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Modeling and Assessment of Production Cycle Information Entropy Under Joint Activities

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Proceedings of the 6th International Conference on Industrial Engineering (ICIE 2020) (ICIE 2021)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

Development of optimal interaction scenario of industrial facilities under joint activities is a problem requiring taking into account strategic planning and tactical coordination of goals and scenarios of participants of production cycle. The collaborative scenario is proposed to be presented as an interrelated set of scenarios of participants resulting in a common strategy by eliminating contradictions and fixing common goals. Objective functions of joint activities and criteria for achieving them are formulated. Balanced scorecard of an enterprise is created and used for joint activities monitoring and control as well as dynamic assessment of goal achievement, harmonization and correction of operational plans at all stages of production cycle. A model and a method of assessing the degree of trust in operational processes of participants are developed along with subsequent control measures in case of deviation from the interaction scenario. The model and the method are illustrated by numerical modeling example of optimization of inventory nomenclature to be purchased. The space of states of the process under investigation is formed and transition probabilities are determined as a solution of corresponding Kolmogorov’s system of differential equations. Information entropy is calculated and compliance of the parties of joint activities is estimated according to the method of Nikolayev-Temnov. Modeling results confirm the correlation between entropy growth and compliance decrease and, as a result, performance sustainability degradation. Monitoring entropy can serve a tool of joint production cycle maintenance.

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References

  1. Dulesov AS, Kondrat NN (2015) Opredelenie dlya prostejshej struktury` tekhnicheskoj sistemy` kolichestva informacionnoj entropii posredstvom eyo normirovki (Determination for the simplest structure of the technical system of the amount of information entropy through its normalization). Fundamental`ny`eissledovaniya (Fundamental Research) 2–20:4408–4412

    Google Scholar 

  2. Ma RG, Xu H, Liu WY, Wang X, Cheng CA (2019) Dynamic Yardstick Evaluation Approach for Assessing Development and Production Management Status of Grass-roots. Distrib Util IOP Conf Ser: Earth Environ Sci 223(1):012045

    Google Scholar 

  3. Bychkova SM, Makarova NN, Zhidkova EA (2018) Measurement of information in the subsystem of internal control of the controlling system of organizations of the agro-industrial complex. Entrepreneurship Sustainability 6(1):35–43

    Article  Google Scholar 

  4. Kim S (2015) Unsupervised spectral-spatial feature selection-based camouflaged object detection using VNIR hyperspectral camera. Scientific World Journal, 834635

    Google Scholar 

  5. Kozulia T, Bilova M, Kozulia M (2015) Environmental assessment development of anthropogenic objects using comparator identification method Eastern-European. J Enterp Technol 5(10):27–33

    Google Scholar 

  6. Chen C-B, Wang L-Y (2006) Rough set-based clustering with refinement using shannon’s entropy theory. Comput Math Appl 52(10–11):1563–1576

    Article  MathSciNet  Google Scholar 

  7. Yudin SV (2018) Metodika rascheta informacionnykh planov statisticheskogo priemochnogo kontrolya na osnove bajesovskogo podkhoda (Methodology of calculation of information plans of statistical acceptance control on the basis of Bayesian approach). Sovremenny`enaukoemkie tekhnologii (Modern knowledge-intensive technologies) 11-1:90–94

    Google Scholar 

  8. Loginovskiy OV, Dranko OI, Hollay AV (2018) Mathematical Models for Assessment of Activity of Industrial Enterprises under the Conditions of Instability.Bulletin of the South Ural State University. Ser. Comput Technol, Autom Control, Radio Electron 18(4):88–102

    Google Scholar 

  9. Novikov DA (2008) Matematicheskie Modeli Formirovaniya i Funkcionirovaniya Komand (Mathematical models of formation and functioning of commands). Publishing House of Physical and Mathematical Literature, Moscow

    Google Scholar 

  10. Vinogradov GP, Burdo GB, Isaev AA (2015) A decision-making in high-tech products production systems. Softw Syst 2(110):75–82

    Google Scholar 

  11. Kulagovskiy EV (2016) Metodological tools for risk assessment industrial enterprises. Scientif-Technical J “Bull Civil Eng” 5(58):181–185

    Google Scholar 

  12. Gushchina EG, Vitalyeva EM, Volkov SK (2017) Influence of Asymmetry of information on the economic growth. Vestnik of Astrakhan State Technical University. Ser: Econ 4:7–14

    Google Scholar 

  13. Kaplan Robert S, Norton David P (2003) Sbalansirovannaya sistema pokazateley. (Balanced Scorecard). ZAO “Olimp –biznes”, Moscow

    Google Scholar 

  14. Podvalny SL (2016) Creation of Indirect control models in Information Computer Systems. Vestnik Voronezhskogogo sudarstvennogo tekhnicheskogo universiteta (Bulletin of Voronezh State Technical University) 12(6):44–51

    Google Scholar 

  15. Nikolayev VI, Brooke VM (1985) Systemotekhnika: Metody I Prilozhenia (Systems Engineering: Methods and Applications). Otdelenie, Leningrad, Mashinostroenie, Leningr

    Google Scholar 

  16. Nikolayev VI, Temnov VN (1972) On one method for determining objective and subjective value of data in control. Autom Remote Control 33(9):1521–1526

    MATH  Google Scholar 

  17. Dulesov AS, Khrustalev VI (2012) Entropy Definition as Information Measure by Comparison of Look-ahead and Actual Indicators of the Enterprise. Sovremennye Problemy Nauki I Obrazovaniya (Modern Problems of Science and Education) 1:151

    Google Scholar 

  18. Greyz GM, Kuzmenko YuG, Okolnishnikova IYu (2018) Entropy as a Status Indicator of the Industrial Enterprise Logistic System. Bulletin of Udmurt University. Ser Econ Law 28(1):7–14 (In Russian)

    Google Scholar 

  19. Kolmogorov AN (1938) Ob Analiticheskikh Metodakh v Teorii Veroyatnostei (On Analytical Methods in Probability Theory). Uspekhi Matematicheskikh Nauk (Successes of Mathematical Sciences) 5:5–41

    Google Scholar 

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Correspondence to R. Fatkieva .

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Fatkieva, R., Evnevich, E., Vasiliev, A. (2021). Modeling and Assessment of Production Cycle Information Entropy Under Joint Activities. In: Radionov, A.A., Gasiyarov, V.R. (eds) Proceedings of the 6th International Conference on Industrial Engineering (ICIE 2020). ICIE 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-54817-9_72

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  • DOI: https://doi.org/10.1007/978-3-030-54817-9_72

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-54816-2

  • Online ISBN: 978-3-030-54817-9

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