Performance Measurement of Financial Officer Recruitment of a Company Using PIVN-AHP & PIVN-TOPSIS
Volume 36, Issue 4 (2025), pp. 797–831
Pub. online: 11 November 2025
Type: Research Article
Open Access
Open Access
Received
1 March 2025
1 March 2025
Accepted
1 October 2025
1 October 2025
Published
11 November 2025
11 November 2025
Abstract
When it comes to building and sustaining a company’s financial base, financial officers (FOs) are indispensable. Consequently, hiring FOs should be fair and efficient to guarantee continuous economic growth. Evaluating their performance is crucial. The main objective of this research is to find the best financial officer. The research developed an innovative method based on the parametric representation of interval numbers to handle the uncertainty in real-life multi-criteria decision-making (MCDM) scenarios. This research considers all the essential characteristics of an FO to find the best candidate. We provide a new approach to determining the weight of each criterion and sub-criterion, the Parametric Interval Number-Analytic Hierarchy Process (PIVN-AHP). The next step in finding the best FO is to use a hybrid algorithm called PIVN-TOPSIS, which stands for Parametric Interval Number-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Several MCDM approaches, such as Simple Additive Weighting (SAW), Weighted Aggregated Sum Product Assessment (WASPAS), and the Weighted Sum Model (WSM), were used in a comparative study to confirm the ranks. We could also conduct a sensitivity study by shifting the weight of specific criteria. An FO’s evaluation focuses on key criteria and sub-factors, with PIVN-AHP used to calculate weights. “Accounts Knowledge” (C5) is the most significant criterion, while “Growth of Customer” (CW31) holds the highest sub-criterion weight.
References
Alaa, M., Albakri, I.S.M.A., Singh, C.K.S., Hammed, H., Zaidan, A.A., Zaidan, B.B., Albahri, O.S., Alsalem, M.A., Salih, M.M., Almahdi, E.M., Baqer, M.J., Jalood, N.S., Nidhal, S., Shareef, A.H., Jasim, A.N. (2019). Assessment and ranking framework for the English skills of pre-service teachers based on fuzzy Delphi and TOPSIS methods. IEEE Access, 7, 126201–126223. https://doi.org/10.1109/ACCESS.2019.2936898.
Alrashedi, A.K. (2024). Optimising human resources management process on organizational success: integrating Markov chain and fuzzy TOPSIS. Journal of Human Resource and Sustainability Studies, 12(3), 669–685. https://doi.org/10.4236/jhrss.2024.123035.
Atanassov, K.T., Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems, 31(3), 343–349. https://doi.org/10.1016/0165-0114(89)90205-4.
Atanassov, K.T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3.
Atanassov, K.T. (1989). More on intuitionistic fuzzy sets. Fuzzy Sets and Systems, 33(1), 37–45. https://doi.org/10.1016/0165-0114(89)90215-7.
Baas, S.M., Kwakernaak, H. (1977). Rating and ranking of multi-aspect alternatives using fuzzy sets. Automatica, 13(1), 47–58. https://doi.org/10.1016/0005-1098(77)90008-5.
Bellman, R.E., Zadeh, L.A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), B141–B164. https://doi.org/10.1287/mnsc.17.4.B141.
Boral, S., Howard, I., Chaturvedi, S.K., McKee, K., Naikan, V.N.A. (2020). An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA. Engineering Failure Analysis, 108, 104195. https://doi.org/10.1016/j.engfailanal.2019.104195.
Brauers, W.K.M., Zavadskas, E.K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5–24. https://doi.org/10.3846/tede.2010.01.
Chen, C.T., Hung, W.Z., Lin, K.H., Cheng, H.L. (2009). An evaluation model of service quality by applying linguistic TOPSIS method. In: 2009 IEEE/INFORMS International Conference on Service Operations, Logistics and Informatics. IEEE, pp. 335–340. https://doi.org/10.1109/SOLI.2009.5203955.
