Academia.eduAcademia.edu

Figure 3 – uploaded by Mehdi Keshavarz-Ghorabaee

See full PDFdownloadDownload figure
The result of ABC classification with different methods.

Table 3 The result of ABC classification with different methods.

Related Figures (9)

Fig. 1. PDA and NDA values in a simple situation. Step 5: Determine the weighted sum of PDA and NDA for all alternatives, shown as fol-
Fig. 1. PDA and NDA values in a simple situation. Step 5: Determine the weighted sum of PDA and NDA for all alternatives, shown as fol-
Data of the inventory classification problem and average solutions.
Data of the inventory classification problem and average solutions.
The results of the proposed method. Table 2
The results of the proposed method. Table 2
The values of S, for comparison between ABC classification methods. Table 4 Table 5
The values of S, for comparison between ABC classification methods. Table 4 Table 5
Data of the MCDM problem for comparative analysis. As can be seen in Table 4, there is good similarity between the proposed method and the other methods. Moreover, all methods that have chosen for comparison use DEA-like mathematical modeling for classifying the items. Using this mathematical modeling tech- nique usually leads to a high computational process. However, we can say that the sim- plicity and lower computational process are the major advantages of the proposed method. As can be seen in Table 4, there is good similarity between the proposed method and
Data of the MCDM problem for comparative analysis. As can be seen in Table 4, there is good similarity between the proposed method and the other methods. Moreover, all methods that have chosen for comparison use DEA-like mathematical modeling for classifying the items. Using this mathematical modeling tech- nique usually leads to a high computational process. However, we can say that the sim- plicity and lower computational process are the major advantages of the proposed method. As can be seen in Table 4, there is good similarity between the proposed method and
Fig. 2. Importance level of criteria in different sets.
Fig. 2. Importance level of criteria in different sets.
Normalized weights of criteria in different sets. Figure 2 represents the importance level of each criterion in each set, and Table 6 shows the normalized weights of criteria in these sets. In each set, one of the criteria has the highest weight. The ranking results that obtained by different methods and weights are shown in Table 7. We use also the Spearman’s correlation coefficient (7; ) for analyzing the correlation between the results of the proposed method and the other methods in different criteria weights. The results of this analysis are shown in Table 8.
Normalized weights of criteria in different sets. Figure 2 represents the importance level of each criterion in each set, and Table 6 shows the normalized weights of criteria in these sets. In each set, one of the criteria has the highest weight. The ranking results that obtained by different methods and weights are shown in Table 7. We use also the Spearman’s correlation coefficient (7; ) for analyzing the correlation between the results of the proposed method and the other methods in different criteria weights. The results of this analysis are shown in Table 8.
The results of ranking with EDAS method and four MCDM methods.
The results of ranking with EDAS method and four MCDM methods.
Values of Spearman’s correlation coefficient (rs) in different sets.
Values of Spearman’s correlation coefficient (rs) in different sets.
Related topics:
Operations ResearchMulti Criteria Decision MakingMulticriteria Decision AnalysisMulticriteria decision makingMulticriteria Decision AidMultiple attributes decision makingMultiple criteria decision making (MCDM)Multicriteria OptimizationMultiple Criteria Decision Making TechniquesEDAS method
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved