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Analytical hierarchy process: revolution and evolution

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Abstract

The Analytical Hierarchy Process (AHP) is a reliable, rigorous, and robust method for eliciting and quantifying subjective judgments in multi-criteria decision-making (MCDM). Despite the many benefits, the complications of the pairwise comparison process and the limitations of consistency in AHP are challenges that have been the subject of extensive research. AHP revolutionized how we resolve complex decision problems and has evolved substantially over three decades. We recap this evolution by introducing five new hybrid methods that combine AHP with popular weighting methods in MCDM. The proposed methods are described and evaluated systematically by implementing a widely used example in the AHP literature. We show that (i) the hybrid methods proposed in this study require fewer expert judgments than AHP but deliver the same ranking, (ii) a higher degree of involvement in the hybrid voting AHP methods leads to higher acceptability of the results when experts are also the decision-makers, and (iii) experts are more motivated and attentive in methods requiring fewer pairwise comparisons and less interaction, resulting in a more efficient process and higher acceptability.

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References

  • Abastante, F., Corrente, S., Greco, S., Ishizaka, A., & Lami, I. M. (2019). A new parsimonious AHP methodology: Assigning priorities to many objects by comparing pairwise few reference objects. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2019.02.036

    Article  Google Scholar 

  • Aguarón, J., Escobar, M. T., & Moreno-Jiménez, J. M. (2020). Reducing inconsistency measured by the geometric consistency index in the analytic hierarchy process. European Journal of Operational Research, 288(2), 576–583.

    Article  Google Scholar 

  • Ahmadi, H. B., Kusi-Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126, 99–106.

    Article  Google Scholar 

  • Alimardani, M., Zolfani, S. H., Aghdaie, M. H., & Jolanta, T. (2013). A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment. Technological and Economic Development of Economy, 19(3), 533–548. https://doi.org/10.3846/20294913.2013.814606

    Article  Google Scholar 

  • Amenta, P., Lucadamo, A., & Marcarelli, G. (2020a). On the transitivity and consistency approximated thresholds of some consistency indices for pairwise comparison matrices. Information Sciences, 507, 274–287.

    Article  Google Scholar 

  • Amenta, P., Lucadamo, A., & Marcarelli, G. (2020b). On the choice of weights for aggregating judgments in non-negotiable AHP group decision making. European Journal of Operational Research, 288(1), 294–301.

    Article  Google Scholar 

  • Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264.

    Article  Google Scholar 

  • Ansari, Z. N., Kant, R., & Shankar, R. (2020). Evaluation and ranking of solutions to mitigate sustainable remanufacturing supply chain risks: A hybrid fuzzy SWARA-fuzzy COPRAS framework approach. International Journal of Sustainable Engineering. https://doi.org/10.1080/19397038.2020.1758973

    Article  Google Scholar 

  • Ban, A. I., Ban, O. I., Bogdan, V., Popa, D. C. S., & Tuse, D. (2020). Performance evaluation model of Romanian manufacturing listed companies by fuzzy AHP and TOPSIS. Technological and Economic Development of Economy, 26(4), 808–836.

    Article  Google Scholar 

  • Belton, V., & Stewart, T. (2002). Multiple criteria decision analysis: an integrated approach. Springer Science & Business Media.

    Book  Google Scholar 

  • Bodin, L., & Gass, S. I. (2004). Exercises for teaching the analytic hierarchy process. Informstransactions on Education, 4(2), 1–13.

    Google Scholar 

  • Borda, J.-C. de. (1781). Mémoire sur les élections au scrutin: Histoire de l’Académie Royale des Sciences. Paris, France, 12.

  • Bouroumine, Y., Bahi, L., Ouadif, L., Elhachmi, D., & Errouhi, A. A. (2020). Sitting MSW landfill combining GIS and analytic hierarchy process (AHP), case study: Ajdir, Morocco. International Journal of Advanced Research in Engineering and Technology (IJARET), 11(5).

  • Brunelli, M. (2018). A survey of inconsistency indices for pairwise comparisons. International Journal of General Systems, 47(8), 751–771.

