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Article

SELECTION OF MOBILE NETWORK OPERATOR USING THE CRITIC-ARAS METHOD

DOI: 10.7708/ijtte.2021.11(1).02


11 / 1 / 17 - 29 Pages

Author(s)

Sara Bošković - University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11000 Belgrade, Serbia -

Vesna Radonjić-Djogatović - University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11000 Belgrade, Serbia -

Predrag Ralević - Technical College of Applied Sciences, Uroševac (Leposavić), 24. Novembar bb, 38218 Leposavić, Serbia -

Momčilo Dobrodolac - University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11000 Belgrade, Serbia -

Stefan Jovčić - University of Pardubice, The Faculty of Transport Engineering, Studentská 95, 532 10 Pardubice, Czech Republic -


Abstract

Mobile Network Operators play an important role in the communication industry since there are several billions of customers using their services in the world. This paper solves a problem of Mobile Network Operator selection by a user. Therefore, a methodology proposed in this paper is of particular interest from the user’s point of view. It can be applied as a decision-making tool that would support a customer in the process of evaluating and choosing a Mobile Network Operators. To rank the available Mobile Network Operators, the authors of this paper consider several criteria identified by the experts. The CRITIC (CRiteria Importance through Inter-criteria Correlation method) finds the importance of the criteria (weights), while the ARAS (Additive Ratio Assessment) method is used for ranking the alternatives and making a decision about the best possible alternative.


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References:

Baležentis, A.; Baležentis, T.; Misiūnas, A. 2012. Integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM Methods, Technological and Economic Development of Economy 18(1): 34–53. https://doi.org/10.3846/20294913.2012.656151.

 

Diakoulaki, D.; Mavrotas, G.; Papayannakis, L. 1995. Determining objective weights in multiple criteria problems: the critic method, Computers & Operations Research 22(7): 763–770. https://doi.org/10.1016/0305-0548(94)00059-h.

 

Dobrodolac, M.; Ralević, P.; Švadlenka, L.; Radojičić, V. 2016. Impact of a new concept of universal service obligations on revenue increase in the Post of Serbia, Promet - Traffic and Transportation 28(3): 235-244. http://dx.doi.org/10.7307/ptt.v28i3.1835.

 

Dobrodolac, M.; Marković, D.; Čubranić-Dobrodolac, M.; Denda, N. 2014. Using work stress measurement to develop and implement a TQM programme: a case of counter clerks in Serbian Post, Total Quality Management and Business Excellence 25(11-12): 1262-1279. https://doi.org/10.1080/14783363.2012.704280.

 

Dobrodolac, M.; Švadlenka, L.; Čubranić-Dobrodolac, M.; Čičević, S.; Stanivuković, B. 2018. A model for the comparison of business units, Personnel Review 47(1): 150-165. https://doi.org/10.1108/PR-02-2016-0022.

 

Fu, Y.-K. 2019. An integrated approach to catering supplier selection using AHP-ARAS-MCGP methodology, Journal of Air Transport Management 75: 164–169. https://doi.org/10.1016/j.jairtraman.2019.01.011.

 

Ghenai, C.; Albawab, M.; Bettayeb, M. 2020. Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method, Renewable Energy 146: 580–597. https://doi.org/10.1016/j.renene.2019.06.157.

 

Ghorabaee, K. M.; Amiri, M.; Zavadskas, E. K.; Antuchevičienė, J. 2017. Assessment of third-party logistics providers using a CRITIC–WASPAS approach with interval type-2 fuzzy sets, Transport 32(1): 66-78. https://doi.org/10.3846/16484142.2017.1282381.

 

Hassan, N.; Ahmad, N.; Malissa-Aminuddin, W. 2013. Selection of Mobile Network Operator Using Analytic Hierarchy Process (AHP), Advances in Natural and Applied Sciences 7(1): 1-5.

 

Jovčić, S.; Průša, P.; Dobrodolac, M.; Švadlenka, L. 2019. A Proposal for a Decision-Making Tool in Third-Party Logistics (3PL) Provider Selection Based on Multi-Criteria Analysis and the Fuzzy Approach, Sustainability 11(15): 4236. https://doi.org/10.3390/su11154236.

