USE OF ARTIFICIAL INTELLIGENCE METHODS IN OPERATIONAL PLANNING OF TEXTILE PRODUCTION
Abstract
In this article, the authors analyzed the widely used methods of artificial intelligence in world practice, described the production management system. Also, the main functions are considered and it is determined that the organization of any production begins with its operational planning. And in the basis of production planning functions are various methods and algorithms of artificial intelligence. The authors considered important stages in the construction of the neural network system in planning, and discussed the most common methods and algorithms of artificial intelligence for planning textile production: taboo search method, ant algorithm, genetic algorithms, neural networks.
References
Barsky A.B. (2007) "Logical neural networks" Moscow: Internet-University of Information Technologies.
Beale, H. & Demuth, H.B. (2001). Fuzzy systems toolbox for use with MATLAB, 1st ed., Massachusetts: International Thomson Publishing.
Burmistrov K.V., Tsuprik L.S., Burmistrova I.S., Oshurkov V.A. Osobennostiproyektirovaniyamesierp-sistemnagornodobyvayushchikhpredpriyatiyakh//NauchnyyetrudySWorld. 2014. T. 9. № 4. pp. 94-99.
Castellano, G. & Fanelli, A.M. (2000). Variable selection using neural-network models, Neurocomputing, 31, pp 1-13.
Chusavitina G.N., Makashova V.N. i dr. Povysheniyekonkurentosposobnostivypusknikov IT-spetsial'nosteyvuza v usloviyakhmonopromyshlennogogoroda. Monografiya. Pod red. G.N. Chusavitinoy. -Magnitogorsk: MaGU, 2010. -158 p.
DoroninV.Yu., VolshchukovYu.N., Makashov PL, Romanenko AV, Ishmetev EN, Lednov AV, Makashova V.N. (2011) Building a system for dispatching and controlling technological processes as an element of industrial enterprise management. // Management of large systems. Moscow, рр. 116-119.
El-Shafie, A., Taha, M.R. &Noureldin, A.. (2007). A neuro-fuzzy model for inflow forecasting of the Nile River at Aswan High Dam, Water Resources Management, 21(3), pp 533-556.
Eremenko YI, Tsukanov MA, SolovyevA.Yu. (2013) About application of multi-agent algorithms of ant colonies for the decision of a problem of structural optimization in power systems // Fundamental researches. Vol. 10-15. - pp. 3316-3320; URL: www.rae.ru/fs/?section=content&op=show_article&article_id=10002326 (reference date: 08.10.2015).
Gladkov L.A., Kureichik V.V., Kureichik V.M. (2006) Genetic Algorithms: Textbook. - 2 nd ed. - M: Fizmatlit, p. 320. - ISBN 5-9221-0510-8.
MayorovaYe.S., Oshurkov V.A., Buber M.G. Kontseptsiyatekhnicheskogoikommercheskogoenergouchetapromyshlennykhpredpriyatiy//Aktual'nyyeproblemysovremennoynauki, tekhnikiiobrazovaniya.2015. T. 2. № 1. S. 135-138.
Lohani, A., Goel, N. & Bhatia, K. (2006). Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship, Journal of Hydrology, 331, pp 146-160.
Oshurkov V.A., Tsuprik L.S., MayorovaYe.S., Burmistrov K.V., Burmistrova I.S. Razrabotkasistemyupravleniyatekhnologicheskimprotsessommnogonomenklaturnogomashinostroitel'nogoproizvodstva v usloviyakhprimeneniyastankov s CHPU//Mashinostroyeniye: setevoyelektronnyynauchnyyzhurnal. 2015. T. 3. № 3. S. 53-55.
Tanaka H., Guo P., Zimmermann H.J. (2000) Possibility distribution of fuzzy decision variables obtained from possibilistic linear programming problems, Fuzzy Sets and Systems. pp. 323_332.
Tursunov B. (2017) Vestnik Instituta Rossiyskoy Akademii nauk. Vol.5, 2017.Moscow. 146-156 pp.
Tursunov B. (2017) Role of managing industrial stocksin increasing of textile enterprises capacity.Vol.6, Number 4, Fall 2017.
Yan-FeiLan, Yan-Kui Liu, Gao-Ji Sun. (2009) Modeling fuzzy multi-period production planning and sourcing problem with credibility service levels. Journal of Computational and Applied Mathematics. pp 208_221.
Ying, L.C. & Pan, M.C. (2008). Using adaptive network based fuzzy inference system to forecast regional electricity loads, Energy Conversion and Management, 49(2), pp 205-211.
Zadeh L.A. (1978) Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, Vol. 3, p 28.
Zheng, F. &Zhong, S. (2011). Time series forecasting using a hybrid RBF neural network and AR model based on binomial smoothing, World Academy of Science, Engineering and Technology, 75, pp 1471-1475.
ANSI/ISA-95.00.01-2000, Enterprise-ControlSystemIntegration.