    Article  Google Scholar 

  • Calabrese, A., Costa, R., Levialdi, N., & Menichini, T. (2019). Integrating sustainability into strategic decision-making: A fuzzy AHP method for the selection of relevant sustainability issues. Technological Forecasting and Social Change, 139, 155–168.

    Article  Google Scholar 

  • Çavmak, D., & Çavmak, Ş. (2020). Using AHP to prioritize barriers in developing medical tourism: Case of Turkey. International Journal of Travel Medicine and Global Health, 8(2), 73–79.

    Article  Google Scholar 

  • Chaiyaphan, C., & Ransikarbum, K. (2020). Criteria analysis of food safety using the analytic hierarchy process (AHP) - A case STUDY of Thailand’s fresh markets. E3S Web of Conferences, 141, 02001.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Article  Google Scholar 

  • Contreras, I. (2011). A DEA-inspired procedure for the aggregation of preferences. Expert Systems with Applications, 38(1), 564–570.

    Article  Google Scholar 

  • Cook, W. D., & Kress, M. (1990). A data envelopment model for aggregating preference rankings. Management Science, 36(11), 1302–1310.

    Article  Google Scholar 

  • Darvishi, L., Daryaei, M. G., & Kouchi, A. H. S. (2020). Comparison of statistical modeling and AHP Methods in fire risk assessment in Oak forests of Iran. Fores Res, 9, 229.

    Google Scholar 

  • Das, B., Bordoloi, R., Thungon, L. T., Paul, A., Pandey, P. K., Mishra, M., & Tripathi, O. P. (2020). An integrated approach of GIS, RUSLE and AHP to model soil erosion in West Kameng watershed, Arunachal Pradesh. Journal of Earth System Science, 129(1), 1–18.

    Article  Google Scholar 

  • de Jesus França, L. C., Mucida, D. P., Santana, R. C., de Morais, M. S., Gomide, L. R., & de Meneses Bateira, C. V. (2020). AHP Approach applied to multi-criteria decisions in environmental fragility mapping. Floresta, 50(3), 1623–1632.

    Article  Google Scholar 

  • Delice, E. K., & Can, G. F. (2020). A new approach for ergonomic risk assessment integrating KEMIRA, best–worst and MCDM methods. Soft Computing, 24(19), 15093–15110. https://doi.org/10.1007/s00500-020-05143-9

    Article  Google Scholar 

  • Duleba, S. (2020). Introduction and comparative analysis of the multi-level parsimonious AHP methodology in a public transport development decision problem. Journal of the Operational Research Society. https://doi.org/10.1080/01605682.2020.1824553

    Article  Google Scholar 

  • Faramondi, L., Oliva, G., & Bozóki, S. (2020). Incomplete analytic hierarchy process with minimum weighted ordinal violations. International Journal of General Systems, 49(6), 574–601.

    Article  Google Scholar 

  • Foroughi, A. A., & Tamiz, M. (2005). An effective total ranking model for a ranked voting system. Omega, 33(6), 491–496.

    Article  Google Scholar 

  • Gál, T., Stewart, T. J., & Hanne, T. (1999). Multicriteria decision making: Advances in MCDM models, algorithms, theory, and applications, volume 21 of international series in operations research & management science. Kluwer Academic Publishers, Boston, Dordrecht, London.

  • Ghamari, A., Abdollahi, B., Zeinabadi, H. R., & Tabeshfar, G. H. (2017). Assessment of organizational excellence based on analytical hierarchy process (AHP) emphasizing on the development of Bank Shahr economic capabilities. Journal of Urban Economics and Management, 5(19), 1–13.

    Article  Google Scholar 

  • Ghorshi Nezhad, M. R., Zolfani, S. H., Moztarzadeh, F., Zavadskas, E. K., & Bahrami, M. (2015). Planning the priority of high tech industries based on SWARA-WASPAS methodology: The case of the nanotechnology industry in Iran. Economic Research-Ekonomska Istraživanja, 28(1), 1111–1137. https://doi.org/10.1080/1331677X.2015.1102404

    Article  Google Scholar 

  • Goswami, S., & Mitra, S. (2020). Selecting the best mobile model by applying AHP-COPRAS and AHP-ARAS decision making methodology. International Journal of Data and Network Science, 4(1), 27–42.

