Electric Vehicle Routing Problem with States of Charging Stations

Round 1

Reviewer 1 Report (Previous Reviewer 2)

This article develops a mixed-integer programming model and proposes a solution strategy for the electric vehicle routing problem that considers the state of the charging station. This paper investigates how to estimate the state of the charging station and determine the routing strategy, and through arithmetic examples, it provides the optimal path strategy for electric vehicles in an environment where the state of the charging station changes.

The paper has been revised in accordance with the previous suggestions, but there are still some elements that I would like to add to improve the paper.

1.  The paper mentions the use of a nonlinear function to model charging time.  However, it would be beneficial to explore in more detail how variability in charging times (which may depend on the EV's battery state, the charger's power output, and other factors) affects the routing solution.  A sensitivity analysis or discussion of how different charging rate scenarios affect the results of the proposed model could add depth to the research.

2.  While the focus is on routing efficiency and optimizing electric vehicle use, integrating an environmental impact assessment could align the study more closely with sustainability goals.  For example, evaluating the reduction in CO2 emissions resulting from optimized routing compared to traditional routing methods could offer a more holistic view of the benefits.

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report (Previous Reviewer 1)

The authors have addressed all comments. The explanations are sufficient.

The authors referred to the adopted and model data set during the study. The article reported that, therefore, terrain and weather conditions were not taken into account. The degree of vehicle loading during route optimization was also not taken into account. I understand that work in this area is being developed and this is a presentation of a certain stage of work. So I think that with the information of assumptions the article can be published in the current version.

At the same time, I would like to emphasize that the change in atmospheric conditions, especially temperature, change of topography (driving uphill or downhill), or carried weight have a significant impact on the operation of batteries and thus the range of electric vehicles. Taking into account these parameters in further development work I think is an important aspect.

Author Response

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Author Response File: Author Response.pdf

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

The topics raised by the author are timely and very important in today's world. How the route is planned and the timing of battery charging is crucial for reliability and continuity of supply. The author emphasizes this repeatedly in the text. The author reviewed the literature related to the topic of the manuscript and presented it in brief form in the first part of the article. In the next part of the article, the author presents the concept of his idea of solving the issue related to optimizing the route of electric vehicles taking into account the need to charge the batteries. The presented model takes into account the status of charging stations.

However, the following questions arise when reading the text of the manuscript:

·        Page 4, row 191 - there is "In general, there are multiple charging stations... " - what about regions where the amount of charging stations is low?

·         Page 5, lines 202 -208 - is: " If the vehicle chooses to charge in the first time period, the vehicle does not require charging in the second time period...". - and isn't it possible to arrange the work schedule in such a way that the vehicle charges in the interval between the first and second time periods?

·        Are the data for the examples (number of vehicles, distribution of charging stations, status of charging stations, etc.) data obtained from a specific area, estimated from historical data, statistical data or random data invented for the purpose of the work in progress?

·        Are the layout of charging points, list of customers, distances between customers, and terrain topography taken from real data or are they just sample random points in a random number?

·        Was the route topography taken into account during route optimization and was it verified, for example, with the selected real area?

·        Is the change in weather conditions (temperature, rain, snow, etc. ) factored into the route optimization?

·        Does the level of vehicle load matter and affect the route optimization process?

·        Has the model been verified in any way or is it a purely theoretical model?

·        Is the information related to the status of the charging points updated in real-time, with what frequency is it updated or is it based only on statistics?

In addition, several editorial errors were noted:

-        Page 9 row 344 - 345 - there is :" For example, zero arrivals is four times in Table 1..." in Table 1 there are 5 occurrences of "0".

-        Page 10 figure 6, 7 - to avoid confusion and misinterpretation I would suggest in the text describe what the numbers in the table mean and how they should be understood.

-        I would suggest describing / explaining in the text how the "total cost" of each route is calculated.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

This article develops a mixed-integer programming model and proposes a solution strategy for the electric vehicle routing problem that considers the state of the charging station. This paper investigates how to estimate the state of the charging station and determine the routing strategy, and through arithmetic examples, it provides the optimal path strategy for electric vehicles in an environment where the state of the charging station changes.

This paper suggests some modifications. The problems are as follows:

1. the literature review section has a lot of space but less literature, consider whether to add some literature from the last three years?

2. The algorithm design part can add other algorithm comparisons and experimental controls.

3. The above function (17) and the following (18), (19) expressions should be consistent.

4. In the conclusion part after the five examples, the original expression is: Thus, visiting charging stations in period 2 is better than the other way. On the other hand, vehicles are better to visit the charging stations in period 1, which indicates that it is better to visit charging stations in both periods, which is a contradictory conclusion.

It can be revised

Author Response

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Author Response File: Author Response.pdf