Document Type : Research Paper
1 Department of Human Resources Management, Institute for Humanities and Cultural Studies, Tehran, Iran.
2 Department of Management and Accounting, College of Farabi, University of Tehran, Iran.
3 Department of Management and Accounting, College of Farabi, University of Tehran, Iran
Multiple criteria decision-making (MCDM) is well known nowadays as a methodology in which a set of techniques are integrated to evaluate a set of alternatives with specified criteria for the purpose of selecting or ranking. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is well-established methodology frequently considered in MCDM analyses. TOPSIS has a sound logic that represents the rationale of human choice and is a scalar value simultaneously taking into account both the best and worst alternatives. Moreover, it has a simple computation process that could be easily programmed and finally it has the ability to rank alternatives on attributes to be visualized on a polyhedron, in at least two dimensions. Despite the advantages of this method, the process of ranking alternative according to related criteria may need more consideration. Typically, there are contributions in this article. First, a new similarity measure has been introduced followed by a modification applied to TOPSIS analyses. Second, the modified similarity technique was subsequently extended in the fuzzy context to cope with the uncertainty inherently existing in human judgments. A numerical example of the personnel selection was presented to demonstrate the possible application of the proposed method in human resource management. The outcome of applying fuzzy similarity method showed a significant distinction in ranking alternatives compered to TOPSIS method. Therefore, the modification is sound to be a proper solution.
- Özcan, T., Çelebi, N., & Esnaf, Ş. (2011). Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem. Expert systems with applications, 38(8), 9773-9779. https://doi.org/10.1016/j.eswa.2011.02.022
- Wang, J. W., Cheng, C. H., & Huang, K. C. (2009). Fuzzy hierarchical TOPSIS for supplier selection. Applied soft computing, 9(1), 377-386. https://doi.org/10.1016/j.asoc.2008.04.014
- Chen, Y., Kilgour, D. M., & Hipel, K. W. (2011). An extreme-distance approach to multiple criteria ranking. Mathematical and computer modelling, 53(5-6), 646-658.
- Düğenci, M. (2016). A new distance measure for interval valued intuitionistic fuzzy sets and its application to group decision making problems with incomplete weights information. Applied soft computing, 41, 120-134. https://doi.org/10.1016/j.asoc.2015.12.026
- Shen, F., Xu, J., & Xu, Z. (2016). An outranking sorting method for multi-criteria group decision making using intuitionistic fuzzy sets. Information sciences, 334, 338-353.
- Efe, B. (2016). An integrated fuzzy multi criteria group decision making approach for ERP system selection. Applied soft computing, 38, 106-117. https://doi.org/10.1016/j.asoc.2015.09.037
- Ju, Y. (2014). A new method for multiple criteria group decision making with incomplete weight information under linguistic environment. Applied mathematical modelling, 38(21-22), 5256-5268.
- Zhang, X., Xu, Z., & Wang, H. (2015). Heterogeneous multiple criteria group decision making with incomplete weight information: a deviation modeling approach. Information fusion, 25, 49-62.
- Zhang, Z., Wang, C., & Tian, X. (2015). Multi-criteria group decision making with incomplete hesitant fuzzy preference relations. Applied soft computing, 36, 1-23.
- Deng, H. (2007). A similarity-based approach to ranking multicriteria alternatives. Advanced intelligent computing theories and applications, with aspects of artificial intelligence: third international conference on intelligent computing, ICIC 2007 (pp. 253-262). Springer Berlin Heidelberg.
- Hwang, C., & Yoon, K. (1981). Multiple attribute decision making: theory and applications. Springer Berlin, Heidelberg.
- Lima-Junior, F. R., & Carpinetti, L. C. R. (2016). Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management. International journal of production economics, 174, 128-141.
- Kusumawardani, R. P., & Agintiara, M. (2015). Application of fuzzy AHP-TOPSIS method for decision making in human resource manager selection process. Procedia computer science, 72, 638-646.
- Patil, S. K., & Kant, R. (2014). A fuzzy AHP-TOPSIS framework for ranking the solutions of knowledge management adoption in supply chain to overcome its barriers. Expert systems with applications, 41(2), 679-693.
- Mandic, K., Delibasic, B., Knezevic, S., & Benkovic, S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic modelling, 43, 30-37.