    Article  Google Scholar 

  • Green, R. H., Doyle, J. R., & Cook, W. D. (1996). Preference voting and project ranking using DEA and cross-evaluation. European Journal of Operational Research, 90(3), 461–472.

    Article  Google Scholar 

  • Güler, M., Mukul, E., & Büyüközkan, G. (2019). Business intelligence system selection with AHP-VIKOR methodology. 6th International Conference on New Ideas in Management, Economics and Accounting, Paris, France, 57–72.

  • Hadi-Vencheh, A., & Niazi-Motlagh, M. (2011). An improved voting analytic hierarchy process–data envelopment analysis methodology for suppliers selection. International Journal of Computer Integrated Manufacturing, 24(3), 189–197.

    Article  Google Scholar 

  • Hajkowicz, S. A., McDonald, G. T., & Smith, P. N. (2000). An evaluation of multiple objective decision support weighting techniques in natural resource management. Journal of Environmental Planning and Management, 43(4), 505–518.

    Article  Google Scholar 

  • Han, Y., Wang, Z., Lu, X., & Hu, B. (2020). Application of AHP to Road Selection. ISPRS International Journal of Geo-Information, 9(2), 86. https://doi.org/10.3390/ijgi9020086

    Article  Google Scholar 

  • Harker, P. T. (1987). Incomplete pairwise comparisons in the analytic hierarchy process. Mathematical Modelling, 9(11), 837–848.

    Article  Google Scholar 

  • Hashimoto, A. (1997). A ranked voting system using a DEA/AR exclusion model: A note. European Journal of Operational Research, 97(3), 600–604.

    Article  Google Scholar 

  • Hwang, C.-L., & Yoon, K. (1981). Multiple Attribute Decision Making (Vol. 186). Springer.

    Google Scholar 

  • İnce, M., Yiğit, T., & Hakan Işik, A. (2020). A novel hybrid fuzzy AHP-GA method for test sheet question selection. International Journal of Information Technology & Decision Making, 19(02), 629–647. https://doi.org/10.1142/S0219622020500054

    Article  Google Scholar 

  • Ishizaka, A., & Labib, A. (2009). Analytic hierarchy process and expert choice: Benefits and limitations. Or Insight, 22(4), 201–220.

    Article  Google Scholar 

  • Ishizaka, A., & Labib, A. (2011a). Review of the main developments in the analytic hierarchy process. Expert Systems with Applications, 38(11), 14336–14345.

    Google Scholar 

  • Ishizaka, A., & Labib, A. (2011b). Selection of new production facilities with the group analytic hierarchy process ordering method. Expert Systems with Applications, 38(6), 7317–7325.

    Article  Google Scholar 

  • Jain, V., & Ajmera, P. (2019). Evaluation of performance factors of FMS by combined decision making methods as AHP, CMBA and ELECTRE methodology. Management Science Letters, 9(4), 519–534.

    Article  Google Scholar 

  • Jamali, G., Asl, E. K., Zolfani, S. H., & Šaparauskas, J. (2017). Analysing LARG supply chain management competitive strategies in Iranian cement industries [Analyzování konkurenčních strategií LARG řízení dodavatelského řetĕzce v Íránském cementárenském odvĕtví].

  • Juodagalvienė, B., Turskis, Z., Šaparauskas, J., & Endriukaitytė, A. (2017). Integrated multi-criteria evaluation of house’s plan shape based on the edas and swara methods. Engineering Structures and Technologies, 9(3), 117–125. https://doi.org/10.3846/2029882X.2017.1347528

    Article  Google Scholar 

  • Katranci, A., & Kundakci, N. (2020). SWARA Temelli Bulanık COPRAS Yöntemi ile Soğuk Hava Deposu Seçimi. Optimum: Journal of Economics & Management Sciences/Ekonomi ve Yönetim Bilimleri Dergisi, 7(1), 63–80

  • Keršuliene, 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.

    Article  Google Scholar 

  • Khan, A. A., Shameem, M., Kumar, R. R., Hussain, S., & Yan, X. (2019). Fuzzy AHP based prioritization and taxonomy of software process improvement success factors in global software development. Applied Soft Computing, 83, 105648.

