FACTOR ANALYSIS METHOD APPLICATION FOR CONSTRUCTING OBJECTIVE FUNCTIONS OF OPTIMIZATION IN MULTIMODAL TRANSPORT PROBLEMS
Serhii Zabolotnii
zabolotniua@gmail.comCherkasy 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 transportationReferences
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
Authors
Serhii Zabolotniizabolotniua@gmail.com
Cherkasy State Business-College Ukraine
http://orcid.org/0000-0003-0242-2234
Authors
Artem HoncharovCherkasy State Technological University Ukraine
https://orcid.org/0000-0003-4043-5300
Authors
Sergii MogileiRauf Ablyazov East European University Ukraine
http://orcid.org/0000-0002-9296-6827
Statistics
Abstract views: 269PDF downloads: 163
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Most read articles by the same author(s)
- Serhii Zabolotnii, Sergii Mogilei, APPLICATION OF THE MATRIX FACTOR ANALYSIS METHOD FOR DETERMINING PARAMETERS OF THE OBJECTIVE FUNCTION FOR TRANSPORT RISK MINIMIZATION , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 11 No. 1 (2021)
- Serhii Zabolotnii, Sergii Mogilei, MODIFICATIONS OF EVANS PRICE EQUILIBRIUM MODEL , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 13 No. 1 (2023)