Urban Rail Transit Rolling Stock Scheduling Optimization with Shared Depot
Round 1
Reviewer 1 Report
The contribution seems to be a search for an optimisation design solution rather than a research project. Basically, there are constituent elements that could be interesting (especially methodologically) if they were developed and compared not only with a local problem but with a broader view of recent developments on the subject. This is evident from the references and the state of the art, which are predominantly local.
A closer examination of the state of the art and a comparison of the proposed methodology with those already in place in other countries is called for, bringing out the ability to relate the local problem to global advancement of knowledge.
Author Response
We highly appreciate all reviewers for their positive and constructive comments, which have helped improve the manuscript substantially. We provide a point-by-point response showing how each comment has been addressed.
Next, we give our detailed reply to each comment of reviewers. We use the following color scheme in this reply report. The original comments from the reviewers are kept in black and italic; our responses are marked in purple; and the added/changed texts in the revised manuscript are in blue. The changes in the revised paper are marked in red.
(1) Response of Reviewer #1
The contribution seems to be a search for an optimisation design solution rather than a research project. Basically, there are constituent elements that could be interesting (especially methodologically) if they were developed and compared not only with a local problem but with a broader view of recent developments on the subject. This is evident from the references and the state of the art, which are predominantly local.
Response: Many thanks for your valuable and constructive comments, which have helped us to improve the clarity and quality of our paper. At the same time, we also appreciate the reviewer’s recognition of the research content. We give item-by-item responses to the reviewer’s comments as follows.
A closer examination of the state of the art and a comparison of the proposed methodology with those already in place in other countries is called for, bringing out the ability to relate the local problem to global advancement of knowledge.
Response: Thanks very much for the valuable suggestions of the reviewer. In the Introduction, the literature review has some commonalities in nature, that is, it is necessary to describe the research status and the comparison between the studies in other countries and our proposed methodology. As a result, a part in the Introduction have been added to bring out the ability to relate the local problem to global advancement of knowledge according to the suggestions of reviewer. Please refer to the revised Section 1 for details.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The paper aims to optimize the rolling stock scheduling of two urban rail transit lines with a shared train depot.
1. please describe EMU in the earlier part of the paper.
2. as suggested in the introduction that the aim of the study is to optimize the rolling stock scheduling of two urban rail transit lines with a shared train depot. However, the objective function aims to reduce the operating costs and improve the utilization rate of the rolling stocks, can authors be more specific on the objectives?
3. In the results, the paper suggested the efficiency of rolling stock and the daily utilization efficiency, however, it is not clear how this two matrixes can be used to optimize the rolling stock scheduling. Please add more discussion on the optimization.
4. It is strongly recommended to run a sensitivity analysis.
Author Response
We highly appreciate all reviewers for their positive and constructive comments, which have helped improve the manuscript substantially. We provide a point-by-point response showing how each comment has been addressed.
Author Response File: Author Response.pdf
Reviewer 3 Report
The article deals with the optimization task of the urban transport network. It is a difficult combinatorial problem, for which the authors chose a heuristic method of integer programming – the ant colony method. In the research section, I positively evaluate table No. 1, where the orientation of the method used is clearly justified. I have a critical comment about the number of iterations in the Results section. In the algorithm (Section 3.3), in the case of branching, the next step is decided randomly. The question is whether the number of iterations of 100 is sufficient to reach a steady state. I think it would be appropriate to carry out 1-2 more experiments with different number of iterations (nmax) and prove that the results of the experiments differ minimally.
Author Response
We highly appreciate all reviewers for their positive and constructive comments, which have helped improve the manuscript substantially. We provide a point-by-point response showing how each comment has been addressed.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
The authors have addressed all comments, hence the article can be accepted in the current format.
Author Response
We highly appreciate all reviewers for their positive and constructive comments, which have helped improve the manuscript substantially. We provide a point-by-point response showing how each comment has been addressed.
Next, we give our detailed reply to each comment of reviewers. We use the following color scheme in this reply report. The original comments from the reviewers are kept in black and italic; our responses are marked in purple; and the added/changed texts in the revised manuscript are in blue. The changes in the revised paper are marked in red.
Response of Reviewer #2
Reviewer 1’s concerns have not been properly addressed. Please, enrich the state of the art with more recent contributions. Moreover, there are notably studies totally neglected, see for instance D’Ariano et. al.; D’Acierno et al. etc.
Please, make sure to suitably do that; otherwise, the paper cannot be accepted for the publication.
Response:
We thank the reviewer for this important comment since it has helped us to improve our work. As suggested, we have cited three relevant references in the revised manuscript. The added references list as follows.
- Andrea D’Ariano, Lingyun Meng, Gabriele Centulio, Francesco Corman. Integrated stochastic optimization approaches for tactical scheduling of trains and railway infrastructure maintenance. Computers & Industrial Engineering, 2019, 127, 1315-1335.
- Marcella Samà, Andrea D’Ariano, Dario Pacciarelli, Francesco Corman. Lower and upper bound algorithms for the real-time train scheduling and routing problem in a railway network. IFAC Conference, 2016, 49-3, 215-220.
- Antonio Placido, Luca D’Acierno A methodology for assessing the feasibility of fleet compositions with dynamic demand. Transportation Research Procedia, 2015, 10, 595-604.
These references are cited in our manuscript to enrich the state of the art in the fields of (i) the research object and (ii) the model structure and solution method. Accordingly, we have added the following to 1 Introduction and Table 1.
Placido and D’Acierno promoted a decision support system for assessing the feasibility of fleet compositions with dynamic demand, and a Italy metro line has been used to demonstrate the usage of this method[14]. Samà et al. discussed the train scheduling and routing problem in a complex railway network in which two practical cases from Dutch and British railways are studied[15]. D’Ariano et al. contributed a mixed-integer linear programming formulation to describe the optimization of train sequencing and routing decisions and timing decisions related to short-term maintenance works in a railway network subject to disturbed process times[16].
Table 1. Summary of relevant studies on the rolling stock scheduling and circulation planning.
|
Publications |
Object |
Infrastructure |
Model structure |
Solution algorithms |
||||
|
Single line |
Multi line |
Single depot |
Multi depots |
Shared depot |
||||
|
Cadarso[11] |
SR |
|
√ |
|
|
√ |
MCFM |
CPLEX |
|
Ciacco[12] |
SR |
|
√ |
|
|
√ |
MILP |
BD |
|
Zheng[13] |
URT |
|
√ |
|
√ |
|
MIP |
Tabu research |
|
Samà[15] |
R |
|
√ |
|
|
|
MILP |
LB/UB/CPLEX |
|
D’Ariano[16] |
R |
|
√ |
|
|
√ |
MILP |
CPLEX |
|
Yue[14] |
URT |
√ |
|
|
√ |
|
Bi-level model |
CPLEX/SA |
|
Wang[15] |
URT |
√ |
|
√ |
|
|
MILP |
CPLEX |
|
Zhang[16] |
URT |
√ |
|
|
√ |
|
MILP |
CPLEX |
|
Zhong[17] |
URT |
|
√ |
|
√ |
|
MILP |
CPLEX |
|
Our work |
URT |
|
√ |
|
√ |
√ |
MIP |
ACO |
1 Symbols description for Table 1: railway (R); suburban railway (SR); urban rail transit (URT); Multi-commodity flow model (MCFM); mixed integer linear programming (MILP); mixed integer programming (MIP); Cplex solver (CPLEX); Benders decomposition (BD); Lower bound (LB); Upper bound (UB); Simulated annealing (SA); Ant colony optimization algorithms (ACO).
Author Response File: Author Response.pdf