Chen, J.F., Hsieh, H.N., Do, Q.H. (2015). Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach. Applied Soft Computing, 28, 100–108. https://doi.org/10.1016/j.asoc.2014.11.050.
Chen, S.J., Hwang, C.L. (1992). In: Fuzzy Multiple Attribute Decision-Making: Methods and Applications, Lecture Notes in Economics and Mathematical Systems, Vol. 375. Springer Berlin Heidelberg, Berlin, Heidelberg. 978-3-540-54998-7. https://doi.org/10.1007/978-3-642-46768-4
Chien, T.K. (2007). Using the learning satisfaction improving model to enhance the teaching quality. Quality Assurance in Education, 15(2), 192–214. https://doi.org/10.1108/09684880710748947.
Daniawan, B. (2018). Evaluation of lecturer teaching performance using AHP and SAW methods. bit-Tech, 1(2), 30–39. https://doi.org/10.32877/bt.v1i2.41.
Demirel, Z., Çubukçu, C. (2021). Measurement of employees on human resources with fuzzy logic. EMAJ: Emerging Markets Journal, 11(2), 1–7. https://doi.org/10.5195/emaj.2021.226.
Dhruva, S., Krishankumar, R., Pamucar, D., Zavadskas, E.K., Ravichandran, K.S. (2024a). Demystifying the stability and the performance aspects of CoCoSo ranking method under uncertain preferences. Informatica, 35(3), 509–528. https://doi.org/10.15388/24-INFOR565.
Dhruva, S., Krishankumar, R., Zavadskas, E.K., Ravichandran, K.S., Gandomi, A.H. (2024b). Selection of suitable cloud vendors for health centre: a personalized decision framework with Fermatean fuzzy set, LOPCOW, and CoCoSo. Informatica, 35(1), 65–98. https://doi.org/10.15388/23-INFOR537.
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.
Dong, W.M., Wong, F. (1987). Fuzzy weighted averages and implementation of the extension principle. Fuzzy Sets and Systems, 21(2), 183–199. https://doi.org/10.1016/0165-0114(87)90163-1.
Bana e Costa, C.A, Vansnick, J.C. (2008). A critical analysis of the eigenvalue method used to derive priorities in AHP. European Journal of Operational Research, 187(3), 1422–1428. https://doi.org/10.1016/j.ejor.2006.09.022.
Erdogan, S.A., Šaparauskas, J., Turskis, Z. (2019). A multi-criteria decision-making model to choose the best option for sustainable construction management. Sustainability, 11(8), 2239. https://doi.org/10.3390/su11082239.
Esangbedo, M.O., Bai, S., Mirjalili, S., Wang, Z. (2021). Evaluation of human resource information systems using grey ordinal pairwise comparison MCDM methods. Expert Systems with Applications, 182, 115151. https://doi.org/10.1016/j.eswa.2021.115151.
Fishburn, P.C. (1967). Additive utilities with incomplete product set: applications to priorities and assignments. Operations Research, 15(3), 537–542. https://doi.org/10.1287/opre.15.3.537.
Garg, H. (2016). A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems. Applied Soft Computing, 38, 988–999. https://doi.org/10.1016/j.asoc.2015.10.040.
Ghorabaee, M.K., Amiri, M., Zavadskas, E.K., Turskis, Z. (2017). Multi-criteria group decision-making using an extended EDAS method with interval type-2 fuzzy sets. Ekonomika a Management, 20(1), 48–68. https://doi.org/10.15240/tul/001/2017-1-004.
Ghorui, N., Ghosh, A., Mondal, S.P., Kumari, S., Jana, S., Das, A. (2021). Evaluation of performance for school teacher recruitment using MCDM techniques with interval data. Multicultural Education, 7(5), 380. https://doi.org/10.2478/eoik-2024-0007.
Gigović, L., Pamučar, D., Bajić, Z., Milićević, M. (2016). The combination of expert judgment and GIS-MAIRCA analysis for the selection of sites for ammunition depots. Sustainability, 8(4), 372. https://doi.org/10.3390/su8040372.
