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Journal Dynamics of Complex Systems - XXI century №4 for 2021 г.
Article in number:
Representation and modeling of adaptive algorithms for control of transport systems in the form of finite hierarchical automata
Type of article: scientific article
DOI: 10.18127/j19997493-202104-04
UDC: 519.24
Authors:

V.Yu. Stroganov1, E.V. Zelentsova2

1 Bauman Moscow State Technical University (Moscow, Russia)

Abstract:

Formulation of the problem. The article discusses the problem of planning transportation within the framework of one motor transport enterprise (ATP) on the basis of available vehicles (TS). The complexity of the task lies in the presence of restrictions on the ability to fulfill hotel orders with their own vehicles. The problem is also represented by the choice of a mechanism for distributing orders among the existing vehicles when setting various criteria for assessing the efficiency of the ATP for the planning period.

The aim of the work is to build a formal model of algorithms for adaptive traffic management within the ATP's own vehicle fleet for modeling the process of transporting goods and assessing the effectiveness of the chosen strategy for various traffic flow schemes and flows of transportation orders.

Results. There are many different adaptive algorithms for traffic management for a different vehicle fleet. Moreover, each algorithm has its own representation in the form of some formal scheme. The paper proposes a universal mechanism for describing the entire set of adaptive algorithms in the form of a finite state machine. In addition, when considering a multilevel scheme for representing the control structure of ATP, the article proposes a hierarchical automaton scheme for representing adaptive algorithms.

Practical significance. The results obtained make it possible, when choosing a specific control algorithm, to construct a finite hierarchical automaton, which makes it possible to evaluate the effectiveness of the selected control strategy.

Pages: 29-35
For citation

Stroganov V.Yu., Zelentsova E.V. Representation and modeling of adaptive algorithms for control of transport systems in the form of finite hierarchical automata. Dynamics of complex systems. 2021. T. 15. № 4. Р. 29−35. DOI: 10.18127/j19997493-202104-04 (In Russian)

References
  1. Aksenov K.A., Rud' S.A., Rud' S.I., Nevolina A.L. Primenenie mul'tiagentnogo imitacionno-go modelirovaniya pri reshenii zadachi snabzheniya seti stroitel'nyh magazinov. Imitacionnoe modelirovanie. Teoriya i praktika – IMMOD 2015: Trudy VII Vseros. nauch.-prakt. konf.: v 2 t. M.: IPU RAN, 2015. T. 2. S. 13–17 (In Russian).
  2. Zyubin V.E. Programmirovanie informacionno-upravlyayushchih sistem na osnove konechnyh av-tomatov. Novosibirsk: NGU. 2006 (In Russian). 
  3. Karsaev O.V., Morozov B.M., Smirnova G.S., Sabitov R.A. Modelirovanie gruzoperevozok po tekhnologii kamatejner. Imitacionnoe modelirovanie. Teoriya i praktika» – IMMOD 2015: Trudy VII Vseros. nauch.-prakt. konf.: v 2 t. M.: IPU RAN, 2015. T. 2. S. 139–144 (In Russian).
  4. Kogdenko V.G. Strategicheskoe modelirovanie pribyli kompanii metodom Monte-Karlo. Economic Analysis. 2018. № 17(9). S. 1622– 1641 (In Russian). 
  5. Konechnye avtomaty v srede dinamicheskogo modelirovaniya SimInTech – https://habr.com/ru/post/307090. (In Russian).
  6. Lapshina S.N., Berg D.B., Bazhenov I.A. i dr. Imitacionnye modeli v ekonomike dlya izucheniya scenariev razvitiya ekonomicheskih sistem. Ekonomika i upravlenie v mashinostroenii. 2016. № 1. S. 53–55 (In Russian). 
  7. Majorov N.N., Kovalev K.G. Voprosy razrabotki informacionnogo obespecheniya dlya resheniya zadachi marshrutizacii transportnyh sredstv. Sistemnyj analiz i logistika. 2013. Vyp. 9. S. 21–23 (In Russian).
  8. Mirotin L.B., Gudkov V.A., Zyryanov V.V. Upravlenie gruzovymi potokami v transportno-logisticheskih sistemah. M.: Goryachaya liniya – Telekom. 2010 (In Russian).
  9. Palej A.G., Pollak G.A. Imitacionnoe modelirovanie. Razrabotka imitacionnyh modelej sredstvami iWebsim i AnyLogic: Ucheb. posobie. SPb.: Lan'. 2019. 204 s. (In Russian).
  10. Polikarpova N.I., Shalyto A.A. Avtomatnoe programmirovanie. 2008. 167 s. (In Russian). 
  11. Transportnoe planirovanie i modelirovanie: Sb. trudov IV Mezhdunar. nauch.-prakt. konf. [11–12 aprelya 2019 g.]; SPbGASU. SPb., 2019. 194 s. https://www.spbgasu.ru/upload-files/nauchinnovaz/sbornik_trudov/Transportnoe_planirovanie_i_mode.pdf (In Russian).
  12. Coltin B. Multi-agent Pickup and delivery Planning with Transfers: Doctor of Philosophy in Robotics Thesis. 2014. URL: http://www.cs.cmu. edu/~mmv/papers/ColtinThesis.pdf 
  13. Hassani H., and Soofi A. and Zhigljavsky A. Predicting Daily Exchange Rate with Singular Spectrum Analysis, Nonlinear Analysis: Real World Applications. 2010. V. 11. № 3. P. 2023–2034.
  14. Perslev M., Jensen M.H., Darkner S. et al. U-time: A fully convolutional network for time series segmentation applied to sleep staging. Advances in Neural Information Processing Systems. 2019. P. 4415–4426. 
  15. Smirnova G., Sabitov R., Elizarova N. et al. To the problem of dynamic modelling and management in an integrated environment of the industrial cluster. IFAC-PapersOnLine. 2015. V. 43. № 3. P. 1230–1235.
Date of receipt: 22.09.2021
Approved after review: 12.10.2021
Accepted for publication: 10.11.2021