Creation and development of logistic systems of information support for decision making at the stage of after-sale service
Keywords:
Aviation, decision-making, fuzzy logic, information support, logistics, multi-agent systems, operational technologies.
Abstract
In this article we discuss about the problematic aspects of constructing logistics systems for decision support information systems used at the stage of after-sales servicing of aircraft, based on an analysis of the modern organization of their design, principles of a systematic approach and fuzzy logic.
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References
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Chutia R., Mahanta S., Baruah H.K. (2010). Alternative Method of Finding the Membership of a Fuzzy Number. International Journal of Latest Trends in Computing. 69–72.
Demidova L.A., Kirakovskiy V.V., Pylkin A.N. (2005). Algorithms and fuzzy inference systems for solving problems of diagnosing urban engineering communications in the MATLAB environment. Moscow: Radio Communications - Hotline – Telecom.
Dubois D., Hullermeier E. (2007). Comparing Probability Measures Using Possibility Theory: Notion of Relative Peakedness. International Journal of Approximate Reasoning, 364-385.
Dubois D., Prade H. (1990). Scalar Evaluation of Fuzzy Sets: Overview a nd Applications. Applied Math. Letters. 37-42.
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Golomazov A.V. (2019). Decision support information method implemented in a multi-agent system environment. Proceedings of the Moscow Aviation Institute. 106, 1-22.
Golomazov A.V., Smirnov N.Ya. (2016). Features of the use of multi-agent systems in the subject areas of transport logistics in the face of uncertainty. Advances in modern science and education. 11(2), 9-15.
Goswami P., Dutta P., Baruah H.K. (1997). Latin Square Design Using Fuzzy Data. Journal of Fuzzy Mathematics. 767-779.
Kofman A. (1982). Introduction to the theory of fuzzy sets. Мoscow: Radio Communications.
Komarova N.V., Zamkovoi A.A., Novikov S.V. (2018). The Fourth Industrial Revolution and Staff Development Strategy in Manufacturing. Russian Engineering Research, 39(4), 330–333.
Kruglov V.V., Dli M.I., Golunov R.Yu. (2001). Fuzzy logic and artificial neural networks. Moscow: Fizmatlit.
Kumar G.T., Poornaselvan K.J., Sethumadhavan M. (2010). Fuzzy support vector machine-based multi-agent optimal path planning approach to robotics environment. Defense science journal, 60(4), 387-391.
Li C., Guo L., Li Z. (2014). Design of decision-making system of emergency logistics information system based on data mining. Journal of Digital Information Management. 12(6), 383-386.
Li H., Duan X., Liu Z. (2010). Three-dimensional fuzzy logic system for process modeling and control. Journal of Control Theory and Applications. 8(3), 280-285.
Li S., Li J.Z. (2010). Agents International: Integration of multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making. Expert Systems with Applications. 37(3), 2580-2587.
Liou J.J.H., Yen L., Tzeng G. (2008). Building an effective safety management system for airlines. Journal of Air Transport Management. 14(1), 20-26.
Mahanta S., Chutia R., Baruah H.K. (2010). Fuzzy Arithmetic Without Using the Method of α – Cuts. International Journal of Latest Trends in Computing. 73-80.
Minaev Yu.N., Filimonova O.Yu., Benameur L. (2003). Methods and algorithms for solving identification and forecasting problems in the face of uncertainty in a neural network logical basis. Moscow: Hotline - Telecom.
Novikov S.V, Veas Iniesta D.S. (2018). State regulation of the development of the connectivity of the Russian territory. Espacios, 39(45), 20.
Novikov S.V. (2017). The formation prospects of the command culture of the organization management thinking in the new paradigm of social and economic development of the society. Journal of Applied Economic Sciences, 12(7), 1996-2002.
Novikov S.V. (2018). Russian Support for Innovation and Export Growth. Russian Engineering Research, 38(4), 305-308.
Novikov S.V. (2019). The features of innovative processes in the Russian Federation: Analysis of current practices. Espacios. 39(39), 10.
Orlovsky S.A. (1981). Decision making problems with fuzzy information. Мoscow: Science.
Pamučar D, Lukovac V, Pejčić-Tarle S. (2013). Application of adaptive neuro fuzzy inference system in the process of transportation support. Asia Pacific Journal of Operational Research. 30(2).
Pospelov D.A. (1986). Situational management: theory and practice. Мoscow: Science.
Saati T. (1989). Making decisions. Hierarchy Analysis Method. Мoscow: Radio Communications.
Semenova E.G., Smirnova M.S., Tushavin V.A. (2014). Decision making support system in multi-objective issues of quality management in the field of information technology. International Journal of Applied Engineering Research. 9(22), 16977-16984.
Sheremetov L.B., Contreras M, Valencia C. (2004). Intelligent multi-agent support for the contingency management system. Expert Systems with Applications. 26(1), 57-71.
Smirnov A., Pashkin M., Chilov N., Levashova T. (2004). Knowledge logistics in information grid environment. Future Generation Computer Systems. 20(1), 61-79.
Uskov A.A., Kuzmin A.V. (2004). Intelligent management technology. Artificial neural networks and fuzzy logic. Moscow: Hotline - Telecom.
Utkin L.V., Shubinskiy I.B. (2000). Unconventional methods for assessing the reliability of information systems. St. Petersburg: Lubavich.
Wittbrodt P, Paszek A. (2015). Decision support system of machining process based on the elements of fuzzy logic. International Journal of Modern Manufacturing Technologies. 7(1), 81-85.
