.26467/2079-0619-2020-23-6-53-64
Abstract
We consider the problem of collaborative decision making of the production process at airlines (CDM) in dynamically changing conditions of occurrence of emergency situations that make changes in the action plan. In the production process, due to the different orientation of the tasks to be solved, the solution may require a large or small number of possible variant solutions. The article presents a concrete example of such a situation affecting the conventional three services of the aviation complex, each with its own interests in the overall production process. The solution to this problem is the only option in favor of the overall production process. For this purpose, several designations and assumptions have been introduced, the list of which can be supplemented. Dynamic priorities are defined for each participant of the process. Optimization of collaborative decision-making can be achieved either by a simple search for solutions, or by using a genetic algorithm that allows you to get a suboptimal solution that meets the requirements of the participants in the process using a smaller number of iterations in real time. In this example, we consider a situation that occurs in a real enterprise due to bad weather conditions. Thus, dynamic priorities are assigned based on a multiplicative form for delayed flights, considering the interests of participants in the process, private criteria are formed for ranking flights at each step of rescheduling, and a genetic algorithm is applied. As a result, we obtained four solutions to the disruption caused by external factors. The first three options correspond to the interests of three parties concerned, and the fourth one is consolidated. All the solutions were different, which indicates the need for an objective and reasonable decision-making apparatus for joint management of the production process. The proposed mathematical apparatus has this ability and prospects for implementation.
About the Authors
G. N. Lebedev
Moscow Aviation Institute (National Research University)
Russian Federation
Russian Federation
Doctor of Technical Sciences, Professor of the Automatic and Intellectual Management Systems Chair,
Moscow
V. B. Malygin
Moscow State Technical University of Civil Aviation
Russian Federation
Russian Federation
Head of the Training Center of the Air Traffic Management Chair,
Moscow
References
1. Zhiltsov, I.E., Mitrofanov, A.K. and Rudelson, L.E. (2018). Air space capacity assessment regarding the problem of the collaborative management of air traffic flows. Civil Aviation High Technologies, vol. 21, no. 2, рp. 83–95. DOI: 10.26467/2079-0619-2018-21-2-83-95. (in Russian)
2. Mikhaylin, D.A. (2017). Flight expert risk assessment of aircraft group at their proximity using a program-manager. Civil Aviation High Technologies, vol. 20, no. 5, pp. 116–130. DOI: 10.26467/2079-0619-2017-20-5-116-130. (in Russian)
3. Mikhaylin, D.A. (2017). Neural network algorithm safe overflight aerial obstacles and prohibited land areas. Civil Aviation High Technologies, vol. 20, no. 4, pp. 18–24. DOI: 10.26467/2079-0619-2017-20-4-18-24. (in Russian)
4. Lebedev, G.N. and Мalygin, V.B. (2019). Formation of private performance criteria acdm taking into account the interests of the participants in the decision-making process in a dynamic environment. Civil Aviation High Technologies, vol. 22, no. 6, pp. 44–54. DOI: 10.26467/2079-0619-2019-22-6-44-54. (in Russian)
5. Mikhaylin, D.A., Alliluyeva, N.V. and Rudenko, E.M. (2018). Comparative analysis of the effectiveness of genetic algorithms the routing of the flight, taking into account their different computational complexity and multicriteria tasks. Trudy MAI, no. 98, 22 p. Available at: http://trudymai.ru/published.php?ID=90386 (accessed 23.09.2020). (in Russian)
6. Zaytsev, А.V. and Talimanchuk, L.L. (2008). Intellektualnaya sistema prinyatiya resheniyy dlya otsenki nauchnoy deyatelnosti na osnove mnogoagentnoy sistemy [Intelligent decision-making system to evaluate scientific activity based on a multi-agent system]. Neurocomputers, no. 7, pp. 85–88. (in Russian)
7. Lugovaya, A.V. and Konovalov, A.E. (2017). Collaborative decision-making on the inbound and outbound air traffic flow in air traffic management. Civil Aviation High Technologies, vol. 20, no. 4, рp. 78–87. DOI: 10.26467/2079-0619-2017-20-4-78-87. (in Russian)
8. Borsoyev, V.A., Lebedev, G.N., Malygin, V.B., Nechayev, Ye.Ye., Nikulin, A.O. and Tin Pkhon Chzho. (2018). Prinyatiye resheniya v zadachakh upravleniya vozdushnym dvizheniyem. Metody i algoritmy [Decision making in air traffic management tasks. Methods and algorithms], in Nechaev Ye.Ye. (Ed.). Moscow: Radiotekhnika, рp. 351–415. (in Russian)
9. Nikulin, A.O. (2018). The system of collaborative decision making as an effective tool for the organization of the airport operation in peak loads. Civil Aviation High Technologies, vol. 21, no. 5, pp. 43–55. DOI: 10.26467/2079-0619-2018-21-5-43-55. (in Russian)
10. Nikulin, A.O. and Popov, A.A. (2015). Implementation procedures A-CDM at the airport Sheremetyevo. Civil Aviation High Technologies, no. 221, pp. 68–80. (in Russian)
11. Nechaev, E.E. and Nikulin, A.O. (2018). The Analytical treatment of the system «SYNCHRON» at Sheremetyevo airport in adverse weather conditions. Civil Aviation High Technologies, vol. 21, no. 6, pp. 31–42. DOI: 10.26467/2079-0619-2018-21-6-31-42. (in Russian)
12. Gabeydulin, R.Kh. (2018). The dynamic air traffic flow management by assigning ground flight delays. Trudy GosNII AS. Seriya: Voprosy avioniki, no. 2, pр. 39–53. (in Russian)
13. Rudelson, L.E. (2008). Programmnoye obespecheniye avtomatizirovannykh system upravleniya vozdushnym dvizheniem. Chast I. Sistemnoye programmnoye obespecheniye. Kniga 2, Operatsyonnyye sistemy realnogo vremeni. Matematicheskiye modeli: uchebnoye posobiye [Software for automated air traffic control systems. Part I. System software. B2. Real-time operating systems. Mathematical models: Tutorial]. Moscow: MGTU GA, 96 p. (in Russian)
14. Sobol, E.M. and Statnikov, R.B. (1981). Vybor optimalnykh parametrov v zadachakh so mnogimi kriteriyami [Selection of optimal parameters in tasks with many criteria]. Moscow: Nauka, 110 р. (in Russian)
15. Ignashchenko, Е.U., Pankov, А.R. and Semenikhin, К.V. (2010). Minimax-statistical approach to increasing reliability of measurement information processing // Avtomatika i Telemehanika, no. 2, pp. 76–91. (in Russian)
Review
For citations:
Lebedev G.N., Malygin V.B. Creating private criteria for A-CDM effectiveness to take into account the interests of decision-making participants in a dynamic environment. Civil Aviation High Technologies. 2020;23(6):53-64. (In Russ.) https://doi.org/10.26467/2079-0619-2020-23-6-53-64
Views:
436
Email this article 
