Multidimensional routing with the increased navigation accuracy with the maintenance of flight notifications of the flight vehicles
https://doi.org/10.26467/2079-0619-2021-24-4-28-37
Abstract
The article covers the problem the multidimensional routing of flights for the transportation of cargo and mail, with the condition of the corresponding equipment presence for performing navigation of increased precision to obtain the possibility of the formation flights under any weather conditions. The given circumstances are capably essential to reduce load while using the airspace, which will make it possible to achieve transportation independent of its saturation. While planning the routes it is also necessary to consider the interests of different interested groups, which are often opposite to one another. In the view of the different directivity of the tasks in question, the solution can require the sorting as excessively as large, so the smaller quantity of possible situations (versions of the solution), the lower the level of the calculation of these versions is, and the greater their quantity is. The exact example of multidimensional routing, which is affected by the interests of operational nature and the interests of the urgency of the performance of the claims, expressed by weight coefficients, is depicted in this work. The only version in favour of the general production process, which is obtained with the help of a genetic algorithm, is a solution of this problem. It was necessary to introduce some designations and assumptions, the enumeration of which can be supplemented. Optimal solution can be obtained both by the enumeration of the solution versions and with the help of the genetic algorithm, which is allowed for a smaller number of iterations, to obtain suboptimal in real time, which corresponds to the conditions of the task solution. In that the example dynamic priorities are assigned, based on multiplicative form by expert evaluation, which form criteria for the ranking of request for each step of route planning. As a result, there are the exact versions of the solution, which correspond to the interests of different groups and the version, obtained with the help of a genetic algorithm, which satisfy the opposite interests of these groups. All versions of the solution are proved to be different, which indicates the need of applying the objective and substantiated apparatus for making the decision, which the genetic algorithm actually is. The proposed mathematical apparatus has prospects for implementation.
About the Authors
V. I. GoncharenkoRussian Federation
Vladimir I. Goncharenko, Doctor of Technical Sciences, Professor, The Director of the Institute of Military Education
Moscow
G. N. Lebedev
Russian Federation
Georgy N. Lebedev, Doctor of Technical Sciences, Professor of the Automatic and Intellectual Management Systems Chair
Moscow
V. B. Malygin
Russian Federation
Vyacheslav B. Malygin, Head of the Training Centre of the Air Traffic Management Chair
Moscow
References
1. Dixon, L.C.W. and Szegö, G.P. (eds.). (1975). Towards global optimization. Proceedings of a Workshop at the University of Cagliari, Italy, October 1974. Amsterdam-Oxford, North-Holland Publ, 472 p. DOI: 10.1002/zamm.19790590220
2. Ichida, K. and Fujii, Y. (1979). An interval arithmetic method for global optimization. Computing, vol. 23, no. 1, pp. 85–97. DOI: 10.1007/BF02252616
3. Hansen, E.R. (1979). Global optimization using interval analysis: The one-dimensional case. Journal of Optimization Theory and Applications, vol. 29, no. 3, pp. 331–344. DOI: 10.1007/BF00933139
4. Sobol, E.M. and Statnikov, R.B. (1981). Vybor optimalnykh parametrov v zadachakh so mnogimi kriteriyami [Selection of efficient parameters in tasks with many criteria]. Moscow: Nauka, 110 p. (in Russian)
5. Gopalakrishnan, K. and Balakrishnan, H. (2021). Control and optimization of air traf-fic networks. Annual Review of Control, Robotics, and Autonomous Systems, vol. 4, pp. 397–424. DOI: 10.1146/annurev-control-070720-080844
