Development of the rough Defining Interrelationships Between Ranked criteria II method and its application in the MCDM model
DOI:
https://doi.org/10.31181/jdaic10009102024tKeywords:
Rough DIBR II, Rough ARAS, MCDM, HRMAbstract
Although numerous methods of multi-criteria decision-making have been developed so far, there are still those that have not been improved by different theories that deal well with uncertainties and inaccuracies that are a normal occurrence in everyday life. The goal of this research is the development of the Rough Defining Interrelationships Between Ranked Criteria II (Rough DIBR II) method and the presentation of its application in the multi-criteria decision-making (MCDM) problem, specifically in human resource management (HRM). The paper presents the subject-developed method and its practical application in the MCDM model with the Rough Additive Ratio Assessment (Rough ARAS) method. In order to check the consistency and validity of the proposed methodology, a sensitivity analysis and a comparative analysis were performed. The conclusions of this research indicate a stable and valid proposed methodology, as well as the possible application of the Rough DIBR II method for defining weighting coefficients of criteria in real-life decision-making problems.
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Abdillah, A. I. J., Rimbawa, H. D., & Asnar, Y. (2023). Evaluation of the determination of it infrastructure personnel in the AL navy using a combination of AHP and VIKOR methods. Antivirus: Jurnal Ilmiah Teknik Informatika, 17(1), 144-154.
Akmaludin, A., Sihombing, E. G., Dewi, L. S., & Arisawati, E. (2023). Comparison of Selection for Employee Position Recommended MCDM-AHP, SMART and MAUT Method. Sinkron: jurnal dan penelitian teknik informatika, 7(2), 603-616.
Arsić, S. N., Pamučar, D., Suknovic, M., & Janošević, M. (2019). Menu evaluation based on rough MAIRCA and BW methods. Serbian journal of management, 14(1), 27-48.
Badi, I., & Abdulshahed, A. (2021). Sustainability performance measurement for Libyan Iron and Steel Company using Rough AHP. Journal of Decision Analytics and Intelligent Computing, 1(1), 22-34.
Biswas, A., Gazi, K. H., Bhaduri, P., & Mondal., S. P. (2025a). Site Selection for Girls Hostel in a University Campus by MCDM based Strategy. Spectrum of Decision Making and Applications, 2(1), 68-93.
Biswas, A., Gazi, K. H., Sankar, P. M., & Ghosh, A. (2025b). A Decision-Making Framework for Sustainable Highway Restaurant Site Selection: AHP-TOPSIS Approach based on the Fuzzy Numbers. Spectrum of Operational Research, 2(1), 1-26.
Biswas, S., Pamucar, D., Dawn, S., & Simic, V. (2024). Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS) Method for Comparing Firm Performance in Energy Sector. Decision Making Advances, 2(1), 1-21.
Bouraima, M. B., Jovčić, S., Dobrodolac, M., Pamucar, D., Badi , I., & Maraka, N. D. (2024). Sustainable Healthcare System Devolution Strategy Selection Using the AROMAN MCDM Approach. Spectrum of Decision Making and Applications, 1(1), 46-63.
Božanić, D., & Pamucar, D. (2023). Overview of the Method Defining Interrelationships Between Ranked Criteria II and Its Application in Multi-criteria Decision-Making. In: Chatterjee, P., Pamucar, D., Yazdani, M., & Panchal, D. (eds) Computational Intelligence for Engineering and Management Applications. Lecture Notes in Electrical Engineering, vol 984. Singapore: Springer.
Božanić, D., Epler, I., Puška, A., Biswas, S., Marinković, D. & Koprivica, S. (2024). Application of the DIBR II – Rough MABAC decision-making model for ranking methods and techniques of Lean organization systems management in the process of technical maintenance. Facta Universitatis. Series: Mechanical Engineering, 22(1), 101-123.
Costa, I. P. D. A., Terra, A. V., Moreira, M. Â. L., Pereira, M. T., Fávero, L. P. L., Santos, M. D., & Gomes, C. F. S. (2022). A systematic approach to the management of military human resources through the ELECTRE-MOr multicriteria method. Algorithms, 15(11), 422.
Fanaei, S., Zareiyan, A., Shahraki, S., & Mirzaei, A. (2023). Determining the key performance indicators of human resource management of military hospital managers; a TOPSIS study. BMC Primary Care, 24(1), 47.
Kannan, J., Jayakumar, V., & Pethaperumal, M. (2024). Advanced Fuzzy-Based Decision-Making: The Linear Diophantine Fuzzy CODAS Method for Logistic Specialist Selection. Spectrum of Operational Research, 2(1), 41-60.
Kara, K., Yalçın, G. C., Simic, V., Yıldırım, A. T., Pamucar, D., & Siarry, P. (2024). A spherical fuzzy-based DIBR II-AROMAN model for sustainability performance benchmarking of wind energy power plants. Expert systems with applications, 253, 124300.
Madić, M., Petrović, G., Petković, D., & Janković, P. (2024). Traditional and Integrated MCDM Approaches for Assessment and Ranking of Laser Cutting Conditions. Spectrum of Mechanical Engineering and Operational Research, 1(1), 250-257.
Nedeljković, M., Puška, A., Jeločnik, M., Božanić, D., Subić, J., Štilić, A., & Maksimović, A. (2023). Enhancing Fruit Orchard Establishment: A Multicriteria Approach for Plum Variety Selection. Yugoslav Journal Of Operations Research, 34(2), 355-380.
Pamučar, D., Stević, Ž., & Zavadskas, E. K. (2018). Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages. Applied soft computing, 67, 141-163.
