A Profit Distribution Model of Reverse Logistics Based on Fuzzy DEA Efficiency—Modified Shapley Value
Round 1
Reviewer 1 Report
This paper studies the allocation of profit among three enterprises in the case of reverse logistics. The authors combined the use of DEA, rough set, and modified Shapley value. The topic is interesting. However, the study has some major issues.
- The abstract is not clear: authors are advised to rewrite the abstract as follows: First, give introduce the importance of the topic, followed by explaining what is the gap, then describe your approach to cover this gap and the main findings from the paper.
- The contribution of the study is not clear.
- Along with the article, the language needs careful revision. Furthermore, some passages use long sentences that make reading heavy and difficult
- At line 67, change a) >> A)
- The section (Problem Description and Model Assumptions) needs more explanation, as the current problem description is hard for normal readers to understand.
- Can you give example for the third-party reverse logistics service provider, is it a manufacturing company?
- ‘The sales enterprise is responsible for delivering products to the market and providing the reusable waste generated during the sales process’… this sentence needs clarification of what types of wastes generated during the sale process?
- The authors mentioned that the logistics service provider would remanufacture the waste and deliver it to the manufacturer or the market. In which shape the waste will be delivered to market? As a product or raw materials?. This needs more explanation.
- Figure 2 is not cited in the text
- The term ‘alliance’ is not clear. Does this mean that all enterprises form a strategic collaboration form?
- The collaboration strategies have been widely applied in other fields. The authors are recommended to mention few examples from these fields. For example:
Alliances for urban logistics as https://doi.org/10.1007/978-981-15-5682-1_56
Maritime logistics as in https://doi.org/10.1504/IJLSM.2019.098324 and https://doi.org/10.1007/978-3-319-94767-9_6
- The input and output data for member enterprises are not defined in details in section 4.1.1
- The methodology is not clear. Can the author mention the input/output of each method in figure 2?
- Who leads the alliance, coordinates the collaboration process, and allocates the profits? Is the logistics service provider the leader?
- Are the DEA and rough set the only methods used in the literature? I think it would be better to cite other methods and to argue your choice.
- In line 285, the CCR model is not defined
- What do you mean by decision-making units? Can you give an example using table 1 or other tables
- In the methodology description, the authors mentioned the trapezoidal Fuzzy Numbers while in the result section, triangular fuzzy numbers are used as well. This needs more clarification.
- How did the authors decide on the 11 initial indicators?
- There is a lack of thorough discussion of how the group of experts was selected for the study, what criteria these individuals had to meet,
- What are the practical implications of levels of confidence?
- Please provide details about Rosetta software
- The results lack practical insights and implications for practitioners
Author Response
Response to Reviewer 1 Comments
Dear editor, reviewers:
Thank you for your positive response to our work and the kind advice. We greatly appreciate your constructive comments that have helped us improve our paper. We have endeavoured to incorporate the feedback and revised our manuscript accordingly. The itemized response is as follows:
Point 1: The abstract is not clear: authors are advised to rewrite the abstract as follows: First, give introduce the importance of the topic, followed by explaining what is the gap, then describe your approach to cover this gap and the main findings from the paper.
Response 1: Thank you for your comment. We had re-written this part according to the reviewer’s suggestion.
Point 2: The contribution of the study is not clear.
Response 2: Thanks for your comment. We redefined the contribution of the research and made a detailed description, please see lines 81-88.
Point 3: Along with the article, the language needs careful revision. Furthermore, some passages use long sentences that make reading heavy and difficult.
Response 3: Thanks for your suggestion. We had revised the writing in our paper as advised.
Point 4: At line 67, change a) >> A).
Response 4: We are sorry for our negligence of made such a low-level mistake. We had revised it.
Point 5: The section (Problem Description and Model Assumptions) needs more explanation, as the current problem description is hard for normal readers to understand.
Response 5: Thanks for your suggestion. We had improved and enriched the section 3 (Problem Description and Model Assumptions).
Point 6: Can you give example for the third-party reverse logistics service provider, is it a manufacturing company?
Response 6: Thanks for your comment. The third-party reverse logistics service provider is not a manufacturing company. “This research investigated the distribution of the overall profit of the manufacturing reverse logistics system among member enterprises based on the outsourcing model.” In the original manuscript, the above sentence is prone to ambiguity, we had revised it.
Point 7: ‘The sales enterprise is responsible for delivering products to the market and providing the reusable waste generated during the sales process’… this sentence needs clarification of what types of wastes generated during the sale process?
Response 7: Thanks for your suggestion. We had already modified the writing, please see lines 199-200.
Point 8: The authors mentioned that the logistics service provider would remanufacture the waste and deliver it to the manufacturer or the market. In which shape the waste will be delivered to market? As a product or raw materials? This needs more explanation.
