Multicriteria Model of Support for the Selection of Pear Varieties in Raising Orchards in the Semberija Region (Bosnia and Herzegovina)

Round 1

Reviewer 1 Report

This paper intents to study "Multi-criteria model of support for the selection of pear variety in raising orchard in the region Semberija (Bosnia and Herze-govina)".The topic seems interesting, but there are some points that should be addressed.
1.The authors have reviewed the literature in separate sections approximately well; however, their conclusions are separated and not coherent for the reader.
2.Please discuss the research gap of your topic to demonstrate the motivation of your study.
3.In section 5 the result for each Step of the fuzzy MABAC method should be provided.
4. The paper is related to decision research under fuzzy environment. I would like to read more discussions about your future studies, the author can extend the proposed method to Pythagorean fuzzy set, the following related paper can be mentioned:  Algorithm for Multiple Attribute Decision-Making with Interactive Archimedean Norm Operations Under Pythagorean Fuzzy Uncertainty.Int. J. Comput. Intell. Syst.
5.Comparative analysis needs to be stronger. In this paper, author does not make a comparative analysis of the proposed method. I hope author can add some comparisons to prove the rationality and effectiveness of the proposed method. 
6.The format of the references should be consistent. Some of the journal names in the references are abbreviations, and some are full names, such as [1,2].

Author Response

Review response

This paper intents to study "Multi-criteria model of support for the selection of pear variety in raising orchard in the region Semberija (Bosnia and Herze-govina)".The topic seems interesting, but there are some points that should be addressed.

All changes to the paper are marked in red.

1.The authors have reviewed the literature in separate sections approximately well; however, their conclusions are separated and not coherent for the reader.

The literature review has been corrected to be coherent.

2.Please discuss the research gap of your topic to demonstrate the motivation of your study.

Conclusions on constraint research are listed in the conclusion.

3.In section 5 the result for each Step of the fuzzy MABAC method should be provided.

The steps of the MABAC method are presented in the tables.

4. The paper is related to decision research under fuzzy environment. I would like to read more discussions about your future studies, the author can extend the proposed method to Pythagorean fuzzy set, the following related paper can be mentioned:  Algorithm for Multiple Attribute Decision-Making with Interactive Archimedean Norm Operations Under Pythagorean Fuzzy Uncertainty.Int. J. Comput. Intell. Syst.

This is stated in the conclusion of the guideline for future research. A reference is given in the paper.

5.Comparative analysis needs to be stronger. In this paper, author does not make a comparative analysis of the proposed method. I hope author can add some comparisons to prove the rationality and effectiveness of the proposed method. 

A comparative method with other fuzzy methods has been added.

6.The format of the references should be consistent. Some of the journal names in the references are abbreviations, and some are full names, such as [1,2].

References have been corrected.

Author Response File: Author Response.pdf

Reviewer 2 Report

The authors raise an essential topic of knowledge-based decision-making using multi-criteria analysis methods. This paper aims to select the pear variety that could give the best results to growers.

The article is a case study for the previously developed MABAC method. In this method, values ​​determined for alternatives are subtracted from the geometric mean and then summed up ("the distance of the alternative from the approximate boundary domain"). It would be valuable to justify why the authors have chosen this particular method and its advantages over other MCDA methods. The authors developed the MABAC method using fuzzy numbers. However, using fuzzy logic and linguistic operators in multi-criteria decision support is not new.

The structure of the article is logical. The authors made the analyzes well and legibly described, and only some elements require clarification (please see the notes below).

Literature review

The authors reasoned that fuzzy logic is represented in all segments of fruit production. It would be valuable to refer to the MABAC method and indicate what benefits the authors propose by using it. In the introduction, the authors wrote: "The contribution of this research is to develop a new model of decision making by using a new methodology that facilitates decision-making on variety selection."

The authors wrote: "The MABAC method is one of them. Since the method was formed in 2015, according to data from google scholar, this method is mentioned in 3690 papers, which makes it one of the most used new methods. Therefore, this paper uses fuzzy MABAC method." It seems that this is not a sufficient justification.

detailed comments:

The authors should place the information about who and when developed the MABAC method when they first mentioned it.

In line: 269, the authors wrote: The results were verified by sensitivity analysis. Please describe a short sentence with basic information about it.

Step 2: Normalization of the initial matrix elements - need more explanation, especially formulas 4 and 5. Why for benefit j∈C and cost J∈ B? It seems unclear how the authors made normalization. Please explain exactly all the markings and indexes used in the formula.

According to Table 5 - please explain what average measure is used.

Author Response

Review response

The authors raise an essential topic of knowledge-based decision-making using multi-criteria analysis methods. This paper aims to select the pear variety that could give the best results to growers.

All corrections in the paper are marked in red.

The article is a case study for the previously developed MABAC method. In this method, values ​​determined for alternatives are subtracted from the geometric mean and then summed up ("the distance of the alternative from the approximate boundary domain"). It would be valuable to justify why the authors have chosen this particular method and its advantages over other MCDA methods. The authors developed the MABAC method using fuzzy numbers. However, using fuzzy logic and linguistic operators in multi-criteria decision support is not new.

The advantages of the MABAC method are further explained.

The structure of the article is logical. The authors made the analyzes well and legibly described, and only some elements require clarification (please see the notes below).

Literature review

The authors reasoned that fuzzy logic is represented in all segments of fruit production. It would be valuable to refer to the MABAC method and indicate what benefits the authors propose by using it. In the introduction, the authors wrote: "The contribution of this research is to develop a new model of decision making by using a new methodology that facilitates decision-making on variety selection."

This paper offers a new approach to decision making when there are a lot of criteria.

The authors wrote: "The MABAC method is one of them. Since the method was formed in 2015, according to data from google scholar, this method is mentioned in 3690 papers, which makes it one of the most used new methods. Therefore, this paper uses fuzzy MABAC method." It seems that this is not a sufficient justification.

The application of the MABAC method is explained and references to the application of the MABAC method in agriculture are added.

detailed comments:

The authors should place the information about who and when developed the MABAC method when they first mentioned it.

In the paper, the explanation of the method mentioned who developed the method and when.

In line: 269, the authors wrote: The results were verified by sensitivity analysis. Please describe a short sentence with basic information about it.

The sensitivity analysis applied is briefly explained.

Step 2: Normalization of the initial matrix elements - need more explanation, especially formulas 4 and 5. Why for benefit j∈C and cost J∈ B? It seems unclear how the authors made normalization. Please explain exactly all the markings and indexes used in the formula.

It is explained when which normalization is used.

According to Table 5 - please explain what average measure is used.

It is explained why the weight average is used.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The paper has been well revised.