- Amiri, M., Zandieh, M., Vahdani, B., Soltani, R., & Roshanaei, V. (2010). An integrated eigenvector–DEA–TOPSIS methodology for portfolio risk evaluation in the FOREX spot market. Expert systems with applications, 37(1), 509-516. https://doi.org/10.1016/j.eswa.2009.05.041
- Oztaysi, B. (2014). A decision model for information technology selection using AHP integrated TOPSIS-Grey: the case of content management systems. Knowledge-based systems, 70, 44-54.
- Mir, M. A., Ghazvinei, P. T., Sulaiman, N. M. N., Basri, N. E. A., Saheri, S., Mahmood, N. Z., ... & Aghamohammadi, N. (2016). Application of TOPSIS and VIKOR improved versions in a multi criteria decision analysis to develop an optimized municipal solid waste management model. Journal of environmental management, 166, 109-115. https://doi.org/10.1016/j.jenvman.2015.09.028
- Wood, D. A. (2016). Supplier selection for development of petroleum industry facilities, applying multi-criteria decision making techniques including fuzzy and intuitionistic fuzzy TOPSIS with flexible entropy weighting. Journal of natural gas science and engineering, 28, 594-612.
- Kim, G., Park, C. S., & Yoon, K. P. (1997). Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement. International journal of production economics, 50(1), 23-33. https://doi.org/10.1016/S0925-5273(97)00014-5
- Shih, H., Shyur, H., & Lee, E. (2007). An extension of TOPSIS for group decision making. Mathematical and computer modelling, 45(7), 801-813. https://doi.org/10.1016/j.mcm.2006.03.023
- Chen, S. J., Hwang, C. L., Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision making methods(pp. 289-486). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-46768-4_5
- Nikfalazar, S., Khorshidi, H., & Hamadani, A. (2016). Fuzzy risk analysis by similarity-based multi-criteria approach to classify alternatives. International journal of system assurance engineering and management, 7, 250-256. https://doi.org/10.1007/s13198-016-0414-6
- Haleh, H., Khorshidi, H. A., & Hoseini, S. M. (2010). A new approach for fuzzy risk analysis based on similarity by using decision making approach. 2010 IEEE international conference on management of innovation & technology(pp. 1112-1117). IEEE. https://doi.org/10.1109/ICMIT.2010.5492895
- Singh, N., & Tyagi, K. (2015). Ranking of services for reliability estimation of SOA system using fuzzy multicriteria analysis with similarity-based approach. International journal of system assurance engineering and management, 8, 317-326. https://doi.org/10.1007/s13198-015-0339-5
- Moradi, M., & Ebrahimi, E. (2014). Applying fuzzy AHP and similarity-based approach for economic evaluating companies based on corporate governance measures. Global journal of management studies and researches, 1(1), 10-20.
- Pamucar, D., & Cirovic, G. (2015). The selection of transport and handling resources in logistics centers using multi-attributive border approximation area comparison (MABAC). Expert systems with applications, 42(6), 3016-3028. https://doi.org/10.1016/j.eswa.2014.11.057
- Zavadskas, E. K., Kaklauskas, A., & Sarka, V . (1994). The new method of multicriteria complex proportional assessment of projects. Technological and economic development of economy, 1(3), 131-139.
- Žižović, M., Pamučar, D., Albijanić, M., Chatterjee, P., & Pribićević, I. (2020). Eliminating rank reversal problem using a new multi-attribute model—the RAFSI method. Mathematics, 8(6), 1-16.
- Yu, P. L. (1973). A class of solutions for group decision problems. Management science, 19(8), 936-946.
- Zelrny, M. (1973). Compromise Programming. Multiple criteria decision making, 74(1), 107-115. https://cir.nii.ac.jp/crid/1573387450346632704
- Baležentis, T., & Zeng, S. (2013). Group multi-criteria decision making based upon interval-valued fuzzy numbers: an extension of the MULTIMOORA method. Expert systems with applications, 40(2), 543-550. https://doi.org/10.1016/j.eswa.2012.07.066
- Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
- Lin, C. T., & Chen, Y. T. (2004). Bid/no-bid decision-making–a fuzzy linguistic approach. International journal of project management, 22(7), 585-593.
- Tai, W. S., & Chen, C. T. (2009). A new evaluation model for intellectual capital based on computing with linguistic variable. Expert systems with applications, 36(2), 3483-3488.