Harsanyi, J.C. (1955). Cardinal welfare, individualistic ethics, and interpersonal comparisons of utility. Journal of Political Economy, 63(4), 309–321. https://doi.org/10.1086/257678.
Hashemi, H., Mousavi, S.M., Zavadskas, E.K., Chalekaee, A., Turskis, Z. (2018). A new group decision model based on grey-intuitionistic fuzzy-ELECTRE and VIKOR for contractor assessment problem. Sustainability, 10(5), 1635. https://doi.org/10.3390/su10051635.
Hladík, M. (2012). Complexity of necessary efficiency in interval linear programming and multiobjective linear programming. Optimization Letters, 6, 893–899. https://doi.org/10.1007/s11590-011-0315-1.
Huang, X.Y., Feng, S.Q. (2015). Research on the teaching quality evaluation for the physical education in colleges based on the AHPTOPSIS. Chemical Engineering Transactions, 46, 487–492. https://doi.org/10.3303/CET1546082.
Hussain, S.A.I., Baruah, D., Dutta, B., Mandal, U.K., Mondal, S.P., Nath, T. (2019). Evaluating the impact of service quality on the dynamics of customer satisfaction in the telecommunication industry of Jorhat, Assam. Telecommunication Systems, 71(1), 31–53. https://doi.org/10.1007/s11235-018-0514-5.
Hussain, S.A.I., Mandal, U.K., Mondal, S.P. (2018). Decision maker priority index and degree of vagueness coupled decision making method: a synergistic approach. International Journal of Fuzzy Systems, 20(5), 1551–1566. https://doi.org/10.1007/s40815-017-0440-9.
Ishizaka, A., Lusti, M. (2006). How to derive priorities in AHP: a comparative study. Central European Journal of Operations Research, 14, 387–400. https://doi.org/10.1007/s10100-006-0012-9.
Kara, K., Edinsel, S., Yalçın, G.C. (2022). Human resources manager selection based on fuzzy and intuitionistic fuzzy numbers for logistics companies. Mersin Üniversitesi Denizcilik ve Lojistik Araştırmaları Dergisi, 4(2), 254–286. https://doi.org/10.54410/denlojad.1211835.
Karmaker, C., Saha, M. (2015). Teachers’ recruitment process via MCDM methods: a case study in Bangladesh. Management Science Letters, 5(8), 749–766. https://doi.org/10.5267/j.msl.2015.6.002.
Kaur, P. (2014). Selection of vendor based on intuitionistic fuzzy Analytical Hierarchy Process. Advances in Operations Research, 2014(1), 987690. https://doi.org/10.1155/2014/987690.
Kelemenis, A., Askounis, D. (2010). A new TOPSIS-based multi-criteria approach to personnel selection. Expert Systems with Applications, 37(7), 4999–5008. https://doi.org/10.1016/j.eswa.2009.12.013.
Keršulienė, V., Turskis, Z. (2011). Integrated fuzzy multiple criteria decision making model for architect selection. Technological and Economic Development of Economy, 17(4), 645–666. https://doi.org/10.3846/20294913.2011.635718.
Keršulienė, V., Turskis, Z. (2014a). An integrated multi-criteria group decision making process: selection of the chief accountant. Procedia-Social and Behavioral Sciences, 110, 897–904. https://doi.org/10.1016/j.sbspro.2013.12.935.
Keršulienė, V., Turskis, Z. (2014b). 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.
Keršulienė, V., Zavadskas, E.K., Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243–258. https://doi.org/10.3846/jbem.2010.12.
Kułakowski, K. (2015). Notes on order preservation and consistency in AHP. European Journal of Operational Research, 245(1), 333–337. https://doi.org/10.1016/j.ejor.2015.03.010.
Kumar, K., Garg, H. (2018). TOPSIS method based on the connection number of set pair analysis under interval-valued intuitionistic fuzzy set environment. Computational and Applied Mathematics, 37(2), 1319–1329. https://doi.org/10.1007/s40314-016-0402-0.
