Devising a model of optimal control over the logistics system under uncertainty

Authors

  • Владимир Алексеевич Доровской Kerch State Maritime Technological University 82 Ordzhonikidze str., Kerch, Crimea Republic, 298309, https://orcid.org/0000-0002-2716-9610
  • Александр Александрович Железняк Kerch State Maritime Technological University 82 Ordzhonikidze str., Kerch, Crimea Republic, 298309, Russian Federation https://orcid.org/0000-0001-8693-7440
  • Ольга Витальевна Бабина Kerch State Maritime Technological University 82 Ordzhonikidze str., Kerch, Crimea Republic, 298309,
  • Инна Викторовна Антипенко Kerch State Maritime Technological University 82 Ordzhonikidze str., Kerch, Crimea Republic, 298309, https://orcid.org/0000-0002-4301-5056
  • Владислав Юрьевич Будник Kerch State Maritime Technological University 82 Ordzhonikidze str., Kerch, Crimea Republic, 298309, https://orcid.org/0000-0001-8012-9346

DOI:

https://doi.org/10.15587/1729-4061.2016.60838

Keywords:

optimization criterion, parametric model, the normalized values of profit, terminal control, two-level optimal control, logistics system, uncertainty, multi-factorial assessment, optimistic criterion, mathematical identification models

Abstract

An objective of the study is to improve the quality of decisions via formalizing each specific optimization problem in the analysis of the logistics system under uncertainty. In multi-criterion conditions, the most promising approach to the assessment problem is the formation of a generalized scalar multi-factor assessment of Р (х) on a set of particular criteria , which requires solving the problem of a structural and parametric identification of the models of formation – P1 (x).

The principal peculiarity of this problem stems from the fact that the assessment procedure is an intellectual process of the decision maker (DM) or experts, i. e. it is necessary to identify the model of intellectual activity. The baseline data of any task for optimization of the logistics system (OLS) consist of uncertain and determined values of various types. The study has determined optimization criteria, controlled variables, and their limitations, which allowed devising new mathematical identification models and optimization techniques. The devised algorithm allows determining the necessary conditions for optimal solutions. The formulated theorem specifies the conditions for obtaining an optimal solution to optimize the tracking mode of terminal control under uncertainty. Its peculiar feature is the choice of three initial prerequisites, namely: parametric models, an optimality criterion, and optimal solutions. The obtained results of a two-stage optimization can serve as a basis for constructing specific optimal control systems that use two modes, such as “a search for the optimum (reference) value of the controlled variable” and “tracking of the optimum value.” They also allow scientific justifying, posing and solving the problem of identification of a two-level model of optimal control over the volume and cost of the logistics system in accordance with the two most important criteria for practical application: the criterion of a maximum profit and the criterion of a minimum cost.

Author Biographies

Владимир Алексеевич Доровской, Kerch State Maritime Technological University 82 Ordzhonikidze str., Kerch, Crimea Republic, 298309

Doctor of technical sciences, Professor

Department of "Electrical equipment of ships and industrial automation"

Александр Александрович Железняк, Kerch State Maritime Technological University 82 Ordzhonikidze str., Kerch, Crimea Republic, 298309

Assistant

Department of "Electrical equipment of ships and industrial automation"

Ольга Витальевна Бабина, Kerch State Maritime Technological University 82 Ordzhonikidze str., Kerch, Crimea Republic, 298309

Teacher

Department of Business Economy

Инна Викторовна Антипенко, Kerch State Maritime Technological University 82 Ordzhonikidze str., Kerch, Crimea Republic, 298309

Teacher

Department of Business Economy

Владислав Юрьевич Будник, Kerch State Maritime Technological University 82 Ordzhonikidze str., Kerch, Crimea Republic, 298309

Postgraduate student

Department of "Electrical equipment of ships and industrial automation"

References

  1. Sergeeva, V. I. (Ed.) (2004). Korporativnaja logistika. 300 otvetov na voprosy professionalov. Infra-M, 967.
  2. Stok, D. R., Lambert, D. M. (2005). Strategicheskoe upravlenie logistikoj. INFRA, 797.
  3. Balashov, E. P. (1985). Jevoljucionnyj sintez system. Radio i Svjaz', 328.
  4. Raskin, L. G., Seraja, O. V. (2008). Nechetkaja matematika. Kharkiv: Parus, 352.
  5. Sergeeva, V. I. (Ed.) (2007). Prakticheskaja jenciklopedija. Logistika. MCFJeR, 320.
  6. Balabanov, I. T. (2000). Finansovyj analiz i planirovanie hozjajstvujushhego subekta. Finansy i statistika, 300.
  7. Arіon, O. V. (2008). Organіzacіja transportnogo obslugovuvannja turistіv. Al'terpres, 192.
  8. Paladich, L. (1989). Morskie kruizy (Morskoj turizm). Znanie, 64.
  9. Nikiforova, E. S., Leckiy, Je. K., Chelnokov, N. I. (Eds.) (1970). Metod regressionnogo analiza. Planirovanie jeksperimenta (algoritmy na jazyke Algol-60). Trudy MJeI, 76.
  10. Efroymson, M. A.; Ralston, A., Wilf, H. S. (Eds.) (1960). Multiple regression analysis. Mathematical Methods for Digital Computer., Wley, New York.
  11. Chernyj, S. G., Logunova, N. A. (2014). Razrabotka segmentov klasterov koordinacii otraslevoj napravlennosti. Mir transporta, 3 (52), 104–115.
  12. Chernyi, S. (2015). The implementation of technology of multi-user client-server applications for systems of decision making support. Metallurgical and Mining Industry, 3, 60–65.
  13. Seraya, O. V., Demin, D. A. (2012). Linear Regression Analysis of a Small Sample of Fuzzy Input Data. Journal of Automation and Information Sciences, 44 (7), 34–48. doi: 10.1615/jautomatinfscien.v44.i7.40
  14. Logunova, N., Chernyi, S., Semenova, A., Antypenko, I. (2015). Modeling the development of complex structures on the example of the maritime industry. Eastern-European Journal of Enterprise Technologies, 6/2 (78), 36–46. doi: 10.15587/1729-4061.2015.56030
  15. Hatcher, W. S., Bunge, M. (1982). The Logical Foundations of Mathematics .A volume in Foundations and Philosophy of Science and Technology Series. Elsevier Ltd.

Published

2016-02-27

How to Cite

Доровской, В. А., Железняк, А. А., Бабина, О. В., Антипенко, И. В., & Будник, В. Ю. (2016). Devising a model of optimal control over the logistics system under uncertainty. Eastern-European Journal of Enterprise Technologies, 1(4(79), 4–9. https://doi.org/10.15587/1729-4061.2016.60838

Issue

Section

Mathematics and Cybernetics - applied aspects