    Article  Google Scholar 

  • Köksalan, M., & Zionts, S. (2012). Multiple criteria decision making in the new millennium: Proceedings of the Fifteenth International Conference on Multiple Criteria Decision Making (MCDM) Ankara, Turkey, July 10–14, 2000 (Vol. 507). Springer Science & Business Media.

  • Leal, J. E. (2020). AHP-express: A simplified version of the analytical hierarchy process method. MethodsX, 7, 100748.

    Article  Google Scholar 

  • Liang, F., Brunelli, M., & Rezaei, J. (2020). Consistency issues in the best worst method: Measurements and thresholds. Omega, 96, 102175.

    Article  Google Scholar 

  • Liu, F.-H.F., & Hai, H. L. (2005). The voting analytic hierarchy process method for selecting supplier. International Journal of Production Economics, 97(3), 308–317.

    Article  Google Scholar 

  • Llamazares, B., & Pena, T. (2009). Preference aggregation and DEA: An analysis of the methods proposed to discriminate efficient candidates. European Journal of Operational Research, 197(2), 714–721.

    Article  Google Scholar 

  • Lotfi, F. H., Rostamy-Malkhalifeh, M., Aghayi, N., Beigi, Z. G., & Gholami, K. (2013). An improved method for ranking alternatives in multiple criteria decision analysis. Applied Mathematical Modelling, 37(1–2), 25–33.

    Article  Google Scholar 

  • Macharis, C., Springael, J., De Brucker, K., & Verbeke, A. (2004). PROMETHEE and AHP: The design of operational synergies in multi-criteria analysis: Strengthening PROMETHEE with ideas of AHP. European Journal of Operational Research, 153(2), 307–317.

    Article  Google Scholar 

  • Mahmudova, S., & Jabrailova, Z. (2020). Development of an algorithm using the AHP method for selecting software according to its functionality. Soft Computing, 24(11), 8495–8502. https://doi.org/10.1007/s00500-020-04902-y

    Article  Google Scholar 

  • Millet, I., & Wedley, W. C. (2002). Modelling risk and uncertainty with the analytic hierarchy process. Journal of Multi-Criteria Decision Analysis, 11(2), 97–107.

    Article  Google Scholar 

  • Mishra, A. R., Rani, P., Pandey, K., Mardani, A., Streimikis, J., Streimikiene, D., & Alrasheedi, M. (2020). Novel multi-criteria intuitionistic fuzzy SWARA–COPRAS approach for sustainability evaluation of the bioenergy production process. Sustainability, 12(10), 4155. https://doi.org/10.3390/su12104155

    Article  Google Scholar 

  • Narayanan, A. K., & Jinesh, N. (2018). Application of SWARA and TOPSIS methods for supplier selection in a casting unit. International Journal of Engineering Research & Technology, 7(5), 456–458.

    Google Scholar 

  • Noguchi, H., Ogawa, M., & Ishii, H. (2002). The appropriate total ranking method using DEA for multiple categorized purposes. Journal of Computational and Applied Mathematics, 146(1), 155–166.

    Article  Google Scholar 

  • Obata, T., & Ishii, H. (2003). A method for discriminating efficient candidates with ranked voting data. European Journal of Operational Research, 151(1), 233–237.

    Article  Google Scholar 

  • Ogundoyin, S. O., & Kamil, I. A. (2020). A Fuzzy-AHP based prioritization of trust criteria in fog computing services. Applied Soft Computing, 97, 106789.

    Article  Google Scholar 

  • Panahi, S., Khakzad, A., & Afzal, P. (2017). Application of stepwise weight assessment ratio analysis (SWARA) for copper prospectivity mapping in the Anarak region, central Iran. Arabian Journal of Geosciences, 10(22), 484.

    Article  Google Scholar 

  • Pérez Vergara, I. G., Arias Sánchez, J. A., Poveda-Bautista, R., & Diego-Mas, J.-A. (2020). Improving distributed decision making in inventory management: A combined ABC-AHP approach supported by teamwork. Complexity, 2020, 1–13. https://doi.org/10.1155/2020/6758108

    Article  Google Scholar 

  • Pishchulov, G., Trautrims, A., Chesney, T., Gold, S., & Schwab, L. (2019). The voting analytic hierarchy process revisited: A revised method with application to sustainable supplier selection. International Journal of Production Economics, 211, 166–179. https://doi.org/10.1016/j.ijpe.2019.01.025

    Article  Google Scholar 

  • Prasad, R. (2019). Selection of internal safety auditors in an Indian construction organization based on the SWARA and ARAS methods. Journal of Occupational Health and Epidemiology, 8(3), 134–140.

