A New Multi-Criteria Approach for Sustainable Material Selection Problem

Round 1

Reviewer 1 Report

In this research, the authors observed that the solutions proposed for RR, in the context of sustainable material selection, are insufficient. The authors present a new material selection approach based on the TOPSIS method, which is free of rank reversal. The authors also demonstrate the causes of rank reversal in the TOPSIS method, how the R-TOPSIS method was designed to solve them, and how it can be applied to sustainable material selection. The problem RR in the TOPSIS method is nothing new. Please improve the manuscript on the following points:

- why in the TOPSIS does the normalization method use vector normalization?

 - why did the authors not do a proper literature review? There is a lack of many methods which are free of RR, e.g., COMET, DARIA-TOPSIS, SPOTS, SIMUS, and so on

- the similarity of rankings should be checked by using rw and WS coefficients, more info you can find in 'A new coefficient of rankings similarity in decision-making problems

- there are also used hybrid methods like in 'A new approach to eliminate rank reversal in the media problems'

- in my opinion, work is valuable but must be improved mainly in the literature review in the area of the MCDA method and RR; the experiment should use similarity coefficients.

- the conclusion should show more future research directions

- references should be extended also with new literature from this area.

 

 

Author Response

Reviewer:

 

1. Why in the TOPSIS does the normalization method use vector normalization?

 

R:

In general, it is a method that has the advantage of converting all attributes into dimensionless measurement unit, thus making inter-attribute comparison easier (Chakraborty and Yeh, 2009). Several studies have also shown that this type of normalization is the best for TOPSIS - see, for example, Chakraborty and Yeh (2009), Celen (2014), and Vafaei et al. (2018).

However, none of these studies took the Rank Reversal Problem (RRP) into account. The normalization vector is demonstrably inconsistent concerning the RRP, which is why R-TOPSIS, the method used in the article, proposes a modification of the classic TOPSIS normalization method (explanation is given on lines 319-322).

 

Chakraborty, S.; Yeh, C-H. A simulation comparison of normalization procedures for TOPSIS. Computing Industrial Engineering 2009, 5, 1815–1820.

Celen, A. Comparative analysis of normalization procedures in TOPSIS method: with an application to Turkish deposit banking market. Informatica 2014, 25, 185-208.

Vafaei, N.; Ribeiro, R.A.; Camarinha-Matos, L. M. Data normalisation techniques in decision making: case study with TOPSIS method. International journal of information and decision sciences 2018, 10, 19-38.

 

 

2. Why did the authors not do a proper literature review? There is a lack of many methods which are free of RR, e.g., COMET, DARIA-TOPSIS, SPOTS, SIMUS, and so on

 

R: Thank you for the suggestion. We conducted a new literature review and have included articles on the methods indicated.

 

 

3. The similarity of rankings should be checked by using rw and WS coefficients, more info you can find in 'A new coefficient of rankings similarity in decision-making problems

 

R:

Thank you for the suggestion. We checked the similarity between the rankings with the two suggested coefficients (Rw and Ws) and added them to the article.

 

 

4. There are also used hybrid methods like in 'A new approach to eliminate rank reversal in the media problems'

 

R:

Thank you for the suggestion. We added the article suggested in our literature review.

 

 

5. In my opinion, work is valuable but must be improved mainly in the literature review in the area of the MCDA method and RR; the experiment should use similarity coefficients.

 

R:

Thank you for the suggestion. We carry out all the suggested points, according to specific answers previously placed.

 

 

6. The conclusion should show more future research directions

 

R:

Thank you for pointing out this problem. We added more suggestions for future studies.

 

 

7. References should be extended also with new literature from this area

 

R:

Thank you for the suggestion.  Due the new literature review, we have also extended the references.

Reviewer 2 Report

This article needs important modifications to be suitable for this journal. I suggest major revision for this paper. The main comments are:

- The Background should be extended to more recently published works available in the literature. R-TOPSIS approach is good, however, the authors could look into other integrated methods such as: MCDM with Fuzzy logic etc and consider them to boost their literature review

-The research gap is unclear. A comprehensive table should be presented by the authors to show the literature review based on their assumptions, methods, and results.

