A Study of Community Group Purchasing Vehicle Routing Problems Considering Service Time Windows

Round 1

Reviewer 1 Report

Express the numbers in the text and tables according to the English writing model, in which:
. (point) - separates decimals from a number;
, (comma) - delimits the units

The sentence on lines 183-184 should end with ":", because then the 4 conditions are listed.

There is a suspicion of plagiarism in the subchapter Problem description, so the text must be reinterpreted.

Author Response

Point 1: Express the numbers in the text and tables according to the English writing model, in which:

. (point) - separates decimals from a number; , (comma) - delimits the units.

 

Response 1: Thank you very much for your valuable comments, we have adjusted the notation in Table 2-7.

 

Point 2: The sentence on lines 183-184 should end with ":", because then the 4 conditions are listed..

 

Response 2: Thank you very much for your valuable comments. I have improved this according to your suggestions.

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Point 3: There is a suspicion of plagiarism in the subchapter Problem description, so the text must be reinterpreted.

 

Response 3: Thank you very much for your comments, we have removed the redundant paragraphs and text. We are sorry that our carelessness has led to such a low-level error.

Reviewer 2 Report

1)- Introduction ignored important contributions in this research area. Several methods are reported in the literature.

2)  The paper is not well written. In addtion, the contribution is, in
my opinion, not clear.   The authors should motivate more their contributions and clearly explain the intuitions behind the ideas. Also, more simulations and comparisons that show the advantage and the drawbacks of the proposed schema are needed. 

3)The authors should consider more challenging cases to assess the performance of the considered approach. 

4) In my opinion,  the discussion is unsatisfactory for presenting and discussing the "innovation" and originality of this work.

5) The paper does not compare to other methods. Please compare your approach with some commonly used.

Author Response

Point 1: Introduction ignored important contributions in this research area. Several methods are reported in the literature.

 

Response 1: Thank you very much for your valuable comments. I have improved this according to your suggestions.

Furthermore, community group purchasing has become a major lifestyle purchase for most residents under the epidemic, accompanied by the requirements of different regions for epidemic prevention and control, the requirement for delivery vehicles to enter the community at specified times, and the restrictions on the length of stay in the community, causing a great impact on the delivery problem of community group pur-chasing. It is therefore necessary to conduct further research on the distribution of community group purchasing.

Point 2: The paper is not well written. In addtion, the contribution is, in my opinion, not clear. The authors should motivate more their contributions and clearly explain the intuitions behind the ideas. Also, more simulations and comparisons that show the advantage and the drawbacks of the proposed schema are needed..

 

Response 2: Thank you very much for your valuable comments. I have done this according to your suggestions.

In contrast to current research, the contributions of this paper can be summarized in the following main points. Firstly, based on the delivery problem in community group buying, a VRP model considering delivery time window and variable service time is developed by taking into account the hard time window of demand infor-mation at the demand point in addition to the variable service time that exists accord-ing to the different quantity of goods demanded. Secondly, the ACA is improved by combining SAA. A simulated annealing mechanism is used to quickly generate the better solution, leaving pheromones for the better path, and then the traversal is com-pleted according to the ant colony algorithm, using simulated annealing to find anoth-er solution in the neighbourhood and eventually the optimal solution. Thereby an im-proved ACA is proposed to solve a real community group delivery problem. Finally, the superiority of the new algorithm is further verified by solving the benchmark da-taset data from the Solomon benchmark with the improved algorithm and the com-monly used algorithm. It is interesting to note from the comparison that commonly used algorithms for solving the VRPTW problem, such as GA, have a great advantage in solving the problem for the benchmark dataset. However, the computational per-formance of GA is not as significant when we use it for real-world problem solving or when we adapt the data from the benchmark dataset, for example, by making the in-formation on the location of demand points more complex and setting variable service times depending on the amount of goods demanded.

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Point 3: The authors should consider more challenging cases to assess the performance of the considered approach.

