Selection of Suitable Data Normalization Method to Combine with the CRADIS Method for Making Multi-Criteria Decision

Hà, Lê Dang

doi:10.18485/aeletters.2023.8.1.4

Cited by 11 publications

(7 citation statements)

References 30 publications

(49 reference statements)

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“…to energy, environment, sustainability, supplier selection, etc. [55], which confirms the justification of using this method in the selection of eco-friendly suppliers. This method was created based on the elements of three different multi-criteria analyses, namely ARAS, MARCOS, and TOPSIS [54].…”

Section: Fuzzy-rough Cradis Methodssupporting

confidence: 65%

“…The CRADIS method has so far been successfully used in solving to energy, environment, sustainability, supplier selection, etc. [55], w justification of using this method in the selection of eco-friendly supp was created based on the elements of three different multi-criteria ARAS, MARCOS, and TOPSIS [54]. There is something from each of t was incorporated into this method.…”

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confidence: 99%

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Selection of a Green Contractor for the Implementation of a Solar Power Plant Project

Stojanović

2024

Symmetry

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This study is focused on the problem of contractor selection for the implementation of a solar power plant project to produce electricity from sustainable sources for the needs of the company Voćar. The goal of this research is to select a construction contractor to install a solar power plant using sustainability criteria. With this power plant, the company Voćar can reduce its electricity costs and contribute to the production of sustainable energy. A total of three main sustainability criteria were used, in which six auxiliary criteria were symmetrically distributed. With these criteria, six suppliers were analyzed, and expert decision making was carried out with the application of the fuzzy–rough approach. To define the weights of the criteria, the SWARA method was utilized in this study. Based on the findings of this method, the most important criteria are the ecological criteria. Using the CRADIS method, the contractors were ranked, and the results show that contractor C6 has the best results and is the first choice for implementing this project. Choosing the best supplier increases the sustainability of project implementation and the realization of the expected effects.

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Section: Fuzzy-rough Cradis Methodssupporting

confidence: 65%

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confidence: 99%

Selection of a Green Contractor for the Implementation of a Solar Power Plant Project

Stojanović

2024

Symmetry

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“…In the case of weighting criteria using the LOPCOW method, both the RAM and PIV methods indicate that LO3 is the best option. These differences are understandable, as many studies have shown that rankings of options depend heavily on the method used for weighting and the MCDM method employed [3][4][5]. To determine which MCDM method is more suitable than the other two, it is necessary to test the stability of ranking options.…”

Section: Fig 2 Ranking Of Lubricant Typesmentioning

confidence: 99%

“…Two critical aspects in multi-criteria decision-making are the selection of methods to determine criterion weights and the choice of MCDM methods to be employed. These choices significantly impact the ranking of alternatives [3][4][5]. Weight determination methods are categorized into three groups: subjective methods, objective methods, and a combination of both, known as combined methods [6].…”

Section: Introductionmentioning

confidence: 99%

Integration of objective weighting methods for criteria and MCDM methods: application in material selection

Dua,

Duc,

Bao

et al. 2024

Eureka: PE

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Determining weights for criteria is an extremely crucial step in the process of selecting an option based on multiple criteria, also known as Multi-Criteria Decision Making (MCDM). This article presents the combination of five objective weighting methods for criteria with three MCDM methods in the context of material selection. The five objective weighting methods considered are Entropy, MEREC (Method based on the Removal Effects of Criteria), LOPCOW (Logarithmic Percentage Change-driven Objective Weighting), CRITIC (Criteria Importance Through Intercriteria Correlation), and MEAN. The three MCDM methods employed are MARA (Magnitude of the Area for the Ranking of Alternatives), RAM (Root Assessment Method), and PIV (Proximity Indexed Value). Material selection investigations were conducted in three different cases, including lubricant selection for two-stroke engines, material selection for manufacturing screw shafts, and material selection for manufacturing gears. The Spearman's rank correlation coefficient was calculated to assess the stability of ranking the alternatives using different MCDM methods. The combinations of objective weighting methods and MCDM methods were evaluated based on factors such as consistency in identifying the best material type, range, average value, and median of each set of Spearman's rank correlation coefficients. Two significant findings were identified. First, the weights of criteria calculated using LOPCOW method appear to be inversely related to those calculated using the Entropy method. Second, among the three MCDM methods used, MARA was identified as the most suiTable for lubricant selection for two-stroke engines, RAM was found to be the most suiTable for material selection for screw shafts and gears. The best material type in each case was also determined

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“…Data normalization is the process of bringing parameters with different units to a common dimensionless form [1,2]. This is one of the stages in multi-criteria decision-making processes [3].…”

Section: Introductionmentioning

confidence: 99%

Research on expanding the scope of application of the MARA method

Thinh,

Dua

2024

Eureka: PE

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Data normalization is a crucial step in multi-criteria decision-making (MCDM) processes. The choice of data normalization method significantly influences the ranking of alternatives. The available data normalization methods in the MARA (Magnitude of the Area for the Ranking of Alternatives) method may not be applicable in certain cases. This study aims to broaden the application scope of the MARA method. Therefore, an investigation into the compatibility of data normalization methods when combined with the MARA method has been conducted. Ten data normalization methods were utilized, including Linear normalization, Weitendorf normalization, Sum linear normalization, Vector normalization, Logarithmic normalization, Max linear normalization, Min linear normalization, Jüttler-Körth normalization, Peldschus normalization, and Stop normalization. The compatibility between the MARA method and these ten data normalization methods was tested in five different scenarios. In the initial four scenarios, variations in the number of alternatives, criteria, and criterion types were introduced. Eight out of the ten data normalization methods were proven to be suitable for integration with the MARA method. In the fifth scenario, a hypothetical situation was presented where the data normalization methods available in the MARA method could not be used. Alternative data normalization methods were employed, and their combination with the MARA method was compared to using other multi-criteria decision-making methods. The results affirmed the accuracy of these combinations. This exploration has expanded the application scope of the MARA method compared to its original version

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Selection of Suitable Data Normalization Method to Combine with the CRADIS Method for Making Multi-Criteria Decision

Cited by 11 publications

References 30 publications

Selection of a Green Contractor for the Implementation of a Solar Power Plant Project

Selection of a Green Contractor for the Implementation of a Solar Power Plant Project

Integration of objective weighting methods for criteria and MCDM methods: application in material selection

Research on expanding the scope of application of the MARA method

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