Prioritizing Fars Province Industrial Clusters by Copeland Aggregation of Qualiflex Hesitant Fuzzy and Topsis Hesitant Fuzzy

Subject Areas : Decision Analysis

Fatemeh Allahakbari ¹ , Ali Mohamadi ² , Payam Shojaei ³ , hadi mirghaderi ⁴

1 - management department, Faculty of Economics, Management and Social Sciences, Shiraz University,Shiraz, Iran
2 - Management department, Faculty of Economics, Management and Social Science, Shiraz University, Shiraz, Iran
3 - Management department, Faculty of Economics, Management and Social Science, Shiraz University, Shiraz, Iran
4 - Department of management, management and social science school, shiraz university

Received: 2023-01-07 Accepted : 2023-03-04 Published : 2023-07-01

Keywords: Industrial cluster, Prioritizing, Topsis Hesitant fuzzy, Copeland, Qualiflex Hesitant fuzzy,

Abstract :

In recent years, industrial clusters have received considerable attention from economists and industry analysts because they are seen as the main reason for certain economic regions' economic growth and success. For many Industrial States Organization, the selection of industrial clusters has become a critical strategic consideration due to the Budget allocation priority. In this paper, an extended qualitative flexible multiple (QUALIFLEX) methods is used to solve problems regarding the priority among this cluster using probability hesitant fuzzy information, which can lead to allocating the budget for industrial clusters more effectively. For more accuracy, we have applied a Hesitant fuzzy Topsis for prioritizing. Both rankings have been aggregated by the Copeland method. From our research results, the Larestan Muscat is of great importance, and Abade Inlaid Wood, Citrus packaging, Shiraz Marquetry, and Niriz stone have ranked respectively.

References:

1. Alao, M. A., Popoola, O. M., & Ayodele, T. R. (2022). Sustainable prime movers selection for biogas-based combined heat and power for a community microgrid: A hybrid fuzzy multi criteria decision-making approach with consolidated ranking strategies. Energy Conversion and Management: X, 16, 100281. 1016/j.ecmx.2022.100281


2. Alexander, B., Tatiana, K., & Svetlana, U. (2013). Formation of industrial clusters using method of virtual enterprises. Procedia Economics and Finance, 5, 68-72. 1016/S2212-5671(13)00011-7


3. Abbasi Bastami, R., Ehtesham Rasi, R., & Abedi, S. (2021). Supplier’s Selection Based on Lean-Green Production Indicators by Goal Programming, Fuzzy DEMATEL and Fuzzy Quality Function Development. Journal of System Management, 6(3), 169-204, doi:30495/jsm.2021.678900.


4. Camci, A., Temur, G. T., & Beskese, A. (2018). CNC router selection for SMEs in woodwork manufacturing using hesitant fuzzy AHP method. Journal of Enterprise Information Management, doi: 1108/JEIM-01-2018-0017.


5. Chen, N., Xu, Z., & Xia, M. (2013). Interval-valued hesitant preference relations and their applications to group decision making. Knowledge-Based Systems, 37, 528-540, doi: 1016/j.knosys.2012.09.009


6. Chen, T.-Y., & Wang, J.-C. (2009). Interval-valued fuzzy permutation method and experimental analysis on cardinal and ordinal evaluations. Journal of Computer and System Sciences, 75(7), 371-387, doi: 1016/j.jcss.2009.03.002


7. Dortaj, A., Maghsoudy, S., Ardejani, F. D., & Eskandari, (2020). A hybrid multi-criteria decision making method for site selection of subsurface dams in semi-arid region of Iran. Groundwater for Sustainable Development, 10, 100284, doi: 10.1016/j.gsd.2019.100284


8. Ecer, F. (2018). An integrated Fuzzy AHP and ARAS model to evaluate mobile banking services. Technological and Economic Development of Economy, 24(2), 670–695-670–695, doi: 3846/20294913.2016.1255275.


9. Andrea Giovannetti, 2012. "Financial Contagion in Industrial Clusters: A Dynamical Analysis and Network Simulation," Department of Economics University of Siena 654, Department of Economics, University of Siena. https://ideas.repec.org/p/usi/wpaper/654.html.


10. Hajihassani, V., Rangriz, H., & Hajikarimi, B. (2021). Prioritize Development of Corporate Social Responsibility in Food Industry Based on the Taxonomy Method. Journal of System Management, 7(1), 49-70, doi: 30495/jsm.2021.1921446.1437


11. He, K., & Zhu, N. (2022). Strategic emerging industry layout based on analytic hierarchy process and fuzzy comprehensive evaluation: A case study of Sichuan province. PloS one, 17(3), e0264578, doi: 1371/journal.pone.0264578.


12. Hussain, Z., & Yang, -S. (2018). Entropy for hesitant fuzzy sets based on Hausdorff metric with construction of hesitant fuzzy TOPSIS. International Journal of Fuzzy Systems, 20(8), 2517-2533, doi: 10.1007/s40815-018-0523-2.


13. Iran small industries and industrial parks organization – ISIPO. (2014). Business clusters.Retrieved 22 April, 2018, from http://isipo.ir/?part=menu&inc=menu&id=67


14. Jiang, M. Rees, L. Z. Yu and Q. Chen, "Prioritization of strategies to achieve world-class manufacturing using a hybrid approach of fuzzy multiple criteria technique: Case study from Quanzhou industrial clusters," 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Xiamen, China, 2014, pp. 249-254, doi: 10.1109/FSKD.2014.6980841.


