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Article
Peer-Review Record

A Time-Space Network-Based Optimization Method for Scheduling Depot Drivers

Sustainability 2022, 14(21), 14431; https://doi.org/10.3390/su142114431
by Fei Peng 1, Xian Fan 2, Puxin Wang 1 and Mingan Sheng 2,*
Reviewer 1:
Reviewer 2:
Reviewer 3: Anonymous
Sustainability 2022, 14(21), 14431; https://doi.org/10.3390/su142114431
Submission received: 1 August 2022 / Revised: 16 October 2022 / Accepted: 27 October 2022 / Published: 3 November 2022

Round 1

Reviewer 1 Report

The paper considers the problem of optimization of scheduling drivers in a high-speed railway depot. The work is interesting, relevant.

The paper is well-organized, supplemented with good examples and illustrations.

 

But there are a number of shortcomings that should be corrected.

 

It’s recommended to provide an open data and program code in order if s-b wants to verify numerical results.

 

Does the developed algorithm have any restriction, for example, the dimension, task-size?

 

What the computational complexity of suggested approach? This estimation should be provided.

 

 

Some types, mistakes, formatting typos (extra spaces, missed spaces, formulae formatting, etc..) and mistakes have place. Please, re-check text carefully.

 

I do not think its’ a good idea to use notations from programming languages to denote the functions such as “solution.Constraints” or “tasks.Then.objective function”. It would be better to use commas or special formatting to select them in text.

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Review on sustainability-1870737

A time-space network-based optimization method for scheduling drivers in a high-speed railway depot

 

This paper concentrates on defining, modeling and solving the depot driver scheduling problem which can simultaneously determine driver size and driver schedule. A time-space network is first constructed based on which the authors formulate the problem as a minimum cost multi-commodity network flow problem. Then, a time-space network is developed based on which the authors formulate the problem as a minimum cost multi-commodity network flow problem. The obtained results show the significance of the ratio of driver size to task size in the depot.

The paper is well written and organized. Also, it contributes to the literature. Despite it has some merits, however, there are some comments as follows which should be applied as a major revision:

 

1)      The Introduction and literature sections are strongly suggested to be separated.

2)      The literature must be improved, specifically with the recently published ones.

3)      The assumptions of the study should be better presented as a different subsection.

4)      It sounds like that the title of Table 1 is wrongly written, since the content provided in Table 1 is not ‘’the input data”, but they are actually input parameters and their definitions.

5)       All input parameters, decision variables, and indices should be separately presented as different subsections.

6)      The characteristics of the used PC and other software should be specified at the beginning of Section 5.

7)      How did the authors tune the used parameters?

8)      Some managerial insights should be extracted according to the obtained results.

9)      Conclusion sections should be rewritten, so that more details about the obtained results, conducted sensitivity analyses, and extracted managerial implications should be included.  

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

I have reviewed the paper, titled “A time-space network-based optimization method for scheduling drivers in a high-speed railway depot ".

 

Comments:

 

A)     Relevance: The paper is within the areas of interest of Sustainability journal.

 

B)    Title: The paper title reflects the main core of the provided novelties. However, the title can be shortened and condensed.

 

C)   Keywords: The provided keywords are not enough.

 

D)   Originality and contribution: The paper conveys some novelties and contributions. It seems to be eligible for publications after doing the minor modifications as follows:

 

E)    The communication aspect of paper suffers from some grammatical mistakes and typos. I recommend to revising some main sections, including Abstract and Conclusion with the help of a native editor.

 

F)    I had a deep review on research literature. I found that the enough attention has not been paid to describe the literature and the related issues to Reference section. It is so necessary to have a comprehensive review on references not only for providing a correct form of citations, but also for detail description of new published papers.

 

G)   I’m wondering why the authors didn’t mention some exact optimization methods to tackle this type of models!!!. There are some exact optimization methods to treat this type of problems. The authors strongly do need to address mentioned methods in below papers. The author(s) don't need to describe the applied methods in below papers in detail manner, but they heavily need mentioning the name of author(s) and publication year for more reviews of readers about exact solution methods. So, you can open a parenthesis and write: for deep reviews of exact optimization methods, consider the below references:

 

  • doi.org/10.1080/23302674.2022.2083254 
  • doi.org/10.1016/j.cie.2022.108130 
  • doi.org/10.1080/23302674.2021.1958023 
  • doi.org/10.1080/23302674.2021.1919336 
  • doi.org/10.1080/23302674.2021.2015007 
  • doi.org/10.1007/978-3-030-89743-7_10 
  • doi.org/10.1016/j.jclepro.2021.127216 
  • doi.org/10.1007/s10479-022-04648-w

 

H)   It is strongly recommended to add ALL OF the above references not only in the manuscript, but also at the end of paper (Reference section) in a detail manner, accurately.

 

I)      Please delete old references. Maintain references from 2010 to 2021.

 

 

J)     Assumptions: I believe that this paper is configured based on some certain assumptions. Assumption(s) may be obvious, but they must be clearly stated.

 

K)    Applications: Who would benefit from the paper and how? How valuable your results are to managers? What suggestions you have for them?

 

L)    I recommend authors to going in deep in managerial implications. Therefore, a stronger effort in reporting recommendations is heavily requested.

 

M)   Limitations and Future research: What are the other limitations of this research? So, what do author(s) suggest as future research in order to cover the limitations. It is strongly recommended to mentioning aforesaid issues in “Conclusion” section.

 

 

N)   At first, the above minor modifications should be done, accurately. Then, the paper must be reviewed, again. I will make my decision, after doing the above modifications in a detail manner, accurately.

 

 

 

 

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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