doi: 10.17586/2226-1494-2017-17-3-552-558


ON THE SIMULATION PARADIGM ANALYSIS

O. I. Kutuzov, T. M. Tatarnikova


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For citation: Kutuzov O.I., Tatarnikova T.M. On the simulation paradigm analysis. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2017, vol. 17, no. 3, pp. 552–558 (in Russian). doi: 10.17586/2226-1494-2017-17-3-552-558

Abstract

Subject of Study.We discuss implementation features of the system time promotion in existing simulation paradigms: discrete event, dynamic, system dynamics and multi-agent approach. In the models with continuous processes the value of the promotion step in time is proposed to be chosen according to the Nyquist-Kotelnikov theorem. Methods. The assignment of the system time promotion step is based on cyclic sampling with a constant Dtor a random step. A fixed step is used for dynamic modeling and system dynamics. With discrete-event and agent modeling, both fixed and random steps of the system time promotion are used. When constructing the "mover" of the system time, two main schemes for creation of modeling algorithms are used: the scheme of events and the scheme of processes; the first scheme is used for discrete-event modeling, and the second one for multi-agent modeling. In both cases, the promotion of the system time is performed according to the principle of "special" moments. To determine the next "special" moment, a calendar is used where the closest time of this event is specified for each type of event. Main Results. We have shown the unity of four simulation modeling versions: discrete-event, dynamic, system dynamics and multi-agent. We have substantiated a formalized approach to the choice of the system time promotion step. The schemes of events and processes are compared, realizing different approaches to modeling algorithm creation. Practical Relevance. The unity of paradigms contributes to the implementation of the integrated simulation environment. Recommendations for choosing a step in the system time promotion, given in the paper, enable to speed up the process of modeling and save computing resources.


Keywords: simulation modeling, simulator, system time promotion step, events calendar, scheme of processes and events, randomness simulation, acceleration

References
1.          Lychkina N.N. Innovative paradigms of simulation modeling and their application in the field of management consulting, logistics and strategic management. Logistika i Upravlenie Tsepyami Postavok, 2013, no. 5, pp. 28–41.
2.          Zadorozhnyi V.N., Semenova I.I. Control of complex technical objects and simulation paradigm. Omsk Scientific Bulletin, 2006, no. 2, pp. 102–108. (In Russian)
3.          Banks J., Carson J.S., Nelson B.L., Nicol D.M. Discrete-Event System Simulation. 5thed. PrenticeHall, 2009, 638 p.
4.          Brazhnik A.N. Simulation Modeling: GPSS WORLD Capabilities. St. Petersburg, Renome Publ., 2006, 439 p. (In Russian)
5.          Law A.M., Kelton W.D. Simulation Modeling and Analysis. McGraw-Hill, 1991.
6.          Forrester J.W. Industrial Dynamics. MIT Press, 1961.
7.          Bogatyrev V.A., Parshutina S.A. Redundant distribution of requests through the network by transferring them over multiple paths. Communications in Computer and Information Science,2016,vol. 601, pp. 199–207. doi: 10.1007/978-3-319-30843-2_21
8.          Bogatyrev V.A., Karmanovsky N.S., Poptcova N.A., Parshutin S.A., VoroninаD.A., Bogatyrev S.V. Simulation model for design support of infocomm redundant systems. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 5, pp. 831–838. (In Russian) doi: 10.17586/2226-1494-2016-16-5-831-838
9.          Sovetov B.Ya., Yakovlev S.A. Modeling of Systems. Moscow, Vysshaya Shkola Publ., 2007, 343p. (In Russian)
10.       Bikkenin R.R., Chesnokov M.N. Theory of Electrical Communication. Moscow, Akademiya Publ., 2010, 329 p. (In Russian)
11.       Karpov Yu.G. Simulation Modeling of Systems. Introduction to Modeling with AnyLogic 5. St. Petersburg, BKhV-Peterburg Publ., 2005, 403 p. (In Russian)
12.       Polyak Yu.G. Probabilistic Modeling with a Computer. Moscow, Sovetskoe Radio Publ., 1971, 400 p. (In Russian)
13.       Kutuzov O.I., Tatarnikova T.M. Practical Experience of Using Monte Carlo Method. Industrial Laboratory, 2017, vol. 83, no. 3, pp. 65–70. (In Russian)
14.       Olzoeva S.I. Distributed Modeling in Problems of Control Systems Development. Ulan-Ude, VSGTU Publ., 2005, 219 p. (In Russian)
15.       Kutuzov O.I., Tatarnikova T.M. Infocommunication Networks. Simulation and Evaluation of Probability-Time Characteristics. St. Petersburg, SUAI Publ., 2015, 381 p. (In Russian)


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