    Article  Google Scholar 

  • Ramanathan, R. (2001). A note on the use of the analytic hierarchy process for environmental impact assessment. Journal of Environmental Management, 63(1), 27–35.

    Article  Google Scholar 

  • Rani, P., & Mishra, A. R. (2020). Single-valued neutrosophic SWARA-VIKOR framework for performance assessment of eco-industrial thermal power plants. ICSES Trans. Neural Fuzzy Comput, 3, 1–9.

    Google Scholar 

  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.

    Article  Google Scholar 

  • Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001

    Article  Google Scholar 

  • Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 135, 577–588.

    Article  Google Scholar 

  • Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems with Applications, 42(23), 9152–9164. https://doi.org/10.1016/j.eswa.2015.07.073

    Article  Google Scholar 

  • Saaty, T. L. (1980). The analytic hierarchy process Mcgraw Hill, New York. Agricultural Economics Review70.

  • Saaty, R. (2018). A validation of the effectiveness of inner dependence in an ANP model. International Journal of the Analytic Hierarchy Process. https://doi.org/10.13033/ijahp.v10i2.594

    Article  Google Scholar 

  • Saaty, T. L. (2006). Fundamentals of decision making with the analytic hierarchy process. RWS Publications.

    Google Scholar 

  • Saaty, T. L. (2013). The modern science of multicriteria decision making and its practical applications: The AHP/ANP approach. Operations Research, 61(5), 1101–1118.

    Article  Google Scholar 

  • Sales, A. C. M., de Guimarães, L. G. A., Veiga Neto, A. R., El-Aouar, W. A., & Pereira, G. R. (2020). Risk assessment model in inventory management using the AHP method. Gestão & Produção. https://doi.org/10.1590/0104-530x4537-20

    Article  Google Scholar 

  • Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 1986(32), 73–105.

    Article  Google Scholar 

  • Sharafi, H., Lotfi, F. H., Jahanshahloo, G., Rostamy-malkhalifeh, M., Soltanifar, M., & Razipour-GhalehJough, S. (2019). Ranking of petrochemical companies using preferential voting at unequal levels of voting power through data envelopment analysis. Mathematical Sciences, 13(3), 287–297.

    Article  Google Scholar 

  • Singh, R., Avikal, S., Rashmi, R., & Ram, M. (2020). A Kano model, AHP and TOPSIS based approach for selecting the best mobile phone under a fuzzy environment. International Journal of Quality & Reliability Management, ahead-of-p(ahead-of-print). https://doi.org/10.1108/IJQRM-01-2020-0022

  • Sinuany-Stern, Z., Mehrez, A., & Hadad, Y. (2000). An AHP/DEA methodology for ranking decision making units. International Transactions in Operational Research, 7(2), 109–124.

    Article  Google Scholar 

  • Soltanifar, M. (2017). A new group voting analytical hierarchy process method using preferential voting. Journal of Operational Research and Its Applications, 14(3540016), 1–13.

    Google Scholar 

  • Soltanifar, M. (2020). A new voting model for groups with members of unequal power and proficiency. International Journal of Industrial Mathematics, 12(2), 121–134.

    Google Scholar 

  • Soltanifar, M., Ebrahimnejad, A., & Farrokhi, M. M. (2010). Ranking of different ranking models using a voting model and its application in determining efficient candidates. International Journal of Society Systems Science, 2(4), 375–389.

    Article  Google Scholar 

  • Soltanifar, M., & Lotfi, F. H. (2011). The voting analytic hierarchy process method for discriminating among efficient decision making units in data envelopment analysis. Computers & Industrial Engineering, 60(4), 585–592.

    Article  Google Scholar 

  • Soltanifar, M., & Shahghobadi, S. (2013). Selecting a benevolent secondary goal model in data envelopment analysis cross-efficiency evaluation by a voting model. Socio-Economic Planning Sciences, 47(1), 65–74.