- The presentation of the results and conclusions were not enough; it should be highlighted.

- The authors should elaborate more on the practical implications of their study, as well as the limitations of the study, and further research opportunities.

- The non-relevant references should be removed from the paper.

 

Author Response

Reviewer:

 

1. The Background should be extended to more recently published works available in the literature. R-TOPSIS approach is good, however, the authors could look into other integrated methods such as: MCDM with Fuzzy logic etc and consider them to boost their literature review

 

R:

Thank you for the suggestion. As another reviewer also suggested this point, we conducted a new literature review and have included articles on methods indicated by him.

 

 

2. The research gap is unclear. A comprehensive table should be presented by the authors to show the literature review based on their assumptions, methods, and results.

 

R:

As described in the introduction (lines 55-86), the gap is related to the limitations that need to be investigated in future Sustainable material selection (SMS) research, including (i) not presenting a solution for the RRP; (ii) limiting assessment of rank reversal cases to the addition/removal of alternatives; (iii) presenting new difficult-to-operationalize methods for practical applications. Therefore, our study aimed to present a new approach for SMS using MCDM concepts that overcome the aforementioned limitations. Also, as stated in the previous answer, we have extended the literature review.

 

 

3. The presentation of the results and conclusions were not enough; it should be highlighted.

 

R:

Thank you for the suggestion. In the results, we added two new coefficients of similarity between the rankings (Rw and Ws) and elaborated further on the practical implications. In the conclusion, we improved the research limitations and opportunities.

 

 

4. The authors should elaborate more on the practical implications of their study, as well as the limitations of the study, and further research opportunities.

 

R:

Thank you for the suggestion. As stated in the previous answer, we improved these points.

 

 

5. The non-relevant references should be removed from the paper.

 

R:

Thank you for this reminder. We conducted an overall revision on this point, as suggested.

Reviewer 3 Report

 

The manuscript is well articulated. It identified the causes resulting to the common rank reversal problem while applying MCDM methods. To overcome such problem, authors proposed a solution to the rank reversal problem for the most applicable method (TOPSIS). It will be useful for sustainable material selection problems. This article is recommended for publication with the following minor modifications:

1.     The abbreviations should be defined at the first place of its use in the article. Please check and do the needful.

 

2.    In Section 3.2, Eqs. (11) & (12), PIS and NIS are kept fixed. This change in the previous method can be mentioned and explained in the text for more clarity.

3.  Authors can view the article “Materials selection method using improved TOPSIS without rank reversal based on linear max-min normalization with absolute maximum and minimum values” (doi: 10.1088/2053-1591/ac2d6b).

4.   As per the Turnitin software, the similarity index is 22% at 10 words check which can be improved.

 

Author Response

Reviewer:

 

1. The manuscript is well articulated. It identified the causes resulting to the common rank reversal problem while applying MCDM methods. To overcome such problem, authors proposed a solution to the rank reversal problem for the most applicable method (TOPSIS). It will be useful for sustainable material selection problems.

 

R:

We would like to thank the Reviewer for the positive feedback.

 

 

2. The abbreviations should be defined at the first place of its use in the article. Please check and do the needful.

 

R:

Thank you for this reminder. We conducted an overall revision.

 

 

3. In Section 3.2, Eqs. (11) & (12), PIS and NIS are kept fixed. This change in the previous method can be mentioned and explained in the text for more clarity.

 

R:

Thank you for the suggestion. We clarify that the fact is due to the use of the domain, which is explained in lines 317 and 318.

 

 

4. Authors can view the article “Materials selection method using improved TOPSIS without rank reversal based on linear max-min normalization with absolute maximum and minimum values” (doi: 10.1088/2053-1591/ac2d6b).

 

R:

Thank you for the suggestion. We added the article to our literature review.

 

 

5. As per the Turnitin software, the similarity index is 22% at 10 words check which can be improved.

 

R:

Thank you for pointing out this problem. We conducted a series of analyses in relation to our manuscript using the full version of "Plagius - Plagiarism Detector" software. We understand your point of view and to the best of our ability have made changes to the article.

Round 2

Reviewer 1 Report

The paper has been improved and can be accepted

Reviewer 2 Report

 

I suggest to accept this paper in current form