Liao, H., Xu, Z. (2014). Intuitionistic fuzzy hybrid weighted aggregation operators. International Journal of Intelligent Systems, 29(11), 971–993. https://doi.org/10.1002/int.21672.
Liao, N., Gao, H., Lin, R., Wei, G., Chen, X. (2023). An extended EDAS approach based on cumulative prospect theory for multiple attributes group decision making with probabilistic hesitant fuzzy information. Artificial Intelligence Review, 56(4), 2971–3003. https://doi.org/10.1007/s10462-022-10244-y.
Liou, T.S., Wang, M.J.J. (1992). Fuzzy weighted average: an improved algorithm. Fuzzy Sets and Systems, 49(3), 307–315. https://doi.org/10.1016/0165-0114(92)90282-9.
MacCrimmon, K.R. (1968). Decisionmaking Among Multiple-Attribute Alternatives: A Survey and Consolidated Approach (RM-4823-ARPA). RAND Corporation, Santa Monica, CA. https://doi.org/10.7249/RM4823.
Mazumdar, A., Datta, S., Mahapatra, S.S. (2010). Multicriteria decision-making models for the evaluation and appraisal of teachers’ performance. International Journal of Productivity and Quality Management, 6(2), 213–230. https://doi.org/10.1504/IJPQM.2010.034406.
Melón, M.G., Beltran, P.A., Cruz, M.C.G. (2008). An AHP-based evaluation procedure for innovative educational projects: a face-to-face vs. computer-mediated case study. Omega, 36(5), 754–765. https://doi.org/10.1016/j.omega.2006.01.005.
Michaelowa, K. (2007). The impact of primary and secondary education on higher education quality. Quality Assurance in Education, 15(2), 215–236. https://doi.org/10.1108/09684880710748956.
Mondal, K., Pramanik, S. (2014). Multi-criteria group decision making approach for teacher recruitment in higher education under simplified neutrosophic environment. Neutrosophic Sets and Systems, 6, 28–34. https://digitalrepository.unm.edu/nss_journal/vol6/iss1/6.
Nandi, S., Granata, G., Jana, S., Ghorui, N., Mondal, S.P., Bhaumik, M. (2023). Evaluation of the treatment options for COVID-19 patients using generalized hesitant fuzzy-multi criteria decision making techniques. Socio-Economic Planning Sciences, 88, 101614. https://doi.org/10.1016/j.seps.2023.101614.
Nirmala, G., Uthra, G. (2019). AHP based on triangular intuitionistic fuzzy number and its application to supplier selection problem. Materials Today: Proceedings, 16, 987–993. https://doi.org/10.1016/j.matpr.2019.05.186.
Nong, N.M.T., Ha, D.S. (2021). Application of MCDM methods to qualified personnel selection in distribution science: case of logistics companies. Journal of Distribution Science, 19(8), 25–35. https://doi.org/10.15722/jds.19.8.202108.25.
Ozgormus, E., Senocak, A.A., Goren, H.G. (2021). An integrated fuzzy QFD-MCDM framework for personnel selection problem. Scientia Iranica, 28(5), 2972–2986. https://doi.org/10.24200/sci.2019.52320.2657.
Pal, D., Mahapatra, G.S. (2017). Parametric functional representation of interval number with arithmetic operations. International Journal of Applied and Computational Mathematics, 3, 459–469. https://doi.org/10.1007/s40819-015-0113-z.
Pamučar, D., Stević, Ž., Sremac, S. (2018). A new model for determining weight coefficients of criteria in MCDM models: Full Consistency Method (FUCOM). Symmetry, 10(9), 393. https://doi.org/10.3390/sym10090393.
Peng, A., Wang, Z. (2011). GRA-based TOPSIS decision-making approach to supplier selection with interval number. In: 2011 Chinese Control and Decision Conference (CCDC). IEEE, pp. 1742–1747. https://doi.org/10.1109/CCDC.2011.5968478.