 

Response 3: Thanks to your valuable suggestions, we have added comparison cases from the Solomon benchmark dataset of 50 customers and 100 customers to evaluate the performance of the algorithm.

To achieve a reliable comparison, two cases from the publicly available Solomon benchmark dataset were used, the 50-customer case and the 100-customer c101 case. The 50 customer dataset was modified to bring the problem closer to reality, with modifications to customer location and goods demand, and variable service times given based on the amount of customer demand.

Point 4: In my opinion, the discussion is unsatisfactory for presenting and discussing the "innovation" and originality of this work..

 

Response 4: Thanks to your suggestions, we have added to the introduction and rewritten the discussion section.

Point 5: The paper does not compare to other methods. Please compare your approach with some commonly used.

 

Response 5: Thanks to your suggestion, we have added the GA solution results to the original ACA and SAA for comparison in the article.

Reviewer 3 Report

The manuscript entitled 'A study of community group purchasing vehicle routing problems considering service time windows' has been reviewed. I have some suggestions that may help to improve paper quality further:

1-The abstract is very brief and it is not clear why and how to use this optimization process. Also, given that the comparison of the three optimization methods has been done, it is suggested that the numerical improvement values obtained be given in the abstract. 
2- Does your focus on using ant colony algorithms or any new method or results which have new prominent advantages compared to other approaches of vehicle routing problem?
3- Why do you use your presented optimization algorithms?
4- How do you argue that this method works in other vehicle routing problems?
5- The convergence characteristic curves are provided to validate finding optimal points in single-objective or multi-objective equations. However, how can optimized values be validated?
6- A moderated English editing is needed.

Author Response

Point 1: The abstract is very brief and it is not clear why and how to use this optimization process. Also, given that the comparison of the three optimization methods has been done, it is suggested that the numerical improvement values obtained be given in the abstract..

 

Response 1: Thank you very much for your valuable comments. I have improved this according to your suggestions.

In this paper, a vehicle routing problem(VRP) model considering delivery time windows and var-iable service time is established for the delivery problem in community group purchasing. A solu-tion model for improved ant colony algorithm(ACA) is proposed by improving the initial feasible solution and the neighborhood search mechanism of the ant colony algorithm. The algorithm of the improved ant colony and the commonly used algorithm are solved for real cases and publicly available benchmark datasets, respectively, for comparative analysis. The results show that the improved ACA has stronger optimisation capability and faster convergence speed, and has ad-vantages in solving VRPTW problems with variable service time. The computational efficiency is also improved by 41% over the genetic algorithm(GA) in the solution of the benchmark dataset, which provides a certain reference for solving the community group distribution problem.

Point 2: Does your focus on using ant colony algorithms or any new method or results which have new prominent advantages compared to other approaches of vehicle routing problem?.

 

Response 2: Thank you for your suggestion. Compared with other path optimization algorithms, the ant colony algorithm is suitable for solving path optimization problems due to its positive feedback feature, which is mainly based on the pheromone concentration to determine the proximity of the path. In this paper, we propose improvements to the ACA and compare the computational efficiency with that of the commonly used SAA and GA methods, showing the advantages of the improved ACA in terms of computational performance..

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Point 3: Why do you use your presented optimization algorithms?

 

Response 3: Thanks to your valuable suggestions. We add to the original discussion a comparison with commonly used algorithms, such as GA and SA, as well as an evaluation using a standard dataset, and show that the improved ant colony algorithm has significant improvements in both computing time and computational accuracy.

Point 4: How do you argue that this method works in other vehicle routing problems?

 

Response 4: Thanks to your suggestions. By adding a standard dataset to the article and comparing it with commonly used algorithms to show the generality of the new approach to other vehicle routing problems.