 

Jovčić, S.; Simić, V.; Průša, P.; Dobrodolac, M. 2020. Picture Fuzzy ARAS Method for Freight Distribution Concept Selection, Symmetry 12(7): 1062. https://doi.org/10.3390/sym12071062.

 

Keršulienė, V.; Turskis, Z. 2014. A hybrid linguistic fuzzy multiple criteria group selection of a chief accounting officer, Journal of Business Economics and Management 15(2): 232–252. https://doi.org/10.3846/16111699.2014.903201.

 

Kutut, V.; Zavadskas, E.K.; Lazauskas, M. 2014. Assessment of priority alternatives for preservation of historic buildings using model based on ARAS and AHP methods, Archives of Civil and Mechanical Engineering 14(2): 287–294. https://doi.org/10.1016/j.acme.2013.10.007.

 

Pehlivan, N.Y.; Gürsoy, Z. 2019. Determination of individuals’ life satisfaction levels living in Turkey by FMCDM methods, Kybernetes 48(8): 1871–1893. https://doi.org/10.1108/K-04-2018-0184.

 

Radović, D.; Stević, Ž.; Pamučar, D.; Zavadskas, E.K.; Badi, I.; Antuchevičiene, J.; Turskis, Z. 2018. Measuring performance in transportation companies in developing countries: A novel rough ARAS Model, Symmetry 10(10): 434. https://doi.org/10.3390/sym10100434.

 

Ralević, P.; Dobrodolac, M.; Marković, D.; Mladenović, S. 2015. The Measurement of Public Postal Operators’ Profit Efficiency by Using Data Envelopment Analysis (DEA): a Case Study of the European Union Member States and Serbia, Engineering Economics 26(2): 159-168. http://dx.doi.org/10.5755/j01.ee.26.2.3360.

 

Ralević, P.; Dobrodolac, M.; Švadlenka, L.; Šarac, D.; Đurić, D. 2020. Efficiency and productivity analysis of universal service obligation: a case of 29 designated operators in the European countries, Technological and Economic Development of Economy 26(4): 785-807. https://doi.org/10.3846/tede.2020.12062.

 

Tupenaite, L.; Zavadskas, E.K.; Kaklauskas, A.; Turskis, Z.; Seniut, M. 2010. Multiple criteria assessment of alternatives for built and human environment renovation, Journal of Civil Engineering and Management 16(2): 257–266. https://doi.org/10.3846/jcem.2010.30.

 

Turskis, Z.; Zavadskas, E.K. 2010a. A new fuzzy additive ratio assessment method (ARAS-F). Case study: The analysis of fuzzy multiple criteria in order to select the logistic centers location, Transport 25(4): 423–432. https://doi.org/10.3846/transport.2010.52.

 

Turskis, Z.; Zavadskas, E.K. 2010b. A novel method for multiple criteria analysis: Grey Additive Ratio Assessment (ARAS-G) method, Informatica 21(4): 597–610. http://dspace.vgtu.lt/handle/1/2036.

 

Turskis, Z.; Zavadskas, E.K.; Kutut, V. 2013. A model based on ARAS-G and AHP methods for multiple criteria prioritizing of heritage value, International Journal of Information Technology & Decision Making 12(1): 45–73. https://doi.org/10.1142/S021962201350003X.

 

Vijay, P.; Krishnaveni, V. 2016. Customer preferences towards the mobile network service provider- a study with the special reference to Coimbatore city, International Journal of Management Research & Review 6(10): 1386-1392.

 

Zamani, M.; Rabbani, A.; Yazdani-Chamzini, A.; Turskis, Z. 2014. An integrated model for extending brand based on fuzzy ARAS and ANP methods, Journal of Business Economics and Management 15(3): 403–423. https://doi.org/10.3846/16111699.2014.923929.

 

Zavadskas, E.K.; Turskis, Z. 2010. A new additive ratio assessment (ARAS) method in multi-criteria decision-making, Technological and Economic Development of Economy 16(2): 159–172. https://doi.org/10.3846/tede.2010.10.


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