Software development for the integration of decision support models


DOI: 10.34759/trd-2022-123-19

Аuthors

Kurennykh A. E.

e-mail: alexey.kurennykh@gmail.com

Abstract

The article deals with the issues of decision support and development of recommendations using computer modeling and methods for increasing the consistency of judgments, as well as issues of integration between information systems. The method for using sets of parameters and results of computer modelling in the process of multi-criteria evaluation of alternatives is proposed and formalized. This paper regards a possible solution to the problem of usage of simulation models in the process of decision-making support carried out with enterprises information systems. The integration of simulation models and models of decision support is set-theoretically formalized. Developed software form a separate module in the decision support system that makes it possible to rank alternatives submitted by simulation models. Designed architecture allows applying this approach for variable scientific and technical civil and military problems due to its universality.

In addition, in this paper the author formalized and developed mathematical and software to improve the consistency of judgments on the example of the method of paired comparisons, providing a sketch for an effective method to increase the consistency of judgments in a pairwise comparison matrix. Initially, there were identified criteria that are of great importance for experts who make judgments and then proposed a multi-criteria optimization task and a way to solve it. Basically, the method is based on well-known properties of matrixes containing paired comparisons marks, such as transitivity of judgments or consistency index for example. The use of both methods: integration of computer models and judgments consistency allows carrying out multicriteria analysis effectively with high precision.

Keywords:

decision support, integration, recommender development

References

  1. Ershova I., Ershov A. Development of a Strategy of Import Substitution, Procedia Economics and Finance, 2016, vol. 39, pp. 620-624. DOI: 10.1016/S2212-5671(16)30308-2

  2. Ali M., Cullinane J. A Study to Evaluate the Effectiveness of Simulation based Decision Support System in ERP Implementation in SMEs, Procedia Technology, 2014, vol. 16, pp. 542-552. DOI: 10.1016/j.protcy.2014.10.002

  3. Guzman-Moratto H., Uribe-Martes C., Neira-Rodado D. Improving productivity using simulation: Case study of a mattress manufacturing process, Procedia Computer Science, 2022, vol. 198, pp. 650-655. DOI: 10.1016/j.procs.2021.12.301

  4. Singh P.l., Singari R.M., Mishra R.S. A review of study on modeling and simulation of additive manufacturing processes, Materials Today: Proceedings, 2021. DOI: 10.1016/j.matpr.2021.12.057

  5. Alves C.L., A. De Noni Jr, Janssen R., Hotza D., Rodrigues Neto J.B., Gómez González S.Y., Dosta M. Integrated process simulation of porcelain stoneware manufacturing using flowsheet simulation, CIRP Journal of Manufacturing Science and Technology, 2021, vol. 33, pp. 473-487. DOI: 10.1016/j.cirpj.2021.04.011

  6. Protsenko P.A., Khubbiev R.V. Trudy MAI, 2021, no. 119. URL: http://trudymai.ru/eng/published.php?ID=159796 DOI: 10.34759/trd-2021-119-18

  7. Bhadoria R.S., Chaudhari N.S., Tomar G.S. The Performance Metric for Enterprise Service Bus (ESB) in SOA system: Theoretical underpinnings and empirical illustrations for information processing, Information Systems, 2017, vol. 65, pp. 158-171. DOI: 10.1016/j.is.2016.12.005

  8. Bhadoria R.S., Chaudhari N.S. et al. Analyzing the role of interfaces in enterprise service bus: A middleware epitome for service-oriented systems, Computer Standards & Interfaces, 2018, vol. 55, pp. 146-155. DOI: 10.1016/j.csi.2017.08.001

  9. Ming-zhe YU. Design on enterprise service bus message conversion protocol based on XSLT, The Journal of China Universities of Posts and Telecommunications, 2013, vol. 20, Supplement 1, pp. 50-54. DOI: 10.1016/S1005-8885(13)60249-6