Popović, M. (2021). An MCDM approach for personnel selection using the CoCoSo method. Journal of Process Management and New Technologies, 9(3–4), 78–88. https://doi.org/10.5937/jpmnt9-34876.
Qi, Q.S. (2023). TOPSIS methods for probabilistic hesitant fuzzy MAGDM and application to performance evaluation of public charging service quality. Informatica, 34(2), 317–336. https://doi.org/10.15388/22-INFOR501.
Rajabpour, E., Fathi, M.R., Torabi, M. (2022). Analysis of factors affecting the implementation of green human resource management using a hybrid fuzzy AHP and type-2 fuzzy DEMATEL approach. Environmental Science and Pollution Research, 29(32), 48720–48735. https://doi.org/10.1007/s11356-022-19137-7.
Saaty, T.L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281. https://doi.org/10.1016/0022-2496(77)90033-5.
Saaty, T.L., Bennett, J.P. (1977). A theory of analytical hierarchies applied to political candidacy. Behavioral Science, 22(4), 237–245. https://doi.org/10.1002/bs.3830220402.
Sarkar, B. (2012). An inventory model with reliability in an imperfect production process. Applied Mathematics and Computation, 218(9), 4881–4891. https://doi.org/10.1016/j.amc.2011.10.053.
Sarkar, B., Sana, S.S., Chaudhuri, K. (2011). An imperfect production process for time varying demand with inflation and time value of money–an EMQ model. Expert Systems with Applications, 38(11), 13543–13548. https://doi.org/10.1016/j.eswa.2011.04.044.
Sarucan, A., Söğüt, A., Emin Baysal, M. (2022). A fuzzy multi-criteria decision making methodology for job evaluation. Fuzzy Logic and Modeling, 1(2), e120522204664. https://doi.org/10.2174/2666294901666220512124732.
Sen, B., Hussain, S.A.I., Mia, M., Mandal, U.K., Mondal, S.P. (2019a). Selection of an ideal MQL-assisted milling condition: an NSGA-II-coupled TOPSIS approach for improving machinability of Inconel 690. The International Journal of Advanced Manufacturing Technology, 103(5–8), 1811–1829. https://doi.org/10.1007/s00170-019-03620-6.
Sen, B., Mia, M., Mandal, U.K., Dutta, B., Mondal, S.P. (2019b). Multi-objective optimization for MQL-assisted end milling operation: an intelligent hybrid strategy combining GEP and NTOPSIS. Neural Computing and Applications, 31(12), 8693–8717. https://doi.org/10.1007/s00521-019-04450-z.
Taylan, O., Guloglu, B., Alkabaa, A.S., Sarp, S., Alidrisi, H.M., Milyani, A.H., Balubaid, M. (2024). AI based fuzzy MCDM models: comparison and evaluation of dissimilar outcomes, an application to enhance pilot recruitment process. Expert Systems, 41(9), e13590. https://doi.org/10.1111/exsy.13590.
Triantaphyllou, E., Mann, S.H. (1989). An examination of the effectiveness of multi-dimensional decision-making methods: a decision-making paradox. Decision Support Systems, 5(3), 303–312. https://doi.org/10.1016/0167-9236(89)90037-7.
Turskis, Z., Keršulienė, V. (2024). SHARDA–ARAS: a methodology for prioritising project managers in sustainable development. Mathematics, 12(2), 219. https://doi.org/10.3390/math12020219.
Uslu, Y.D., Yılmaz, E., Yiğit, P. (2021). Developing qualified personnel selection strategies using MCDM approach: a university hospital practice. In: Strategic Outlook in Business and Finance Innovation: Multidimensional Policies for Emerging Economies. Emerald Publishing Limited, pp. 195–205. https://doi.org/10.1108/978-1-80043-444-820211018.
Van Laarhoven, P.J., Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1–3), 229–241. https://doi.org/10.1016/S0165-0114(83)80082-7.