Chutia R., Mahanta S., Baruah H.K. (2010). Alternative Method of Finding the Membership of a Fuzzy Number. International Journal of Latest Trends in Computing. 69–72.
Demidova L.A., Kirakovskiy V.V., Pylkin A.N. (2005). Algorithms and fuzzy inference systems for solving problems of diagnosing urban engineering communications in the MATLAB environment. Moscow: Radio Communications - Hotline – Telecom.
Dubois D., Hullermeier E. (2007). Comparing Probability Measures Using Possibility Theory: Notion of Relative Peakedness. International Journal of Approximate Reasoning, 364-385.
Dubois D., Prade H. (1990). Scalar Evaluation of Fuzzy Sets: Overview a nd Applications. Applied Math. Letters. 37-42.
Feng F, Pang Y, Lodewijks G, Li W. (2017). Collaborative framework of an intelligent agent system for efficient logistics transport planning. Computers and Industrial Engineering. 112, 551-567.
Golomazov A.V. (2019). Decision support information method implemented in a multi-agent system environment. Proceedings of the Moscow Aviation Institute. 106, 1-22.
Golomazov A.V., Smirnov N.Ya. (2016). Features of the use of multi-agent systems in the subject areas of transport logistics in the face of uncertainty. Advances in modern science and education. 11(2), 9-15.
Goswami P., Dutta P., Baruah H.K. (1997). Latin Square Design Using Fuzzy Data. Journal of Fuzzy Mathematics. 767-779.
Kofman A. (1982). Introduction to the theory of fuzzy sets. Мoscow: Radio Communications.
Komarova N.V., Zamkovoi A.A., Novikov S.V. (2018). The Fourth Industrial Revolution and Staff Development Strategy in Manufacturing. Russian Engineering Research, 39(4), 330–333.
Kruglov V.V., Dli M.I., Golunov R.Yu. (2001). Fuzzy logic and artificial neural networks. Moscow: Fizmatlit.
Kumar G.T., Poornaselvan K.J., Sethumadhavan M. (2010). Fuzzy support vector machine-based multi-agent optimal path planning approach to robotics environment. Defense science journal, 60(4), 387-391.
Li C., Guo L., Li Z. (2014). Design of decision-making system of emergency logistics information system based on data mining. Journal of Digital Information Management. 12(6), 383-386.
Li H., Duan X., Liu Z. (2010). Three-dimensional fuzzy logic system for process modeling and control. Journal of Control Theory and Applications. 8(3), 280-285.
Li S., Li J.Z. (2010). Agents International: Integration of multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making. Expert Systems with Applications. 37(3), 2580-2587.
Liou J.J.H., Yen L., Tzeng G. (2008). Building an effective safety management system for airlines. Journal of Air Transport Management. 14(1), 20-26.
Mahanta S., Chutia R., Baruah H.K. (2010). Fuzzy Arithmetic Without Using the Method of α – Cuts. International Journal of Latest Trends in Computing. 73-80.
Minaev Yu.N., Filimonova O.Yu., Benameur L. (2003). Methods and algorithms for solving identification and forecasting problems in the face of uncertainty in a neural network logical basis. Moscow: Hotline - Telecom.
Novikov S.V, Veas Iniesta D.S. (2018). State regulation of the development of the connectivity of the Russian territory. Espacios, 39(45), 20.
Novikov S.V. (2017). The formation prospects of the command culture of the organization management thinking in the new paradigm of social and economic development of the society. Journal of Applied Economic Sciences, 12(7), 1996-2002.
Novikov S.V. (2018). Russian Support for Innovation and Export Growth. Russian Engineering Research, 38(4), 305-308.
Novikov S.V. (2019). The features of innovative processes in the Russian Federation: Analysis of current practices. Espacios. 39(39), 10.
Orlovsky S.A. (1981). Decision making problems with fuzzy information. Мoscow: Science.
Pamučar D, Lukovac V, Pejčić-Tarle S. (2013). Application of adaptive neuro fuzzy inference system in the process of transportation support. Asia Pacific Journal of Operational Research. 30(2).
Pospelov D.A. (1986). Situational management: theory and practice. Мoscow: Science.
Saati T. (1989). Making decisions. Hierarchy Analysis Method. Мoscow: Radio Communications.
Semenova E.G., Smirnova M.S., Tushavin V.A. (2014). Decision making support system in multi-objective issues of quality management in the field of information technology. International Journal of Applied Engineering Research. 9(22), 16977-16984.
Sheremetov L.B., Contreras M, Valencia C. (2004). Intelligent multi-agent support for the contingency management system. Expert Systems with Applications. 26(1), 57-71.
Smirnov A., Pashkin M., Chilov N., Levashova T. (2004). Knowledge logistics in information grid environment. Future Generation Computer Systems. 20(1), 61-79.
Uskov A.A., Kuzmin A.V. (2004). Intelligent management technology. Artificial neural networks and fuzzy logic. Moscow: Hotline - Telecom.
Utkin L.V., Shubinskiy I.B. (2000). Unconventional methods for assessing the reliability of information systems. St. Petersburg: Lubavich.
Wittbrodt P, Paszek A. (2015). Decision support system of machining process based on the elements of fuzzy logic. International Journal of Modern Manufacturing Technologies. 7(1), 81-85.
Published
2019-10-11
How to Cite
Golomazov, A., Smirnov, N., & Iosifov, P. (2019). Creation and development of logistic systems of information support for decision making at the stage of after-sale service. Amazonia Investiga, 8(23), 191-202. Retrieved from https://amazoniainvestiga.info/index.php/amazonia/article/view/862
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