Research on Location and Routing for Cold Chain Logistics in Health Resorts Considering Carbon Emissions

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The phrase needs to focus on some context in which this is the case because it is not the same in all parts of the world, and it must be based on a reference: "The increasingly serious global trend of aging populations has exacerbated the demand for healthcare and elderly care services."

- Missing a "." after the T in several citations such as "Rossi T et al".

- Section 2 should conclude with a summary table showing the characteristics sought and how none of the previous proposals meet all of them, in order to affirm the motivation of the work.

- Regarding carbon emissions, it is not clear to me how the model handles the issue in detail. Energy consumption is not the same as carbon emission. There may be cars powered by solar panels for which it may not be clear how "normal distance fuel consumption per unit distance" is calculated. This could also influence statements such as "The carbon emissions during the storage phase are mainly those produced by cold 213

storage.". In general, I believe that the explanation of the measurement of carbon emission (in conservation and emission) deserves greater detail, as it is an essential aspect of the proposal.

- It seems to me that the UN Sustainable Development Goals should be mentioned, particularly to those to whom it pays.

- I think it is worth explaining why the expression (14) "minZ1 = F1 + F2 + F3 + F4 + F5" was chosen instead of a multi-objective approach that could consider the trade-offs between the different factors. Why was it chosen to add the aspects in functions (14) and (15) (which are then further integrated in expression (35) into a single objective) instead of working with each of the aspects as independent objectives. I think it deserves to be commented on because it would allow us to obtain more compromise solutions that give more options to a decision maker.

- I think the process of obtaining fuzzy numbers deserves more explanation.

- The decision must be further justified: "This method primarily uses a genetic algorithm to solve location allocation problems, while leveraging the Gurobi solver for route arrangement during the evaluation process of each generation of solutions." What led to that division?

- Why was a genetic algorithm chosen among several existing metaheuristics? The nearby previous works mentioned in section 2 must be analyzed to justify the approach from an analytical point of view since there is no experimentation associated with various alternative solution approaches.

- Review the text: "segement"

- A case study is used with "7 medical institutions and 23 elderly care areas". Is it similar to previous ones in the literature? Is size significant? Do the solutions shown in Figures 7 and 8 plus Table 6 correspond to a problem whose magnitude prevents exhaustive exploration? How big is the search space?

- How the parameters shown in Table 5 were arrived at. Algorithm Parameters Value.

Author Response

Thank you for your professional comments. Please find detailed responses in the document "Respond review 1-MDPI.pdf".

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

 

This manuscript addresses the location routing problem in the context of medical, cold chain logistics whenever demand uncertainty exists. Along with the objective of minimizing the total costs associated with the distribution and inventory decisions, this manuscript considers the minimization of the carbon emission as well. However, the manuscript avoids a multi-objective treatment of the problem and adds an equivalent cost of carbon emission to the total cost function. The manuscript proposes a metaheuristic approach based on genetic algorithm (GA) and Gurobi solver. A case study is presented to demonstrate the utility of the proposed approach.

In general, this manuscript addresses an important problem and provides a solution approach that is worthy of consideration. However, there are some concerns that need to be carefully addressed as outlined in the following points:

1. Although the presented model integrates different cost terms, the treatment of the problem cannot be considered as a multi-objective one. After presenting the carbon emission objective function, its equivalent cost is just added to a single total cost objective function. Moreover, the presented solution approach is a single-objective methodology. Therefore, it is not justifiable to use the term “multi-objective” as part of the contributions of this manuscript. Authors are advised to revise the manuscript accordingly, especially lines 38-41 47-48, along with the abstract

2. The presented model in section 3.2 integrates different cost elements into one function. However, the integrated cost elements do not seem to be consistent in terms of the time-value of money. For instance, there is a fixed investment cost for a candidate logistic center, denoted Ca, which is supposed to be a long-term cost. Same applies to the fixed cost of a vehicle (fl). Meanwhile, the transportation, refrigeration, damage, and carbon emission costs are all operational (short-term) costs. When long-term investment costs are combined with short-term operational costs, the time-value of money must be taken into consideration. Please revise the manuscript accordingly.

3. In the proposed model, the symbol Z is used twice, one for the objective function and the other for the decision variables used for vehicle visits. Please use different symbols.

4. The proposed solution approach does not seem to be suitable for large-scale instances of the problem. The utilization of Gurobi solver for solving the vehicle routing sub-problem (VRP) will not be efficient for such cases. No matter how efficient commercial solvers are, their capabilities in solving large-sized instances of NP-hard problems is questionable. Not to mention that the VRP is just a sub-problem that is being solved several times during the GA search. Since the presented case-study is relatively small, this limitation is not apparent in the results. Furthermore, nothing is reported in the manuscript about the computational time requirements. To address this limitation, authors are advised to consider other efficient heuristics for solving the VRP sub-problem from the literature and conduct some computational comparisons with Gurobi solver to assess the computational time requirements.

5. As an important part of the design of metaheuristics, some parameter tuning experiments are conducted. Unfortunately, this manuscript does not include such experiments. The authors are advised to provide computational experiments on small and intermediate instances to guide the selection of the metaheuristic parameters.

6. The presented case study is not sufficient to draw general conclusions about the quality of the solutions generated by the developed metaheuristic. Authors are advised to provide additional computational experiments for a well-designed set of large-sized instances, and to conduct computational experiments with other candidate metaheuristics from the literature.

In conclusion, this manuscript is not suitable for publication in its current form. Authors are required to consider the above concerns and submit a revised manuscript accordingly.

 

Comments on the Quality of English Language

English quality is good. There are some minor typos that need careful proofreading of the manuscript.

Author Response

Thank you for your professional comments. Please find detailed responses in the document "Respond reviewer 2-MDPI.pdf".

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Nothing more to add. All good 

Reviewer 2 Report

Comments and Suggestions for Authors

Authors' responses to my comments and their modifications to the originally submitted manuscript are satisfactory.