IJEM Vol. 16, No. 1, 8 Feb. 2026
Cover page and Table of Contents: PDF (size: 492KB)
PDF (492KB), PP.29-38
Views: 0 Downloads: 0
Fuzzy AHP, Z-numbers, Multi-Criteria Decision-Making, Advanced Technology Evaluation
In the modern global economy, sustainability has emerged as a crucial foundation for achieving long-term stability and growth. Escalating environmental challenges, depletion of natural resources, and growing social expectations have made the selection of suitable technologies and innovations essential for sustainable economic progress. Yet, such decisions are often made amid uncertainty driven by technological risks, volatile markets, evolving regulations, and geopolitical instability. These factors complicate decision-making for policymakers, industry leaders, and investors, underscoring the need for resilient analytical frameworks that support informed innovative choices while mitigating risks. Achieving harmony between innovation and sustainability requires balancing economic feasibility, environmental responsibility, and social well-being. This study introduces a holistic framework for evaluating advanced technologies that contribute to economic development under uncertain and complex conditions. Utilizing the fuzzy Analytic Hierarchy Process (AHP) with Z numbers, the approach combines fuzzy logic and Z-numbers to effectively represent uncertainty and the reliability of expert evaluations. The model supports a structured multi-criteria assessment that integrates economic, environmental, and social dimensions, guiding stakeholders in selecting technologies that foster sustainable and adaptable growth. Through conceptual analysis and practical case applications, the research validates the efficiency of the fuzzy Z-AHP approach as a robust, transparent, and flexible decision-making tool for technology evaluation in dynamic economic environments. The outcomes enhance methodological advancement in sustainable development and strategic innovation management.
Kamala Aliyeva, "Evaluation of Cutting-Edge Technologies for Economic Growth through Fuzzy AHP Approach", International Journal of Engineering and Manufacturing (IJEM), Vol.16, No.1, pp. 29-38, 2026. DOI:10.5815/ijem.2026.01.03
[1]Chan F., Chan H., Chan M., and Humphreys P. An integrated fuzzy approach for the selection of manufacturing technologies. International Journal of Advanced Manufacturing Technology, 27(7–8), pages 747–758, 2006.
[2]Karsak E. Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives. International Journal of Production Research, 40(13), pages 3167–3181, 2002.
[3]Zadeh, L. Fuzzy Sets. Information Control, 8, pages 338-353, 1965.
[4]Kahraman C., Engin O., Kabak O., and Kaya I. Information systems outsourcing decisions using a group decision-making approach. Engineering Applications of Artificial Intelligence, 22 (6), pages 832-841, 2009.
[5]Zadeh, L.A., A Note on a Z-Number. Journal of Information Sciences, 181, pages 2923-2932, 2011.
[6]Aliyeva, K.R. Eigensolution of 2 by 2 z-matrix. Advances in Intelligent Systems and Computing, 1095 AISC, pages 758–762, 2020.
[7]Aliev, R.A., Huseynov, O.H., and Aliyeva, K.R. Toward eigenvalues and eigenvectors of matrices of Z-numbers. International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, 1095, pages 309-317, 2020.
[8]Aliyeva, K.R. Fuzzy Type-2 Decision Making Method on Project Selection. Advances in Intelligent Systems and Computing, 1323 AISC, pages 180–185, 2021.
[9]Bocken, N., Short, S. Rana, P., and Evans S. A literature and practice review to develop sustainable business model archetypes. Journal of Cleaner Production, 65(15), pages 42-56, 2014.
[10]Walker, W. E., Haasnoot, M., and Kwakkel, J. H. Adapt or Perish: A Review of Planning Approaches for Adaptation under Deep Uncertainty. Sustainability, 955–979, 2013.
[11]Wang Y., and Chin K. A new approach for the selection of advanced manufacturing technologies: DEA with double frontiers. International Journal of Production Research, 47(23), pages 6663–6679, 2009.
[12]Capaldo, G., and Zollo, G. Applying fuzzy logic to personnel assessment: A case study. Omega, 29(6), pages 585-597, 2001.
[13]Aliyeva K.R. Z-numbers based modeling of group decision making for supplier selection in manufacturing systems. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska, 14 (3), pages 61 – 67, 2024.
[14]Aliyeva K.R. Demand forecasting for manufacturing under Z-Information. Procedia Computer Science, 120, pages 509–514, 2017.
[15]Aliev, R.A., Alizadeh A.V., and Huseynov O.H. The arithmetic of discrete Z-numbers. Information of Sciences, 290 (1), pages 134-155, 2015.
[16]Aliev R.A., Guirimov B. G., Huseynov O. H., and Aliyev, R. R. A consistency-driven approach to construction of Z-number-valued pairwise comparison matrices, Journal of Fuzzy Systems, 18 (4), pages 37-49, 2021.
[17]Sharghi, P., Jabbarova, K., and Aliyeva, K. RDM interval arithmetic-based decision making on port selection, Procedia Computer Science, 120, pages 572–579, 2017.
[18]Aliyeva, K., Mehdi, S. Dynamic Fuzzy Multi-Attribute Decision Making by Using Z-Numbers for IT Supplier Selection in Service Systems, Lecture Notes in Networks and Systems, 1472 LNNS, 273–282, 2025.