Route Optimization for Hazardous Chemicals Transportation under Time-Varying Conditions
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis paper studies the optimization problem of road transportation routes optimization problem of hazardous chemicals under dynamic time-varying conditions.Taking into account the carbon emissions generated during transportation enriches existing related research. But I think there are several points in the article that need improvement.
â‘ Please add necessary annotations to Figure 1. For example, which areas belong to "on the road" and which areas belong to "around the road".
â‘¡ There is an issue with the image number, why are there two Figure 1?
â‘¢ What are the advantages of the method used in this article to calculate carbon emissions during transportation compared to existing methods?
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe main focus of this paper is to investigate the optimization of road transportation routes for hazardous chemicals under dynamic and time-varying conditions. A multi-objective nonlinear optimization model is developed in order to minimize transportation risks, costs, and carbon emissions during the transportation process. The model is solved using an enhanced fast non-dominated sorting genetic algorithm (NSGA-II). Experimental results demonstrate that vehicles departing at different times will encounter varying transportation costs and risks due to time-varying conditions. Therefore, enterprises should arrange vehicle departure times reasonably based on customer node time windows and road conditions. Furthermore, from an objective optimization perspective, reducing transportation costs should be prioritized in order to achieve environmentally-friendly, sustainable, and low-risk transportation practices.
The strengths of this paper include: firstly, it explores the optimization of road transportation routes for hazardous chemicals which has practical implications; secondly, it constructs a multi-objective optimization model that considers various factors such as transportation risk, cost, and carbon emissions while comprehensively addressing the goal of sustainable development; thirdly, it employs an improved genetic algorithm to enhance efficiency and accuracy.
However, there are areas where this article could be improved: firstly, although experimental results on the Sioux Falls network were mentioned briefly in the article's discussion section but lacked detailed introduction or analysis regarding experimental design which could further enhance its credibility; secondly，the article mentions that enterprises need to arrange vehicle departure times reasonably according to time windows and road conditions without specifying how these arrangements can be made effectively; lastly, the literature review may be improved by citing more relevant papers. Just list several as follows.
AI-Empowered Speed Extraction via Port-Like Videos for Vehicular Trajectory Analysis
A hybrid visualization model for knowledge mapping: scientometrics, SAOM, and SAO
Comments on the Quality of English LanguageGood
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsSecurity issues are always relevant in any field of activity. This also applies to the process of transporting dangerous goods. The search for optimal solutions, which is implemented in the presented study, certainly has practical value and contributes to the implementation of sustainable development goals. In this regard, I note that the problem being solved corresponds to the topic of the Sustainability magazine.
The study conducted by the authors is impressive, it is rich in mathematical tools and appropriate approbation.
At the same time, in order to improve the quality of the manuscript and the provisions formulated by the authors, it is recommended to take into account some comments.
• The "Introduction" section should define the purpose of the work and its significance, including specific testable hypotheses.
• Section 3 requires clarification of the empirical basis underlying route optimization. In section 5.1, the tables contain arrays of data, but the database is not specified. What are the characteristics of the vehicles considered in the optimization problem solved by the authors?
• It is recommended in section 3 to build a detailed research algorithm for all readers to understand.
• In my opinion, the author's recommendations on the results of optimization are presented vaguely, it is recommended to specify exactly how to reduce transport costs, which information technologies will ensure a reduction in transport costs.
• In the section "6. Conclusions" it is recommended to clearly highlight the novelty of the proposed solution.
I wish the authors good luck!
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 4 Report
Comments and Suggestions for AuthorsCheck for minor typos (e.g., line 226, line 243,…).
Page 4, Assumption 1: this assumption is questionable. The interaction between vehicles is crucial in the evaluation of transportation costs.
Page 5: Assumption 5 is questionable. How does it affect route optimization? If the aim is to minimize costs or emissions, visiting customers in growing demand will influence the optimization phase.
Lines 250-251: alfa and beta have the same definition. What is the difference between these two parameters?
Please, discuss in detail equation 1. Clarify the role of each element and discuss the numerator of the equation. Similar considerations for Equation 6.
The equations 4-7-8 are fundamental in understanding your work. I suggest providing more comments on these equations.
Provide more references on risk. As an example, I suggest 10.1007/978-3-319-67308-0_55 and 10.2495/SDP-V1-N2-170-191.
Equations 9-11: To facilitate reading, I suggest that you write the objective functions in full.
Section 4.2: please, clarify the coding method.
Section 4.3: If I understand correctly, you built the path between two customers when decoding the solution using the adjacency matrix. This section needs to be improved, you should better highlight the difference between network nodes and ‘customers’ nodes. For example, it is not very clear whether in the adjacency matrix there are all nodes of the network or only the customers.
Section 4.4: What you do doesn't feel like a crossover. You build the individual C1 starting from the genes of P1 alone, without any contribution from P2. Is that true?
Table 2: the data are random?
Test problem: Although you have foreseen different situations, in my opinion, the test problem is too small. I suggest improving the test by using a large test network.
Comments on the Quality of English LanguageNo comment
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsThe authors have dealt with all my concerns.
Reviewer 4 Report
Comments and Suggestions for AuthorsThe article has been revised taking into account what was indicated in the comments
