FACTOR ANALYSIS METHOD APPLICATION FOR CONSTRUCTING OBJECTIVE FUNCTIONS OF OPTIMIZATION IN MULTIMODAL TRANSPORT PROBLEMS

Serhii Zabolotnii

zabolotniua@gmail.com
Cherkasy State Business-College (Ukraine)
http://orcid.org/0000-0003-0242-2234

Artem Honcharov


Cherkasy State Technological University (Ukraine)
https://orcid.org/0000-0003-4043-5300

Sergii Mogilei


Rauf Ablyazov East European University (Ukraine)
http://orcid.org/0000-0002-9296-6827

Abstract

The paper regards a specific class of optimization criteria that possess features of probability. Therefore, constructing objective function of optimization problem, the importance is attached to probability indices that show the probability of some criterial event or events to occur. Factor analysis has been taken for the main method of constructing objective function. Algorithm for constructing objective function of optimization is done for criterion of minimization risk level in multimodal transportations that demanded demonstration data. The application of factor analysis in classical problem solution was shown to give the problem a more distinct analytical interpretation in solving it.


Keywords:

factor analysis, risk function, optimization criterion, multimodal transportation

Ayed H., Galvez-Fernandez C., Habbas Z., Khadraoui D.: Solving time-dependent multimodal transport problems using a transfer graph model. Computers and Industrial Engineering 61, 2011, 391–401 [http://doi.org/10.1016/j.cie.2010.05.018].
DOI: https://doi.org/10.1016/j.cie.2010.05.018   Google Scholar

Ayed H., Habbas Z., Khadraoui D., Galvez-Fernandez C.: A parallel algorithm for solving time dependent multimodal transport problem. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2011, 722–727 [http://doi.org/10.1109/ITSC.2011.6082973].
DOI: https://doi.org/10.1109/ITSC.2011.6082973   Google Scholar

Boyd K. C.: Factor analysis. The Routledge Handbook of Research Methods in the Study of Religion 2013, 204–216 [http://doi.org/10.4324/9780203154281-22].
  Google Scholar

Chandrakantha L.: Using excel solver in optimization problems. John Jay College of Criminal Justice of CUNY, 2014, 42–49.
  Google Scholar

Elias D., Nadler B., Nadler F., Hauger G.: OPTIHUBS – Multimodal Hub Process Optimization by Means of Micro Simulation. Transportation Research Procedia 14, 2016, 457–466 [http://doi.org/10.1016/j.trpro.2016.05.098].
DOI: https://doi.org/10.1016/j.trpro.2016.05.098   Google Scholar

Ezeokwelume O.: Solving linear programming problems and transportation problems using excel solver. International Journal of Scientific & Engineering Research 7(9), 2016, 134–142.
  Google Scholar

Flórez J. E., Torralba A., García J., Linares López C., García-Olaya A., Borrajo D.: TIMIPLAN: An Application to Solve Multimodal Transportation Problems. Scheduling and Planning Applications Workshop 2010.
  Google Scholar

García J., Florez J. E., Torralba A., Borrajo D., López C. L., García-Olaya A., Sáenz J.: Combining linear programming and automated planning to solve intermodal transportation problems. European Journal of Operational Research 227, 2013, 216–226.
DOI: https://doi.org/10.1016/j.ejor.2012.12.018   Google Scholar

Honcharov A., Mogilei S.: Solving multimodal transportation problems by different program means. Bulletin of Cherkasy State Technological University 3, 2020, 67–74.
  Google Scholar

Jennrich R. I., Bentler P. M.: Exploratory Bi-Factor Analysis. Psychometrika 76(4), 2011, 537–549 [http://doi.org/10.1007/s11336-011-9218-4].
DOI: https://doi.org/10.1007/s11336-011-9218-4   Google Scholar

Journal I., Factor I.: Computational and Mathematical Methods in Medicine. Bio Med Research International 1, 2015, 2–4.
DOI: https://doi.org/10.1155/2015/685036   Google Scholar