Response 8: Thanks for your suggestion. We had already modified the writing, please see lines 201-203.
Point 9: Figure 2 is not cited in the text
Response 9: Thank you for your comment. After discussion, we found that Figure 2 is not helpful to the structure of the entire paper. Therefore, we decided to delete Figure 2 and describe its main content in text.
Point 10: The term ‘alliance’ is not clear. Does this mean that all enterprises form a strategic collaboration form?
Response 10: Thanks for your comment. We had added content about the alliance, please see lines 213-214.
Point 11: The collaboration strategies have been widely applied in other fields. The authors are recommended to mention few examples from these fields. For example:
Alliances for urban logistics as https://doi.org/10.1007/978-981-15-5682-1_56
Maritime logistics as in https://doi.org/10.1504/IJLSM.2019.098324 and https://doi.org/10.1007/978-3-319-94767-9_6
Response 11: Thanks for your kind advice. We had read above paper and cited it to enrich our paper, please see lines 97-101.
Point 12: The input and output data for member enterprises are not defined in details in section 4.1.1
Response 12: Thanks for your comment. We had added relevant definitions, please see lines 240 and 282-283.
Point 13: The methodology is not clear. Can the author mention the input/output of each method in figure 2?
Response 13: Thank you for your comment. We had deleted Figure 2 and describe its main content in text.
Point 14: Who leads the alliance, coordinates the collaboration process, and allocates the profits? Is the logistics service provider the leader?
Response 14: Thanks for your comment. We had made the corresponding explanation in the model assumption part, please see lines 215-218.
Point 15: Are the DEA and rough set the only methods used in the literature? I think it would be better to cite other methods and to argue your choice.
Response 15: Thanks for your kind advice. We had already modified the writing, please see lines 230-235 and 249-253.
Point 16: In line 285, the CCR model is not defined.
Response 16: Thanks for your comment and kind advice. We had defined the CCR model in lines 235-236.
Point 17: What do you mean by decision-making units? Can you give an example using table 1 or other tables.
Response 17: Thank you for your comment. We introduced the definition of decision-making unit in lines 236-238. For example, in the numerical example, member enterprise A or any member enterprise can be regarded as a decision-making unit.
Point 18: In the methodology description, the authors mentioned the trapezoidal Fuzzy Numbers while in the result section, triangular fuzzy numbers are used as well. This needs more clarification.
Response 18: Thanks for your kind advice. The title of 4.1.3 had changed from "The DEA Model of Trapezoidal Fuzzy Numbers" to "Transformation of Fuzzy DEA Models". In this part, we introduce the fuzzy DEA models of trapezoidal fuzzy numbers, triangular fuzzy numbers, etc.
Point 19: How did the authors decide on the 11 initial indicators?
Response 19: Thanks for your comment. We used the questionnaire survey method to determine the initial indicators, we revised this question in lines 466-467.
Point 20: There is a lack of thorough discussion of how the group of experts was selected for the study, what criteria these individuals had to meet.
Response 20: Thank you for your comment. We had revised the writing in our paper as advised, please see lines 466-467.
Point 21: What are the practical implications of levels of confidence?
Response 21: Thanks for your comment. We had added the actual meaning of the confidence level in lines 554-556.
Point 22: Please provide details about Rosetta software.
Response 22: Thanks for your kind advice. We had added details about Rosetta software, please see lines 479-481.
Point 23: The results lack practical insights and implications for practitioners
Response 23: Thank you for your comment. We had enriched the writing in our paper as advised.
Reviewer 2 Report
The paper concerns the reverse logistics problem proposing a Profit Distribution Model based on DEA approach. The topic is relevant and the paper is interesting.
Despite the relevance of the topic, the current version of the paper should be improved in order to improve its quality and readability. In the following, there are broad and specific comments
A Profit Distribution Model of Reverse Logistics Based on Fuzzy DEA Efficiency-Modified Shapley Value
Song et al 2021
Rev.1 June 2021
The paper concerns the reverse logistics problem proposing a Profit Distribution Model based on DEA approach. The topic is relevant and the paper is interesting.
Despite the relevance of the topic, the current version of the paper should be improved in order to improve its quality and readability. In the following, there are broad and specific comments.
Broad comment
The paper has some problem related to its readability.
The main lacks regard different sections.
Literature review is too limited. Some basic references lack. In my opinion, by considering your studied problem, literature about DEA, multi criteria decision making methods and freight transport have to be recalled. There is a wide literature about freight transport models but it seems that the authors neglect these models.
It is not clear if the studied problem is connected to urban transport planning, ITS and energy resources in the context of a smart city.
