Next Article in Journal
Scale-Location Dependence Relationship between Soil Organic Matter and Environmental Factors by Anisotropy Analysis and Multiple Wavelet Coherence
Previous Article in Journal
Assessment of Drinking Water Quality and Associated Socio-Economic Impacts in Arid Mountainous Regions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 19
10.3390/su141912566
Review Report
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
Article
Peer-Review Record

Low-Carbon Product Family Planning for Manufacturing as a Service (MaaS): Bilevel Optimization with Linear Physical Programming

Sustainability 2022, 14(19), 12566; https://doi.org/10.3390/su141912566
by Xiaojie Liu 1,2,*, Xuejian Gong 3 and Roger J. Jiao 3
Reviewer 1:
Lin Gong
Reviewer 2:
Karina Arredondo
Sustainability 2022, 14(19), 12566; https://doi.org/10.3390/su141912566
Submission received: 17 August 2022 / Revised: 13 September 2022 / Accepted: 29 September 2022 / Published: 2 October 2022

Round 1

Reviewer 1 Report

1. In general, this paper solves a specific engineering problem in MaaS. In the study, the decision-making mechanism is proposed and the optimization model is constructed. However, its core problems and main contributions seem to be unclear.

2. in this paper, MaaS often exists in the form of crowdsourcing. It is suggested to further increase the literature analysis on crowdsourcing.

3.Low carbon product is mentioned in the title, but it seems that low carbon is only considered in the manufacturing process in the method study, but the PFP does not involve the relevant content of low carbon.

4.In figure 3, the leader follower interactive decision-making mechanism is mentioned,  but how to interact is not clearly described.

5.Compared with the general GA algorithm, the innovation of NBGA algorithm is not obvious.

Author Response

Thanks for high-quality, constructive reviews. Indeed the review comments are thorough and quite insightful for us to improve the paper. Please refer to the following summary of revisions in response to each point of the review comments.

Author Response File: Author Response.pdf

Reviewer 2 Report

A sufficiently extensive and detailed work is presented on a topic that is in line with the objectives of this journal. Some comments to improve the manuscript are the following:

Regarding the equations section, it is important to cite how they arise or reference if there are other research papers that use them or if you propose them specifically. Determine if this is a contribution as an innovative model or algorithm or is an application very similar to other existing models. Present a comparison with other approaches but do not cite them according to authors proposing them.

Clarify the contribution with respect to other manuscripts that also focus on this topic.

Author Response

Thanks for high-quality, constructive reviews. Indeed the review comments are thorough and quite insightful for us to improve the paper. Please refer to the following summary of revisions in response to each point of the review comments.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

In general, this paper solves a specific engineering problem in MAAS. The proposed methods and models are innovative.

Reviewer 2 Report

All the comments were addressed. Congratulations on your efforts.

Back to TopTop