  10. Schel D., Henkel C., Stock D., Meyer O., Rauhöft G. et al. Manufacturing Service Bus: An Implementation, Procedia CIRP, 2018, vol. 67, pp. 179-184. DOI: 10.1016/j.procir.2017.12.196

  11. Sudakov V., Nesterov V., Kurennykh A. Integration of decision support systems “Kosmos” and WS-DSS with computer models, 2017 Tenth International Conference Management of Large-Scale System Development (MLSD), 2017, pp. 1-4. DOI: 10.1109/MLSD.2017.8109690

  12. Popov E.P., Vereikin A.A, Nasonov F.A. Trudy MAI, 2021, no. 120. URL: http://trudymai.ru/eng/published.php?ID=161429. DOI: 10.34759/trd-2021-120-15

  13. Shkinderov M.S., Mubarakov R.R. Trudy MAI, 2021, no. 120. URL: http://trudymai.ru/eng/published.php?ID=161426. DOI: 10.34759/trd-2021-120-12

  14. Sudakov V.A. Aerospace MAI Journal, 2010, vol. 17, no. 1, pp. 149-153.

  15. Zaitsev A.A., Kurennykh A.E., Sudakov V.A., Romanov O.T. Nauchno-tekhnicheskii vestnik informatsionnykh tekhnologii, mekhaniki i optiki, 2019, vol. 19, no. 2, pp. 292–298. DOI: 10.17586/2226-1494-2019-19-2-292-298

  16. Batkovskiy A., Kurennykh A., Semenova E., Sudakov V., Fomina A., Balashov V. Sustainable project management for multi-agent development of enterprise information systems, Entrepreneurship and Sustainability, 2019, vol. 7, pp. 278-290. DOI: 10.9770/jesi.2019.7.1(21)

  17. Saaty T., Pierfrancesco P. Rethinking Design and Urban Planning for the Cities of the Future, Buildings, 2017, vol. 7, no. 76, pp. 1-22. DOI: 10.3390/buildings7030076

  18. Saaty T.L, Rokou E. How to prioritize inventions, World Patent Information, 2017, vol. 48, pp. 78-95. DOI: 10.1016/j.wpi.2017.02.003

  19. Kakiashvili T., Koczkodaj W.W., Magnot J.-P. Approximate reasoning by pairwise comparisons: Topodynamics of metastable brains by Arturo Tozzi et al. Physics of Life Reviews, 2017, vol. 21, pp. 37-39. DOI: 10.1016/j.plrev.2017.04.001

  20. Saaty T. Neurons the decision makers. The firing function of a single neuron. Part I, Neural Networks, 2017, vol. 86, pp. 102-114. DOI: 10.1016/j.neunet.2016.04.003

  21. Saaty T. The firings of many neurons and their density; the neural network its connections and field of firings. Part 2, Neural Networks, 2017, vol. 86, pp. 115-122. DOI: 10.1016/j.neunet.2016.04.004

  22. Runarsson T., Yao X. Stochastic ranking for constrained evolutionary optimization, Evolutionary Computation, IEEE Transactions, 2000, no. 4, pp. 284-294. DOI: 10.1109/4235.873238

  23. Nelder J.A., Mead R. A Simplex Method for Function Minimization, The Computer Journal, 1965, vol. 7, pp. 308-313. DOI: 10.1093/comjnl/7.4.308

  24. Runarsson T., Yao X. Search Biases in Constrained Evolutionary Optimization, IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2005, no. 35 (2), pp. 233-243. DOI: 10.1109/TSMCC.2004.841906

  25. Richard A. Estombelo Montesco, Marcosiris A. O. Pessoa, Mauricio Blos. Scheduling heuristic resourced-based on task time windows for APS (Advanced planning and scheduling) Systems, 15th IFAC/IEEE/IFIP/IFORS Symposium on Information Control Problems in Manufacturing, 2015, vol. 48, issue 3, pp. 2273-2280. DOI: 10.1016/j.ifacol.2015.06.426


Download

mai.ru — informational site MAI

Copyright © 2000-2024 by MAI

 Вход