Section 3, that should constitute the paper’s core, is poor. The paper’s objective is quite clear but the entire methodology that integrates DEA, fuzzy logic and Shapley value method is not fully clear. In particular: it is clear why you use DEA in order to evaluate efficiency; it is not clear why you need to use fuzzy DEA and Shapley value method. Please, clarify because you need to combine these methodologies. Related to this, I suggest you to clarify which are output of each method that became input for another method.
Section 4 is very extensive. There is a wide explanation of the mathematical approach but these analytical formulations are not fully connected to the problem description and model assumption (section 3)
Specific comments
1) figure 2 is not fully clear. You use rectangular form to represent input, output and procedures. This not aid the potential reader to follow the entire methodology
2) the previous point is related to another problem: it is not clear how analytic formulation presented in section 4 is connected to the entire methodology.
3) the steps illustrated in section 4 are related to the figure 2?
Suggested references
Russo et al. (2010). Data Envelopment Analysis (DEA) for evacuation planning. WIT Transactions on Information and Communication Technologies, 2010, 43 PART I
Musolino et al. (2017) Evaluation in transport planning: A comparison between data envelopment analysis and multi criteria decision making methods. 31st Annual European Simulation and Modelling Conference 2017
Croce et al. (2020). Route and path choices of freight vehicles: A case study with floating car data. Sustainability (Switzerland), 2020, 12(20)
Author Response
Response to Reviewer 2 Comments
Dear editor, reviewers:
Thank you for your positive response to our work and the kind advice. We greatly appreciate your constructive comments that have helped us improve our paper. We have endeavoured to incorporate the feedback and revised our manuscript accordingly. The itemized response is as follows:
Point 1: The paper has some problem related to its readability.
Response 1: Thanks for your comment. We had revised the writing in our paper as advised.
Point 2: Literature review is too limited. Some basic references lack. In my opinion, by considering your studied problem, literature about DEA, multi criteria decision making methods and freight transport have to be recalled. There is a wide literature about freight transport models but it seems that the authors neglect these models.
Response 2: Thanks for your comment and kind advice. We had read relevant papers and cited them to enrich our paper, please see lines 101-102 and 155-156.
Point 3: It is not clear if the studied problem is connected to urban transport planning, ITS and energy resources in the context of a smart city.
Response 3: Thank you for your comment. After our serious discussion, we think that the problems studied in this paper have nothing to do with urban transport planning, ITS and energy resources in the context of a smart city.
Point 4: Section 3, that should constitute the paper’s core, is poor. The paper’s objective is quite clear but the entire methodology that integrates DEA, fuzzy logic and Shapley value method is not fully clear. In particular: it is clear why you use DEA in order to evaluate efficiency; it is not clear why you need to use fuzzy DEA and Shapley value method. Please, clarify because you need to combine these methodologies. Related to this, I suggest you to clarify which are output of each method that became input for another method.
Response 4: Thanks for your comment and kind advice. We had improved and enriched the section 3.
Point 5: Section 4 is very extensive. There is a wide explanation of the mathematical approach but these analytical formulations are not fully connected to the problem description and model assumption (section 3)
Response 5: Thanks for your comment. We had already modified the writing.
Point 6: figure 2 is not fully clear. You use rectangular form to represent input, output and procedures. This not aid the potential reader to follow the entire methodology
Response 6: Thank you for your comment. After discussion, we found that Figure 2 is not helpful to the structure of the entire paper. Therefore, we decided to delete Figure 2 and describe its main content in text.
Point 7: the previous point is related to another problem: it is not clear how analytic formulation presented in section 4 is connected to the entire methodology.
Response 7: Thanks for your comment. We had improved the content of Section 3, and strengthened the connection between Section 3 and Section 4.
Point 8: the steps illustrated in section 4 are related to the figure 2?
Response 8: Thanks for your comment. We had revised the writing in our paper as advised.
Reviewer 3 Report
1.This study shows the effects and efficiency of modified Shapley value model! However, it need to add the other models which are antagonistic to Shapley value model to prove accurately this study. If you can, please add other models more.
2. I agree modified Shapley value model is useful. However the examples are too little to prove the effects. If you can, please add other examples or case studies.
Author Response
Please see the attachment.
Author Response File: 
Author Response.docx
Round 2
Reviewer 1 Report
authors addressed all my comments
Author Response
Dear reviewers:
Thank you for your kind advice. We had invited native English speaking professionals to polish the article. The amendments are highlighted in red in the revised manuscript.
Reviewer 2 Report
the new version improves the paper's quality.
Final revision relative to formatting is suggested. In particular, please, pay attention to the final editing of references, according to the journal's standards. (e.g. not "International Conference on Computer 734 Simulation in Risk Analysis & Hazard Mitigation" but "WIT Transactions on Ecology and the Environment, , ,
Author Response
Dear reviewers:
Thank you for your kind advice. We had invited native English speaking professionals to polish the article. The amendments are highlighted in red in the revised manuscript.