Pawlak, Z. (1982). Rough sets. International Journal of Information and Computer Sciences, 11(5), 341-356.
Pawlak, Z. (2005). Rough sets. International Journal of Computer & Information Sciences, 11, 341-356.
Puška, A., Štilić, A., Božanić, D., Đurić, A., & Marinkovic, D. (2023). Selection of EVs as Tourist and Logistic Means of Transportation in Bosnia and Herzegovina’s Nature Protected Areas Using Z‐Number and Rough Set Modeling. Discrete Dynamics in Nature and Society, 2023(1), 5977551.
Qi, J., Hu, J., & Peng, Y. (2021). Modified rough VIKOR based design concept evaluation method compatible with objective design and subjective preference factors. Applied Soft Computing, 107, 107414.
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.
Regaieg Cherif, M., & Moalla Frikha, H. (2021). An extension of the CODAS method based on interval rough numbers for multi-criteria group decision making. Multiple Criteria Decision Making, 16, 23-43.
Rosiana, E., Garside, A. K., & Amallynda, I. (2021). Integration of Rough SWARA and COPRAS in the Performance Evaluation of Third-Party Logistics Providers. Jurnal Teknik Industri, 22(1), 31-42.
Roy, J., Adhikary, K., Kar, S., & Pamucar, D. (2018). A rough strength relational DEMATEL model for analysing the key success factors of hospital service quality. Decision Making: Applications in Management and Engineering, 1(1), 121-142.
Sahoo, S. K., Choudhury, B. B., & Dhal, P. R. (2024). A Bibliometric Analysis of Material Selection Using MCDM Methods: Trends and Insights. Spectrum of Mechanical Engineering and Operational Research, 1(1), 189-205.
Song, W., Ming, X., Wu, Z., & Zhu, B. (2014). A rough TOPSIS approach for failure mode and effects analysis in uncertain environments. Quality and Reliability Engineering International, 30(4), 473-486.
Stević, Ž., Novarlić, B., & Kchaou, M. (2023). Assessment of human resources performance in urban maintenance tasks: An MCDM approach. Journal of Organizations, Technology and Entrepreneurship, 2(1), 39-51.
Stević, Ž., Pamučar, D., Kazimieras Zavadskas, E., Ćirović, G., & Prentkovskis, O. (2017). The selection of wagons for the internal transport of a logistics company: A novel approach based on rough BWM and rough SAW methods. Symmetry, 9(11), 264.
Stojić, G., Stević, Ž., Antuchevičienė, J., Pamučar, D., & Vasiljević, M. (2018). A Novel Rough WASPAS Approach for Supplier Selection in a Company Manufacturing PVC Carpentry Products. Information, 9(5), 121.
Tešić, D., & Marinković, D. (2023). Application of fermatean fuzzy weight operators and MCDM model DIBR-DIBR II-NWBM-BM for efficiency-based selection of a complex combat system. Journal of Decision Analytics and Intelligent Computing, 3(1), 243-256.
Tešić, D., Božanić, D., & Khalilzadeh, M. (2024a). Enhancing Multi-Criteria Decision-Making with Fuzzy Logic: An Advanced Defining Interrelationships Between Ranked II Method Incorporating Triangular Fuzzy Numbers. Journal of intelligent management decision, 3(1), 56-67.
Tešić, D., Božanić, D., & Khalilzadeh, M. (2024b). Enhancement of the Defining Interrelationships Between Ranked Criteria II method using interval grey numbers for application in the grey-rough MCDM Model. International Journal of Knowledge and Innovation Studies, 2(2), 81-91.
Tešić, D., Božanić, D., Radovanović, M., & Petrovski, A. (2023a). Optimising Assault Boat Selection for Military Operations: An Application of the DIBR II-BM-CoCoSo MCDM Model. Journal of Intelligent Management Decision, 2(4), 160-171.
Tešić,D., Božanić, D., & Milić, A. (2023b). A Multi-Criteria Decision-Making Model for Pontoon Bridge Selection: An Application of the DIBR II-NWBM-FF MAIRCA Approach. Journal of Engineering Management and Systems Engineering, 2(4), 212-223.
Tešić, D., Delibašić, B., Božanić, D., Lojić, R., Pamučar, D., & Balassa, B. E. (2023c). Application of the FUCOM-Fuzzy MAIRCA Model in Human Resource Management. Acta Polytechnica Hungarica, 20(3), 231-249.
Tešić, D., Radovanović, M., Božanić, D., Pamucar, D., Milić, A., & Puška, A. (2022). Modification of the DIBR and MABAC methods by applying rough numbers and its application in making decisions. Information, 13(8), 353.
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.
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.
Vojinović, N., Sremac, S., & Zlatanović, D. (2021). A Novel Integrated Fuzzy‐Rough MCDM Model for Evaluation of Companies for Transport of Dangerous Goods. Complexity, 2021(1), 5141611.
Wang, X. T., & Xiong, W. (2010). Rough AHP approach for determining the importance ratings of customer requirements in QFD. Computer Integrated Manufacturing System, 16(04), 763-771.
Yenilmezel, S., & Ertuğrul, İ. (2024). Human Resources Management Application Selection with fuzzy MAIRCA Method Based on fuzzy PIPRECIA. Pamukkale Üniversitesi İşletme Araştırmaları Dergisi, 11(1), 67-81.
Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Technological and economic development of economy, 16(2), 159-172.
Zhu, G.-N., Hu, J., Qi, J., Gu, C.-C., & Peng, Y.-H. (2015). An integrated AHP and VIKOR for design concept evaluation based on rough number. Advanced Engineering Informatics, 29(3), 408-418.