Double Decker Decision Framework with Fuzzy Data for Multi-Attribute Decision-Making

Kattur Soundarapandian Ravichandran

doi:10.31181/msa2120259

Authors

Kattur Soundarapandian Ravichandran Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore, India Author https://orcid.org/0000-0003-2397-461X

DOI:

https://doi.org/10.31181/msa2120259

Keywords:

Simple rank procedure, Fuzzy sets, Project selection, Rank fusion

Abstract

This paper presents a new framework for decision-making with fuzzy data. Specifically, in a double-decker setup, the first deck contains methods that perform rank value calculation of alternatives. The second deck fuses the rank order to provide a holistic ordering of alternatives. The first deck is highly scalable, as it can accommodate multiple approaches for ranking, and the final ordering is obtained by sending the rank orders from the first deck to the second—where the simple rank procedure is utilized. Earlier frameworks cannot perform rank fusion or use techniques like averaging and the Borda count without considering the personal choice of alternatives. This framework circumvents this challenge, and its usefulness is demonstrated through numerical examples.

Downloads

Download data is not yet available.

References

Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and economic development of economy, 20(1), 165-179. https://doi.org/10.3846/20294913.2014.892037

Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X

Jato-Espino, D., Castillo-Lopez, E., Rodriguez-Hernandez, J., & Canteras-Jordana, J. C. (2014). A review of application of multi-criteria decision making methods in construction. Automation in construction, 45, 151-162. https://doi.org/10.1016/j.autcon.2014.05.013

Gavade, R. K. (2014). Multi-Criteria Decision Making: An overview of different selection problems and methods. International Journal of Computer Science and Information Technologies, 5(4), 5643-5646.

Basílio, M. P., Pereira, V., Costa, H. G., Santos, M., & Ghosh, A. (2022). A systematic review of the applications of multi-criteria decision aid methods (1977–2022). Electronics, 11(11), 1720. https://doi.org/10.3390/electronics11111720

Chakraborty, S., & Zavadskas, E. K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica, 25(1), 1-20. https://doi.org/10.15388/Informatica.2014.01

Eghbali-Zarch, M., Tavakkoli-Moghaddam, R., Dehghan-Sanej, K., & Kaboli, A. (2022). Prioritizing the effective strategies for construction and demolition waste management using fuzzy IDOCRIW and WASPAS methods. Engineering, Construction and Architectural Management, 29(3), 1109-1138. https://doi.org/10.1108/ECAM-08-2020-0617

Radomska-Zalas, A. (2023). Application of the WASPAS method in a selected technological process. Procedia Computer Science, 225, 177-187. https://doi.org/10.1016/j.procs.2023.10.002

de Assis, G. S., dos Santos, M., & Basilio, M. P. (2023). Use of the WASPAS method to select suitable helicopters for aerial activity carried out by the military police of the state of Rio de Janeiro. Axioms, 12(1), 77. https://doi.org/10.3390/axioms12010077

Khan, A. A., Mashat, D. S., & Dong, K. (2024). Evaluating sustainable urban development strategies through spherical CRITIC-WASPAS analysis. Journal of Urban Development and Management, 3(1), 1-17. https://doi.org/10.56578/judm030101

Menekşe, A., & Akdağ, H. C. (2023). Medical waste disposal planning for healthcare units using spherical fuzzy CRITIC-WASPAS. Applied Soft Computing, 144, 110480. https://doi.org/10.1016/j.asoc.2023.110480

Görçün, Ö. F., Pamucar, D., & Küçükönder, H. (2024). Selection of tramcars for sustainable urban transportation by using the modified WASPAS approach based on Heronian operators. Applied Soft Computing, 151, 111127. https://doi.org/10.1016/j.asoc.2023.111127

Arslan, Ö., & Cebi, S. (2024). A novel approach for multi-criteria decision making: extending the WASPAS method using decomposed fuzzy sets. Computers & Industrial Engineering, 196, 110461. https://doi.org/10.1016/j.cie.2024.110461

Zavadskas, E. K., Kaklauskas, A., Peldschus, F., & Turskis, Z. (2007). Multi-attribute assessment of road design solutions by using the COPRAS method. The Baltic journal of Road and Bridge engineering, 2(4), 195-203.

Bathrinath, S., Venkadesh, S., Suprriyan, S. S., Koppiahraj, K., & Bhalaji, R. K. A. (2022). A fuzzy COPRAS approach for analysing the factors affecting sustainability in ship ports. Materials Today: Proceedings, 50, 1017-1021. https://doi.org/10.1016/j.matpr.2021.07.350

Krishna, M., Kumar, S. D., Ezilarasan, C., Sudarsan, P. V., Anandan, V., Palani, S., & Jayaseelan, V. (2022). Application of MOORA & COPRAS integrated with entropy method for multi-criteria decision making in dry turning process of Nimonic C263. Manufacturing Review, 9, 20. https://doi.org/10.1051/mfreview/2022014

Mohata, A., Mukhopadhyay, N., & Kumar, V. (2023). CRITIC-COPRAS-based selection of commercially viable alternative fuel passenger vehicle. In Advances in Modelling and Optimization of Manufacturing and Industrial Systems: Select Proceedings of CIMS 2021 (pp. 51-69). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-6107-6_5

Opoku-Mensah, E., Yin, Y., Asiedu-Ayeh, L. O., Asante, D., Tuffour, P., & Ampofo, S. A. (2023). Exploring governments' role in mergers and acquisitions using IVIF MULTIMOORA-COPRAS technique. International Journal of Emerging Markets, 18(4), 908-930. https://doi.org/10.1108/IJOEM-11-2020-1405

Raja, C., Chinnasami Sivaji, M. R., & Sharma, R. (2023). Evaluating Food Order Industry Performance Using the COPRAS Method: A Multi-Criteria Decision-Making Approach. Intelligence, 2, 4. https://doi.org/10.46632/jdaai/2/4/4

Punetha, N., & Jain, G. (2024). Integrated Shannon entropy and COPRAS optimal model-based recommendation framework. Evolutionary Intelligence, 17(1), 385-397. https://doi.org/10.1007/s12065-023-00886-4