6. Saaty, T.L. (1961). Elements of Queuing Theory. McGraw-Hill, New York, 423 p.
7. 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: 13.03.2021). (in Russian)
8. Ozlem, S.M. (2015). Optimum arrival routes for flight efficiency. Journal of Power and Energy Engineering, no. 3, pp. 449–452. DOI: 10.4236/jpee.2015.34061
9. Lugovaya, A.V. and Konovalov, A.E. (2017). Collaborative decision-making on the in-bound 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)
10. Lebedev, G. and Мalygin, V. (2019). Formation of private performance criteria A-CDM taking into account the interests of the participants in the decision-making process in a dynamic en-vironment. Civil Aviation High Technologies, vol. 22, no. 6, pp. 44‒54. DOI: 10.26467/2079-0619-2019-22-6-44-54 (in Russian)
11. Aliev, T.I. (2009). Osnovy modelirovaniya diskretnykh system [Bases of the discrete sys-tems’ simulation]. St. Petersburg: SPbGU ITMO, 363 p. (in Russian)
12. Goncharenko, V.I., Rozhnov, A.V. and Tseplov, G.I. (2018). Planirovaniye i koordi-natsiya marshrutov polota bespilotnykh aviatsionnykh sistem v interesakh organizatsii i otsenki kachestva sistem podvizhnoy svyazi [Planning and the coordination of the flight courses for un-manned aviation systems in the interest of organization and quality control of the mobile systems]. Raspredelennyye kompyuternyye i telekommunikatsionnyye seti: upravleniye, vychisleniye, svyaz: materialy 21-y Mezhdunarodnoy nauchnoy konferentsii (DCCN-2018) [The distributed computer and telecommunication networks: control, calculation, the connection: proceedings of the 21st interna-tional scientific conference (DCCN-2018, Moscow)]. Moscow: RUDN, pp. 220‒229. (in Russian)
13. Evdokimenkov, V.N., Krasilschikov, M.N., Sebryakov, G.G. and Lyapin, N.A. (2019). Algoritmy i programmno-matematicheskoye obespecheniye bortovoy komponenty raspredelennoy sis-temy intelektualnogo upravleniya gruppoy bespilotnykh letatelnykh apparatov [Algorithms and mathematical programmed software of an airborne component of the distributed system for intellec-tual control with the group of unmanned flying vehicles]. Metody i modeli iskusstvennogo intellekta i ikh prilozheniya v kompyuternoy lingvistike, neyrofiziologicheskikh issledovaniyakh i meditsine. Fundamentalnyye problemy gruppovogo vzaimodeystviya robotov: materialy XII Multikonferentsii po problemam upravleniya (MKPU-2019) [Methods and models of artificial intelligence and their applications in computational linguistics, neurophysiological research and medicine. Fundamental problems of group interaction of robots: proceedings of the XII Multiconference on Control Problems (MKPU 2019)]. Rostov-on-Don: Yuzhnyy federalnyy universitet, pp. 141–143. (in Russian)
14. Rebrov, V.A., Rudelson, L.E. and Chernikova, M.A. (2007). A model of flight request collection and processing in the flight scheduling problem. Journal of Computer and Systems Scienc-es International, vol. 46, no. 3, pp. 429–443.
15. Kim, N.V. and Krylov, I.G. (2012). Using a group of unmanned aerial vehicle in the task of monitoring. Trudy MAI, no. 62, 11 p. Available at: http://trudymai.ru/upload/iblock/bbb/gruppovoe-primenenie-bespilotnogo-letatelnogo-apparata-v-zadachakh-nablyudeniya.pdf?lang=en&issue=62 (accessed: 13.03.2021). (in Russian)
16. Andreev, M.A., Miller, A.B., Miller, B.M. and Stepanyan, K.V. (2012). Path planning for unmanned aerial vehicle under complicated conditions and hazards. Journal of Computer and Systems Sciences International, vol. 51, no. 2, pp. 328‒338.
Review
For citations:
Goncharenko V.I., Lebedev G.N., Malygin V.B. Multidimensional routing with the increased navigation accuracy with the maintenance of flight notifications of the flight vehicles. Civil Aviation High Technologies. 2021;24(4):28-37. https://doi.org/10.26467/2079-0619-2021-24-4-28-37