Wang, P., Li, Y., Wang, Y.H., Zhu, Z.Q. (2015). A new method based on TOPSIS and response surface method for MCDM problems with interval numbers. Mathematical Problems in Engineering, 2015(1). https://doi.org/10.1155/2015/938535.
Wind, Y., Saaty, T.L. (1980). Marketing applications of the analytic hierarchy process. Management Science, 26(7), 641–658. https://doi.org/10.1287/mnsc.26.7.641.
Wu, H.Y., Chen, J.K., Chen, I.S., Zhuo, H.H. (2012). Ranking universities based on performance evaluation by a hybrid MCDM model. Measurement, 45(5), 856–880. https://doi.org/10.1016/j.measurement.2012.02.009.
Xu, Z., Yager, R.R. (2006). Some geometric aggregation operators based on intuitionistic fuzzy sets. International Journal of General Systems, 35(4), 417–433. https://doi.org/10.1080/03081070600574353.
Yager, R.R. (2013a). Pythagorean fuzzy subsets. In: 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS). IEEE, pp. 57–61. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375.
Yager, R.R. (2013b). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958–965. https://doi.org/10.1109/TFUZZ.2013.2278989.
Yager, R.R., Abbasov, A.M. (2013). Pythagorean membership grades, complex numbers, and decision making. International Journal of Intelligent Systems, 28(5), 436–452. https://doi.org/10.1002/int.21584.
Yager, R., Basson, D. (1975). Decision making with fuzzy sets. Decision Sciences, 6(3), 590–600. https://doi.org/10.1111/j.1540-5915.1975.tb01046.x.
Yan, R., Han, Y., Zhang, H., Wei, C. (2024). Location selection of electric vehicle charging stations through employing the spherical fuzzy CoCoSo and CRITIC technique. Informatica, 35(1), 203–225. https://doi.org/10.15388/24-INFOR545.
Yazdani, M., Zarate, P., Zavadskas, E.K., Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501–2519. https://doi.org/10.1108/MD-05-2017-0458.
Yiğit, F. (2023). A three-stage fuzzy neutrosophic decision support system for human resources decisions in organizations. Decision Analytics Journal, 7, 100259. https://doi.org/10.1016/j.dajour.2023.100259.
Zadeh, L. (1963). Optimality and non-scalar-valued performance criteria. IEEE Transactions on Automatic Control, 8(1), 59–60. https://doi.org/10.1109/TAC.1963.1105511.
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X.
Zavadskas, E.K., Turskis, Z. (2008). A new logarithmic normalization method in games theory. Informatica, 19(2), 303–314. https://doi.org/10.15388/Informatica.2008.215.
Zavadskas, E.K., Turskis, Z., Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1), 165–179. https://doi.org/10.3846/20294913.2014.892037.
Zavadskas, E.K., Turskis, Z., Tamosaitiene, J. (2011). Selection of construction enterprises management strategy based on the SWOT and multi-criteria analysis. Archives of Civil and Mechanical Engineering, 11(4), 1063–1082. https://doi.org/10.1016/S1644-9665(12)60096-X.
Zavadskas, E.K., Turskis, Z., Antucheviciene, J., Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Electronics and Electrical Engineering – Elektronika ir Elektrotechnika, 122(6), 3–6. https://doi.org/10.5755/j01.eee.122.6.1810.
Zhü, K. (2014). Fuzzy analytic hierarchy process: fallacy of the popular methods. European Journal of Operational Research, 236(1), 209–217. https://doi.org/10.1016/j.ejor.2013.10.034.
Zimmermann, H.J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45–55. https://doi.org/10.1016/0165-0114(78)90031-3.