Klami A., Virtanen S., Leppaaho E., Kaski S.: Group Factor Analysis. IEEE Transactions on Neural Networks and Learning Systems 26(9), 2015, 2136–2147 [http://doi.org/10.1109/TNNLS.2014.2376974].
DOI: https://doi.org/10.1109/TNNLS.2014.2376974   Google Scholar

Lin C. C., Lin S. W.: Two-stage approach to the intermodal terminal location problem. Computers and Operations Research 67, 2016, 113–119 [http://doi.org/10.1016/j.cor.2015.09.009].
DOI: https://doi.org/10.1016/j.cor.2015.09.009   Google Scholar

Ovcharuk V., Vovkodav N., Kryvets T., Ovcharuk I.: Linear programming in Mathcad on the example of solving the transportation problem. Scientific Works of NUFT 21(4), 2015, 110–117.
  Google Scholar

Sengamalaselvi J.: Solving transportation problem by using Matlab. International Journal of Engineering Sciences & Research Technology 6(1), 2017, 374–381 [http://doi.org/10.5281/zenodo.259588].
  Google Scholar

Slavova-Nocheva M.: Competitiveness of the transport market in Bulgaria. Economic Studies 21(3), 2012, 15–24.
  Google Scholar

Vats B., Kumar Singh A.: Solving transportation problem using excel solver for an optimal solution. MIT International Journal of Mechanical Engineering 6(1), 2016, 18–20.
  Google Scholar

Verga J., Silva R. C., Yamakami A.: Multimodal transport network problem: Classical and innovative approaches. Studies in Fuzziness and Soft Computing, Springer Verlag 358, 2018, 299–332 [http://doi.org/doi:10.1007/978-3-319-62359-7_14].
DOI: https://doi.org/10.1007/978-3-319-62359-7_14   Google Scholar

Virtanen S., Klami A., Khan S.A., Kaski S.: Bayesian group factor analysis. The Journal of Machine Learning Research 22, 2012, 1269–1277.
  Google Scholar

Zabolotnii S., Mogilei S., The methods for determining the parameters of the objective function of multimodal transportation risk. Proceedings of V International Scientific-Practical Conference “ITEST-2020”, 2020, 114–115.
  Google Scholar

Zabolotnii S., Mogilei S.: Optimization of the method of constructing reference plans of multimodal transport problem. Technological audit and production reserves 2(45), 2019, 15–20 [http://doi.org/10.15587/2312-8372.2019.154561].
DOI: https://doi.org/10.15587/2312-8372.2019.154561   Google Scholar

Zelenika R., Sever D., Zebec S., Pirš B.: Logistic operator: Fundamental factor in rational production of services in multimodal transport. Promet - Traffic&Transportation 17(1), 2005, 43–53.
  Google Scholar

Zhao S., Gao C., Mukherjee S., Engelhardt B. E.: Bayesian group factor analysis with structured sparsity. Journal of Machine Learning Research 17, 2016, 1–47.
  Google Scholar

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Published
2021-12-20

Cited by

Zabolotnii, S., Honcharov, A., & Mogilei, S. (2021). FACTOR ANALYSIS METHOD APPLICATION FOR CONSTRUCTING OBJECTIVE FUNCTIONS OF OPTIMIZATION IN MULTIMODAL TRANSPORT PROBLEMS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 11(4), 28–31. https://doi.org/10.35784/iapgos.2788

Authors

Serhii Zabolotnii 
zabolotniua@gmail.com
Cherkasy State Business-College Ukraine
http://orcid.org/0000-0003-0242-2234

Authors

Artem Honcharov 

Cherkasy State Technological University Ukraine
https://orcid.org/0000-0003-4043-5300

Authors

Sergii Mogilei 

Rauf Ablyazov East European University Ukraine
http://orcid.org/0000-0002-9296-6827

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