Clustering and Ranking of Provinces in Terms of Investment Security Based on Multi-Criteria Multi-Period Decision-Making

Document Type : Original Article

Authors

1 MSc., Faculty of Industrial Engineering, Department of Industrial Engineering, SR.C., Islamic Azad University, Science and Research Branch, Tehran, Iran Email: Aliganji201@gmail.com / https://orcid.org/0009-0007-9873-9987

2 Corresponding Author, Associate Prof., Faculty of Industrial Engineering, Department of Industrial Engineering, Se.C., Islamic Azad University, Semnan, Iran Email: Alijahan@iau.ir / Iranalijahan@yahoo.com / https://orcid.org/0000-0001-6347-1676

10.22091/jemsc.2026.14237.1314

Abstract

Investment security is a critical component of economic development in countries, as its enhancement fosters investor confidence and encourages participation in productive sectors and financial markets. Given the regional disparities across the country, assessing investment security at the provincial level is essential. This study aims to rank and cluster Iranian provinces in terms of investment security over the period 2021–2023, using a multi-criteria, multi-period decision-making approach. For this purpose, two techniques—MP-TOPSIS and MULTI-MOORA—were employed to evaluate and rank the provinces, and the results were compared. Subsequently, the k-means clustering method was applied to group provinces into homogeneous clusters. The data were obtained from 12 policy reports published by the Iranian Parliament Research Center, and the weighting of criteria was performed using three methods: MEREC, Shannon entropy, and CRITIC. The findings reveal that Semnan, Golestan, and Hormozgan provinces exhibit the highest levels of investment security, while Tehran, Sistan and Baluchestan, and Kohgiluyeh and Boyer-Ahmad rank lowest. Additionally, the correlation between the two ranking methods was estimated at 87%, indicating a high degree of consistency and validating the proposed model. The results suggest that macroeconomic stability, administrative transparency, and robust legal frameworks are the most influential factors in determining investment security. Region-specific policy recommendations based on these analyses can significantly improve the investment climate across the country

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References

Abbasi Asl, M., Salehi, M., Fatemeh Hosseini, A., Zairi, F., & Nasrollahi, A., (2012). Clustering of the country's provinces based on health profile indicators in the Islamic Republic of Iran in 2006. https://civilica.com/doc/1323277
Abdoli, G., Kardgar, R., Kazemi, A., & Molaei Gholichi, M. (2017). Ranking of the provinces of the country in terms of creating added value in economic sub-sectors based on the multi-criteria decision-making model (VIKOR).  https://civilica.com/doc/1909896
Alavi Ghassoni, J., Mirei, M., & Ahmadi, A. (2017). Security and regional development links in geographical regions of Iran; Case study: Khorasan Razavi Province. International Quarterly Journal of Geopolitics, 13(45), 125-145. https://dor.isc.ac/dor/20.1001.1.17354331.1396.13.45.5.6
Amadeh, H., Emami Meybodi, A., & Azadi-Nejad, A., (2009) Ranking the provinces of the country in terms of technical efficiency of the industrial sector using the data envelopment analysis method. Monetary and Financial Economics, 16(29), 162-180. https://doi.org/10.22067/pm.v16i29.27199
Atoufi, G., & Javidan Darogar H. (2016). The importance and position of economic security in sustainable economic development. Journal of Nations Research, 1(6), 4. https://magiran.com/p1548570
Ayan, B., Abacıoğlu, S., & Basilio, M. P. (2023). A comprehensive review of the novel weighting methods for multi-criteria decision-making. Information, 14(5), 285. https://doi.org/10.3390/info14050285
Brauers, W. K., & Zavadskas, E., (2012). Robustness of MULTIMOORA: A Method for multi-objective optimization. informatica, Vilnius university, 23(1), 1-25. 
Ghafarifard, M., Khashee, M., Safdari, R., & Alameh, M. (2020). Combined indicator of religious capital and ranking of the provinces of Iran using by multi-criteria decision making patterns. Islamic Economics & Banking, 9(30), 181-200. http://mieaoi.ir/article-1-935-fa.html ,
Gorji, E., & Hashemian, M. (2007). Comparative analysis of economic security components in Iran. Strategic Defense Studies, 8(29), 55-78. https://sid.ir/paper/132794/fa
Hemmati, Y., & Salman Mahini, A. (2022). Automatic analysis of the development of Iranian provinces and its trend by preparing software in Excel environment. Environmental Research, 13. https://doi.org/10.22034/eiap.2022.158575
Jahangiri, A., & Jahangiri, M. (2019). Analyzing the performance trend of direct and indirect treatment of the Social Security Organization over the past 25 years using dynamic multi-indicator decision-making and examining the correlation between them. Health Image, 10(4), 270-279. https://sid.ir/paper/265194/fa
Karami, A., Nouri, M., & Mousavi, N. (2016). Evaluation and classification of agricultural development in Kohgiluyeh and Boyer-Ahmad provinces. https://doi.org/10.22034/iaes.2017.22230
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525. https://doi.org/10.3390/sym13040525
 Lin, Y.-H., P.-C. Lee, & Ting, H.-I. (2008). Dynamic multi-attribute decision making model with grey number evaluations. Expert Systems with Applications, 35(4), 1638-1644.
Mohammadi, A., Amiri,Y., & Jobor, M. (2014). Prioritizing status of societal security index and ranking Iran's provinces based on societal security level using Gray Relational Analysis (GRA). Entezam-e-Ejtemaei, 6(3), 7-34. https://magiran.com/p1418630
Ozkaya, G., & Erdin, C. (2020). Evaluation of smart and sustainable cities through a hybrid MCDM approach based on ANP and TOPSIS technique. Heliyon, 6(10).
Rahnama, M., & Ahmadipour, Z. (2003). Classification of the national division system, Geographical Research Journal, 2003, 69. https://sid.ir/paper/418280/fa  
Salehi, M. (2022). Ranking of the country's provinces based on human development and human capital indicators. Quarterly Journal of Research and Planning in Higher Education, 24(1), 27-49. https://sid.ir/paper/68043/fa.
Soltanifar, M. (2024). A new interval for ranking alternatives in multi attribute decision making problems. Journal of Applied Research on Industrial Engineering, 11(1), 37–56.
Tehrani, I., Markaz Mallamiri, A., Ahmadian, M., & Seyed amir, S. (2018). measuring investment security in iran: conceptual framework, Methodology, Index and Indicators (1st ed.). Monthly Journal of Expert Reports of the Research Center of the Islamic Consultative Assembly, 26(2).
Wang, Y.,Yunna, W., Kaifeng, C., Chuanbo, X., & Lingwenying, L. (2017). Social sustainability assessment of small hydropower with hesitant PROMETHEE method. Sustainable Cities and Society, 35, 522-537. ISSN 2210-6707. https://doi.org/10.1016/j.scs.2017.08.034
Witt, T., & Klumpp, M. (2021). Multi-Period multi-criteria decision making under uncertainty: A renewable energy transition case from Germany. Sustainability, 13, 6300. https://doi.org/10.3390/su13116300
Yu, M., & Wang, N. (2023). The influence of geopolitical risk on international direct investment and its countermeasures. Sustainability, 15, 2522. https://doi.org/10.3390/su15032522
Zhang, Y., Zhang, Y., Zhang, H., & Zhang, Y. (2022). Evaluation on new first-tier smart cities in China based on entropy method and TOPSIS. Ecological Indicators, 145, 109616. https://doi.org/10.1016/j.ecolind.2022.109616
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