Biographies
Jana Subrata
S. Jana, double MSc (applied mathematics, applied statistics and analytics), M. Phil, is an assistant professor of Mathematics and Statistics at the Department of Basic Science and Humanities at Techno International New Town, West Bengal, India. He is also an adjunct faculty member of Seacom Engineering College, Howrah, West Bengal, India. Also, he is working as a guest faculty member of West Bengal State University, Barasat, West Bengal, in the Department of Management and Marketing. Currently, he is pursuing PhD at Jadavpur University, Department of Mathematics. He has more than 14 years of academic experience. His area of interest is linear algebra, probability & statistics, operations research, mathematical finance, fuzzy multi-criteria decision making, etc. He also acted as a resource person in various workshops and research programs conducted by Colleges/Universities. He has published several papers in national and international journals of repute and also published several papers in Scopus, SCI, SSCI, ABDC, Web of Science, and UGC Care-listed journals and in edited books published by foreign publishers of repute like Springer Nature, CRC Press, Routledge, Taylor & Francis, etc. He is a life member of the Calcutta Mathematical Society, the Operational Research Society of India, the Indian Statistical Institute, Kolkata, and the Indian Science Congress Association.
Giri Bibhas Chandra
B.C. Giri is a professor in the Department of Mathematics at Jadavpur University, Kolkata, India. He did his M.S. in Mathematics and PhD in Operations Research, both from Jadavpur University, Kolkata, India. His research interests include inventory/supply chain management, production planning and scheduling, reliability and maintenance. Professor Giri has published more than 300 research papers in journals of international repute. His papers have appeared in journals such as Journal of Cleaner Production, European Journal of Operational Research, Naval Research Logistics, International Journal of Production Research, OMEGA, Journal of the Operational Research Society, International Journal of Production Economics, etc. He was a JSPS Research Fellow at Hiroshima University, Japan, from 2002 to 2004 and a Humboldt Research Fellow at Mannheim University, Germany, from 2007 to 2008.
Turskis Zenonas
Z. Turskis is a professor of technical sciences and a chief research fellow at the Institute of Sustainable Construction, Vilnius Gediminas Technical University. He published more than 200 articles in the WoS database-referred journals. His h-index is 67 in the Clarivate Analytics database. His primary research interests are building technology and management, decision-making theory, computer-aided automation in design, and expert systems.
Jana Chiranjibe
C. Jana is an adjunct faculty member of Saveetha School of Engineering, Saveeth Institute of Medical and Technical Sciences (SIMATS), Chennai 602105, Tamil Nadu, India. His current research interests include multi-criteria decision-making, aggregation operators, decision-support systems, renewable energy, fuzzy optimisation, artificial intelligence, fuzzy algebra and soft algebraic structures. He has published 120 papers, among them 80 are published in the international reputed SCI journals such as Applied Soft Computing, Engineering Applications of Artificial Intelligence, Scientia Iranica, International Journal of Intelligent Systems, Journal of Intelligent and Fuzzy Systems, Soft Computing, Journal of Ambient Intelligence and Humanized Computing, Iranian Journal of Fuzzy Systems, Symmetry, Mathematics, and Knowledge-Based Systems, etc. He has published two edited books, one in IGI Global, USA, 2019 and another in Springer, 2023. He published a book as an author with Elsevier in November 2023. He has served as a reviewer in journals including Soft Computing, Artificial Intelligence Review, IEEE Access, International Journal of Intelligent Systems, Complexity, AIMS-Mathematics, The Journal of Super Computing, Pattern Recognition Letters, Engineering Applications of Artificial Intelligence, Expert Systems with Applications, Applied Soft Computing, Information Sciences, IEEE Transactions on Fuzzy Systems, etc. Now, he is an academic editor of Mathematical Problems in Engineering, SCIE, IF-1.305, International Journal of Computational Intelligence Systems and Journal of Mathematics, SCIE, IF-1.4, and He is an advisory board member of the Heliyon journal, Elsevier, SCIE, IF-4. According to Scopus and Stanford University, he is among the World’s top 2% scientists as of 2022, 2023, and 2024.
Hezam Ibrahim M.
I.M. Hezam received a PhD in operations research and decision support from Menoufia University, Egypt. He held a postdoctoral position in industrial engineering at Pusan National University, Pusan, South Korea. He is an associate professor of operations research at King Saud University, KSA. His research fields are artificial intelligence, optimisation, sustainability, operations research, and decision support systems.
