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Berna Uzun, Universidad Carlos III de Madrid

28-04-2022

Fuzzy based Analytical Multi Criteria Decision Analysis Techniques and Its Applications

 

Abstract:


The Multi-criteria Decision-making method is evidently monumental in research -based studies to evaluate products. Overtime, with the sophistication in technology and tools, the MCDM has proven to be efficient in the analysis and evaluation of ideas, products, materials and decisions. This method of decision analysis has conceptualized several approaches that rationally make decisions, based- on the derived criteria and alternatives, to tremendously distinguish them from one another [1].
With the introduction of fuzzy in the theoretical fusion with MCDM, it has recorded overwhelmingly in evaluation of alternatives when compared with other ranking methods. Therefore, the applications of this method widely cross across different learning domains. Some of the pioneer applications of the fuzzy-based MCDM was in the commercial “evaluation of credit card worthiness” of some applicants conducted in Germany. Thus, over the years, the fuzzy-based MCDM became more popular and applicable in several fields with different peculiar problems [2].
Similarly, this method of evaluation was utilized in biotechnology management as reported by Chen and Chang studies, as one of the earliest applications of the fuzzy-based MCDM techniques. Additionally, they applied this method to select the preferred alternatives in biotechnology management. In the assessment of these variables, the criteria, weight and importance weights of criteria are carefully analyzed as part of the fuzzy-based methods. In addition, the linguistic variable is also applied in aggregating the decision makers assessment of the weighting of criteria. Furthermore, to select a preferred alternative, the fuzzy-based MCDM was applied in the early classification method to purchase better products such as cars, houses and home electronic appliances [3].
In recent studies, the fuzzy-based MCDM has proven to stand the test of time through its practical applications in solving revolutionary studies in technology, medicine, engineering, social and applied sciences. Study by Abdullah and a colleague Jamal, was conducted to evaluate the health-related problems among aged-persons, applying the linguistic judgment [4]. Similarly, in civil engineering, the fuzzy MCDM technique was applied by Wen and colleagues in 2021, based on a study survey, to classify articles based-on their research aims and methodology and the criteria [5]. Alternatively, fuzzy-based MCDM was recently applied in mobile healthcare studies to evaluate the efficiency post-covid-19 pandemic era. Chen and Wang 2021 conducted the study, with the methodology to evaluate and compare technology applications in healthcare delivery in the post-COVID era [6].  Environmental-based global issues are also evaluated with the aid of the fuzzy computational analysis; such as in the study by Sadiq and Hussain on the “fuzzy-based methods applied in the risk assessment of the environment” [7]. In Machine Learning (ML), the fuzzy-based MCDM is applied as revealed in a study carried out by Sahin et al. The authors applied the computational approach in purchasing dry bulk carriers. They presented a set of outline criteria and alternatives and evaluated them based on questionnaires distributed to the public [8].
Uzun Ozsahin et al. adopted fuzzy-based Multi-Criteria Decision-Making technique in the selection and evaluation of alternatives in a study on Environmental and Civil Engineering. Due to the increasing global exposure to emitted gases, environmental approaches are devised to improve their effects on the globe; therefore, the fuzzy-based MCDM has assisted in the evaluation, comparatively [9].
The fuzzy-based Multi-criteria Decision-making technique is applied in the medical field to select, assess and evaluate major clinical decisions. Undoubtedly, the Medical and Biomedical field is one of the significant fields to apply the fuzzy-based MCDM. The basic application of this method in Healthcare and Biomedical Engineering was carefully expatiated in a study by Ozsahin et al (2021). In their research, I. Ozsahin and the colleagues, followed both the theoretical and practical applications of the fuzzy-based MCDM in both selecting and evaluation of medical devices, epidemiology of diseases and in curative measures of several diseases. Advance treatments using sophisticated medical tools were comparatively distinguished, getting the preferred alternatives suitable in curing for several diseases [10].
Today, the increase and diversification of technological products and the presence of more than one parameter in the selection have made it difficult to decide, and in such cases, decision support systems provide important support to experts.

References:
1.    Tabatabaee, S., Mahdiyar, A., & Ismail, S. (2021). Towards the success of Building Information Modelling implementation: A fuzzy-based MCDM risk assessment tool. Journal of Building Engineering, 43, 103117.
2.    Lazim Abdullah (2013), “Fuzzy multi criteria decision making and its applications: A brief review of category” The 9th International conference on cognitive science. Procedia-social and Behavioral sciences 97 (2013) 131-136
3.    Chang, P. L., & Chen, Y. C. (1994). A fuzzy multi-criteria decision-making method for technology transfer strategy selection in biotechnology. Fuzzy Sets and Systems, 63(2), 131-139.
4.    Abdullah, L., & Jamal, N. J. (2011). Determination of weights for health-related quality of life indicators among kidney patients: A fuzzy decision-making method. Applied Research in Quality of Life, 6(4), 349-361.
5.    Wen, Z., Liao, H., Zavadskas, E. K., & Antuchevičienė, J. (2021). Applications of fuzzy multiple criteria decision-making methods in civil engineering: A state-of-the-art survey. Journal of Civil Engineering and Management, 27(6), 358-371.
6.    Chen, T.; Wang, Y.-C. Recommending Suitable Smart Technology Applications to Support Mobile Healthcare after the COVID-19 Pandemic Using a Fuzzy Approach. Healthcare 2021, 9, 1461. https://doi.org/10.3390/healthcare911146
7.    Sadiq, R., & Husain, T. (2005). A fuzzy-based methodology for an aggregative environmental risk assessment: a case study of drilling waste. Environmental Modelling & Software, 20(1), 33-46.
8.    Sahin, B., Yip, T. L., Tseng, P. H., Kabak, M., & Soylu, A. (2020). An application of a fuzzy TOPSIS multi-criteria decision analysis algorithm for dry bulk carrier selection. Information, 11(5), 251.