    Article  Google Scholar 

  • Soltanifar, M., & Shahghobadi, S. (2014). Classifying inputs and outputs in data envelopment analysis based on TOPSIS method and a voting model. International Journal of Business Analytics (IJBAN), 1(2), 48–63.

    Article  Google Scholar 

  • Stanujkic, D., Karabasevic, D., & Zavadskas, E. K. (2015). A framework for the selection of a packaging design based on the SWARA method. Engineering Economics, 26(2), 181–187.

    Article  Google Scholar 

  • Tavana, M. (2004). A subjective assessment of alternative mission architectures for the human exploration of mars at NASA using multicriteria decision making. Computers and Operations Research, 31(7), 1147–1164.

    Article  Google Scholar 

  • Tavana, M. (2006). A priority assessment multi-criteria decision model for human spaceflight mission planning at NASA. Journal of the Operational Research Society, 57(10), 1197–1215.

    Article  Google Scholar 

  • Thompson, R G, Langemeiar, L. N., Lee, C. T., & Thrall, R. M. (1989). The measurement of productive efficiency with an application to Kansas Royland wheat farming. Jesse H. Jones Graduate School of Administration Working Paper, 65.

  • Thompson, R. G., Singleton, F. D., Jr., Thrall, R. M., & Smith, B. A. (1986). Comparative site evaluations for locating a high-energy physics lab in Texas. Interfaces, 16(6), 35–49.

    Article  Google Scholar 

  • Triantaphyllou, E. (2001). Two new cases of rank reversals when the AHP and some of its additive variants are used that do not occur with the multiplicative AHP. Journal of Multi-Criteria Decision Analysis, 10(1), 11–25.

    Article  Google Scholar 

  • Tzeng, G.-H., & Huang, J.-J. (2011). Multiple attribute decision making: Methods and applications. CRC Press.

    Book  Google Scholar 

  • Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1–29.

    Article  Google Scholar 

  • Wang, J., & Deng, X. (2020). Comprehensive economic benefit evaluation method of coastal enterprises based on AHP. Journal of Coastal Research, 103, 24–28.

    Article  Google Scholar 

  • Wedley, W. C. (1993). Consistency prediction for incomplete AHP matrices. Mathematical and Computer Modelling, 17(4–5), 151–161.

    Article  Google Scholar 

  • Wei, G., & Liu, J. (2008). A DS/AHP method for comprehensive decision-making in urban power system planning. China International Conference on Electricity Distribution, 2008, 1–5. https://doi.org/10.1109/CICED.2008.5211715

    Article  Google Scholar 

  • Zahir, S. (1999). Clusters in a group: Decision making in the vector space formulation of the analytic hierarchy process. European Journal of Operational Research, 112(3), 620–634.

    Article  Google Scholar 

  • Zand, A., Arfaee, M., & Eslami, K. (2020). Studying effects of consumed fertilizer on sustainable rural development by using AHP method. Agricultural Marketing and Commercialization Journal, 4(1), 1–12.

    Google Scholar 

  • Zandebasiri, M., & Pourhashemi, M. (2016). The place of AHP method among multi criteria decision making methods in forest management. International Journal of Applied Operational Research-an Open Access Journal, 6(2), 75–89.

    Google Scholar 

  • Zhu, G.-N., Hu, J., & Ren, H. (2020). A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments. Applied Soft Computing, 91, 106228. https://doi.org/10.1016/j.asoc.2020.106228

    Article  Google Scholar 

  • Zolfani, S. H., Salimi, J., Maknoon, R., & Kildiene, S. (2015). Technology foresight about R&D projects selection; application of SWARA method at the policy making level. Engineering Economics, 26(5), 571–580.

    Google Scholar 

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Acknowledgment

Dr. Madjid Tavana is grateful for the partial support he received from the Czech Science Foundation (GAˇCR19-13946S) for this research.

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Tavana, M., Soltanifar, M. & Santos-Arteaga, F.J. Analytical hierarchy process: revolution and evolution. Ann Oper Res 326, 879–907 (2023). https://doi.org/10.1007/s10479-021-04432-2

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