15. Jote, N., Beshah, B., Kitaw, D., & Abraham, A. (2014). FuzzyAHP-Based Micro and Small Enterprises’ Cluster Identification. Journal of Network and Innovative Computing, 2(2014), 277-282.


16. Kahraman, C., Onar, S. Ç., & Öztayşi, B. (2018). B2C marketplace prioritization using hesitant fuzzy linguistic AHP. International Journal of Fuzzy Systems, 20(7), 2202-2215, doi: 1007/s40815-017-0429-4.


17. Kaya, T. (2010). Multi-attribute evaluation of website quality in E-business using an integrated fuzzy AHPTOPSIS methodology. International Journal of Computational Intelligence Systems, 3(3), 301-314, doi: 1080/18756891.2010.9727701.


18. Khanmirzaee, S., Jafari, M., & Akhavan, P. (2021). Analyzing the competitive advantage’s criteria of science and technology parks and incubators using DEMATEL approach. Journal of the Knowledge Economy, 1-17. doi:1007/s13132-021-00802-0.


19. Lin, C.-L., & Tzeng, G.-H. (2009). A value-created system of science (technology) park by using DEMATEL. Expert systems with applications, 36(6), 9683-9697, doi: 1016/j.eswa.2008.11.040


20. Lin, M.-I., Lee, Y.-D., & Ho, T.-N. (2013). Model Building to Evaluate Performance of Industrial Clusters with Hybrid DBA Approaches. Paper presented at the International Conference on Business Strategy and Organizational Behaviour (BizStrategy). Proceedings.


21. Liu, Y. Xu, R. Montes, R. -X. Ding and F. Herrera, "Alternative Ranking-Based Clustering and Reliability Index-Based Consensus Reaching Process for Hesitant Fuzzy Large Scale Group Decision Making," in IEEE Transactions on Fuzzy Systems, vol. 27, no. 1, pp. 159-171, Jan. 2019, doi: 10.1109/TFUZZ.2018.2876655.


22. Ren, Z., Xu, Z., & Wang, H. (2018). Normal wiggly hesitant fuzzy sets and their application to environmental quality evaluation. Knowledge-Based Systems, 159, 286-297, doi: 1016/j.knosys.2018.06.024


23. Roghani, M., Modiri, M., Fathi Hafshjani, K., & Alirezaei, A. (2021). Futurology of Multi-Criteria Decision Making Techniques Using Philosophical Assumptions of Paradigms in Scenario Writing. Journal of System Management, 6(3), 139-168, doi: 30495/jsm.2021.678899.


24. Shakib, M. D. (2020). Using system dynamics to evaluate policies for industrial clusters development. Computers & Industrial Engineering, 147, 106637, doi:1016/j.cie.2020.106637.


25. Sulistiandi, S., Marpaung, B., & Sunardi, O. (2020). Clustering on Small-scale Food Manufacturing Industry in West Jakarta: A Fuzzy Analytical Hierarchy Process Paper presented at the IOP Conference Series: Materials Science and Engineering, doi: 10.1088/1757-899X/847/1/012093.


26. Sun, C.-C., Lin, G. T., & Tzeng, G.-H. (2009). The evaluation of cluster policy by fuzzy MCDM: Empirical evidence from HsinChu Science Park. Expert systems with applications, 36(9), 11895-11906, doi: https://doi.org/10.1016/j.eswa.2009.04.019.


27. Sun, G., Guan, X., Yi, X., & Zhou, Z. (2018). Grey relational analysis between hesitant fuzzy sets with applications to pattern recognition. Expert systems with applications, 92, 521-532.


28. Tian, Z.-p., Wang, J., Wang, J.-q., & Zhang, H.-y. (2016). A likelihood-based qualitative flexible approach with hesitant fuzzy linguistic information. Cognitive Computation, 8(4), 670-683, doi: 1016/j.eswa.2017.09.048.


29. Tong, X., & Tao, Y. (2017). A quantitative evaluating method of the effectiveness of hi-tech industrial cluster policies based on the fuzzy QFD. Paper presented at the Proceedings of the 2017 International Conference on Management Engineering, Software Engineering and Service Sciences, doi: 1145/3034950.3034979.


30. Torra, V., & Narukawa, Y. (2009). On hesitant fuzzy sets and decision. Paper presented at the 2009 IEEE international conference on fuzzy systems, doi: 1109/FUZZY.2009.5276884.


31. Wang, J.-C., Tsao, C.-Y., & Chen, T.-Y. (2015). A likelihood-based QUALIFLEX method with interval type-2 fuzzy sets for multiple criteria decision analysis. Soft computing, 19(8), 2225-2243. , doi: 1007/s00500-014-1404-8.


32. Xia, M., & Xu, Z. (2011). Hesitant fuzzy information aggregation in decision making. International journal of approximate reasoning, 52(3), 395-407, doi: 1016/j.ijar.2010.09.002


33. Zhang, X., & Xu, Z. (2015). Hesitant fuzzy QUALIFLEX approach with a signed distance-based comparison method for multiple criteria decision analysis. Expert systems with applications, 42(2), 873-884, doi: 1016/j.eswa.2014.08.056