In the study, an information technology for adaptive multi-criteria selection of investment projects in urban construction was developed, which ensures the rationality of decision-making processes under conditions of limited data volume. A logarithmic model for identifying the weighting coefficients of criteria was proposed, allowing their significance to be automatically determined based on retrospective data without involving expert evaluations. The mechanism of forming super-criteria that integrate regulatory, customer, and retrospective indicators into a single evaluation system was investigated. The use of the adaptive multi-criteria selection method ensures the stability and robustness of results when working with small data samples.
An architecture of the information technology was developed, which includes three interconnected levels – informational, analytical, and knowledge levels – implementing a full cycle of data-to-knowledge transformation. It was found that the results of analytical computations can be represented as “if – then” production rules that reflect the patterns of successful project implementation. It was established that the developed technology is characterized by adaptability, interpretability, robustness to fragmented data, and the possibility of further model extension. This is achieved because the combination of the logarithmic model and iterative reduction provides increased system sensitivity to changes in input data and reduces the risk of incorrect project classification. At the same time, the use of super-criteria allows maintaining a balance between regulatory requirements and managerial priorities of stakeholders. The improved technology can be integrated into strategic-level decision support systems for automated selection and ranking of investment programs in urban planning, energy, and social infrastructure. The obtained results form the basis for developing intelligent decision support systems in the fields of urban planning, energy, transport, and investment resource management, and also open prospects for integration with modern machine learning tools.
1. Zavadskas, E., Turskis, Z. and Antucheviciene, J. (2015) Selecting a Contractor by Using a Novel Method for Multiple Attribute Analysis: Weighted Aggregated Sum Product Assessment with Grey Values (WASPAS-G). Studies in Informatics and Control, 24, 141-150.
https://doi.org/10.24846/v24i2y201502
2. Mardani, A., Jusoh, A., & Zavadskas, E. K. (2015). Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014. Expert systems with Applications, 42(8), 4126-4148.
https://doi.org/10.1016/j.eswa.2015.01.003
3. Yang, C.-C., Ou, S.-L., & Hsu, L.-C. (2019). A Hybrid Multi- Criteria Decision-Making Model for Evaluating Companies' Green Credit Rating. Sustainability, 11(6), 1506.
https://doi.org/10.3390/su11061506
4. Sierra, L. A., Yepes, V., & Pellicer, E. (2018). A review of multi-criteria assessment of the social sustainability of infrastructures. Journal of Cleaner Production, 187, 496-513.
https://doi.org/10.1016/j.jclepro.2018.03.022
5. Greco, S., Ehrgott, M., & Figueira, J. R. (Eds.). (2016). Multiple Criteria Decision Analysis: State of the Art Surveys (2nd ed.). New York: Springer.
https://doi.org/10.1007/978-1-4939-3094-4
6. Slama, H. B., Gaha, R., Tlija, M., Chatti, S., & Benamara, A. (2023). Proposal of a Combined AHP-PROMETHEE Decision Support Tool for Selecting Sustainable Machining Process Based on Toolpath Strategy and Manufacturing Parameters. Sustainability, 15(24), 16861.
https://doi.org/10.3390/su152416861
7. Villalba Nieto, P. X., Sбnchez-Garrido, A. J., & Yepes Piqueras, V. (2024). A review of multi-criteria decision-making methods for building assessment, selection, and retrofit.
https://doi.org/10.3846/jcem.2024.21621
8. Salimian, S., Mousavi, S. M., Tupenaite, L., & Antucheviciene, J. (2023). An Integrated Multi-Criteria Decision Model to Select Sustainable Construction Projects under Intuitionistic Fuzzy Conditions. Buildings, 13(4), 848.
https://doi.org/10.3390/buildings13040848
9. Yu, G., Zeng, S., & Zhang, C. (2020). Single-Valued Neutrosophic Linguistic-Induced Aggregation Distance Measures and Their Application in Investment Multiple Attribute Group Decision Making. Symmetry, 12(2), 207.
https://doi.org/10.3390/sym12020207
10. Chen, J. (2014). GIS-based multi-criteria analysis for land use suitability assessment in City of Regina. Environ. Syst. Res., 3, 13.
https://doi.org/10.1186/2193-2697-3-13
11. Esfandi, S., Tayebi, S., Byrne, J., Taminiau, J., Giyahchi, G., & Alavi, S. A. (2024). Smart Cities and Urban Energy Planning: An Advanced Review of Promises and Challenges. Smart Cities, 7(1), 414-444.
https://doi.org/10.3390/smartcities7010016
12. Mulesa, O. (2025). An Adaptive Selection of Urban Construction Projects: A Multi-Stage Model with Iterative Supercriterion Reduction. Urban Science, 9(5), 146.
https://doi.org/10.3390/urbansci9050146