- Wang, R. C., & Chuu, S. J. (2004). Group decision-making using a fuzzy linguistic approach for evaluating the flexibility in a manufacturing system. European journal of operational research, 154(3), 563-572.
- Lee, S. H. (2010). Using fuzzy AHP to develop intellectual capital evaluation model for assessing their performance contribution in a university. Expert systems with applications, 37(7), 4941-4947.
- Wu, W. W., & Lee, Y. T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert systems with applications, 32(2), 499-507. https://doi.org/10.1016/j.eswa.2005.12.005
- Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information sciences, 8(3), 199-249. https://doi.org/10.1016/0020-0255(75)90036-5
- Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-III. Information sciences, 9(1), 43-80. https://doi.org/10.1016/0020-0255(75)90017-1
- 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.
- Karabašević, D., Stanujkić, D., & Urošević, S. (2015). The MCDM model for personnel selection based on SWARA and ARAS methods. Management, 20(77), 43-52.
- Polychroniou, P. V., & Giannikos, I. (2009). A fuzzy multicriteria decision‐making methodology for selection of human resources in a Greek private bank. Career development international, 14(4), 372-387.
- Widianta, M. M. D., Rizaldi, T., Setyohadi, D. P. S., & Riskiawan, H. Y. (2018). Comparison of multi-criteria decision support methods (AHP, TOPSIS, SAW & PROMENTHEE) for employee placement. Journal of physics: conference series, 953(1), 012116. DOI: 1088/1742-6596/953/1/012116
- Abdullah, D., Djanggih, H., Suendri, S., Cipta, H., & Nofriadi, N. (2018). Fuzzy model tahani as decision support system for employee promotion. International journal of engineering & technology, 7(2.5), 88-91.
- Baležentis, A., Baležentis, T., & Brauers, W. K. (2012). Personnel selection based on computing with words and fuzzy MULTIMOORA. Expert systems with applications, 39(9), 7961-7967.
- Dursun, M., & Karsak, E. (2010). A fuzzy MCDM approach for personnel selection. Expert systems with applications, 37(6), 4324-4330. https://doi.org/10.1016/j.eswa.2009.11.067
- Bilgehan Erdem, M. (2016). A fuzzy analytical hierarchy process application in personnel selection in it companies: a case study in a spin-off company. Acta physica polonica a, 130(1), 331-334.
- Güngör, Z., Serhadlıoğlu, G., & Kesen, S. (2009). A fuzzy AHP approach to personnel selection problem. Applied soft computing, 9(2), 641-646. https://doi.org/10.1016/j.asoc.2008.09.003
- Sang, X., Liu, X., & Qin, J. (2015). An analytical solution to fuzzy TOPSIS and its application in personnel selection for knowledge-intensive enterprise. Applied soft computing, 30, 190-204.
- Zhang, S., & Liu, S. (2011). A GRA-based intuitionistic fuzzy multi-criteria group decision making method for personnel selection. Expert systems with applications, 38(9), 11401-11405.
- Wang, C., & Wu, H. (2016). A novel framework to evaluate programmable logic controllers: a fuzzy MCDM perspective. Journal of intelligent manufacturing, 27, 315-324.
- Sun, C. C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert systems with applications, 37(12), 7745-7754. https://doi.org/10.1016/j.eswa.2010.04.066
- Safari, H., & Ebrahimi, E. (2014). Using modified similarity multiple criteria decision making technique to rank countries in terms of Human Development Index. Journal of industrial engineering and management (JIEM), 7(1), 254-275.
- Garg, H., & Arora, R. (2020). TOPSIS method based on correlation coefficient for solving decision-making problems with intuitionistic fuzzy soft set information. AIMS mathematics, 5(4), 2944-2966.
- Keikha, A., Garg, H., & Mishmast Nehi, H. (2021). An approach based on combining choquet integral and TOPSIS methods to uncertain MAGDM problems. Soft computing, 25(10), 7181-7195.
- Garg, H., Keikha, A., & Mishmast Nehi, H. (2020). Multiple-attribute decision-making problem using topsis and choquet integral with hesitant fuzzy number information. Mathematical problems in engineering, 2020, 9874951. https://doi.org/10.1155/2020/9874951
- Wang, L., Wang, H., Xu, Z., & Ren, Z. (2019). The interval-valued hesitant pythagorean fuzzy set and its applications with extended TOPSIS and choquet integral-based method. International journal of intelligent systems, 34(6), 1063-1085.