 To achieve a reliable comparison, two cases from the publicly available Solomon benchmark dataset were used, the 50-customer case and the 100-customer c101 case. The 50 customer dataset was modified to bring the problem closer to reality, with modifications to customer location and goods demand, and variable service times given based on the amount of customer demand.

Point 5: The convergence characteristic curves are provided to validate finding optimal points in single-objective or multi-objective equations. However, how can optimized values be validated?

 

Response 5: Thanks to your suggestion. To verify the optimal values, we introduce an open standard dataset, which gives the optimal values for comparison with the improved ACA calculation results, in order to judge the performance of the improved algorithm.

Point 6: A moderated English editing is needed.

 

Response 6: Thanks to your suggestion. I have improved this according to your suggestions..

 

Reviewer 4 Report

This paper discusses the implementation of meta-heuristic optimization to solve vehicle routing problem modelling. The paper lacks the novelty that should characterize any research paper. The mentioned contribution at the end of “section 1” didn’t bring any added value. The combination of simulated annealing algorithm and ant colony algorithm was addressed by the research community for at least 2 decades. There are some particular problems the authors should consider strictly, which are listed below.

1. The content studied in this paper lacks  interconnexion. Authors are encouraged to restructure their paper and address the VRP problem rather than to provide literature review concepts on ACO and SAA in separate sections. A synthesized flowchart that analyses the VRP modelling issues using the selected optimization routine should be presented in this paper.
2. Isn't the system studied in this paper a bit outdated? It is more like a special case of some article system in reference.
3. The expression of this paper should be improved and the contribution should be highlighted through a practical significance example.
4. The conservatism issue should be discussed. The authors should consider whether
   There are other possible approaches to reduce conservatism or not.
5. The convergence is guaranteed but the time of convergence is uncertain. How can authors skip on this drawback for the studied case study?
6. The conclusions part just states what the authors have done. The advantages and drawbacks of the proposed approach should be highlighted.

 

Author Response

Point 1: The content studied in this paper lacks  interconnexion. Authors are encouraged to restructure their paper and address the VRP problem rather than to provide literature review concepts on ACO and SAA in separate sections. A synthesized flowchart that analyses the VRP modelling issues using the selected optimization routine should be presented in this paper.

 

Response 1: Thank you for your valuable suggestions. We have added a comprehensive flowchart to the article to describe the new algorithm. This is shown in Figure 3.

Figure 3. Flowchart of SAACA.

The flow chart is shown in Fig.3

Steps

1) SAA generates initial solution

2) The better path leaves the pheromone

3) the ACA completes a facilitation to produce a solution

4) SAA finds a new solution in the neighbourhood of the solution generated by the ACA

5) Determine if it is less than 0. If so, the Ant Colony updates the pheromone, otherwise go back to step 2)

6) Determine if the ACA has reached the maximum number of iterations, if so, output the optimal solution, otherwise go back to step 2) and continue the cycle.

 

Point 2: Isn't the system studied in this paper a bit outdated? It is more like a special case of some article system in reference.

 

Response 2: Thank you for your valuable comments. We have mainly considered the impact of the current epidemic, the important role that community group buying plays in our lives, and the problems of timing in the distribution process at this particular time, and the object and context of our study still meets the needs of society at the moment.

Furthermore, community group purchasing has become a major lifestyle purchase for most residents under the epidemic, accompanied by the requirements of different regions for epidemic prevention and control, the requirement for delivery vehicles to enter the community at specified times, and the restrictions on the length of stay in the community, causing a great impact on the delivery problem of community group pur-chasing. It is therefore necessary to conduct further research on the distribution of community group purchasing.

Point 3: The expression of this paper should be improved and the contribution should be highlighted through a practical significance example.

 

Response 3: We are very grateful for the valuable comments given by the referees that have improved the quality of the paper. We have added the corresponding examples to the article.

The company's distribution center is located near the Beijing Xinfadi vegetable Wholesale Market. To maintain customer information and the company's interests without loss of generality, customer demand information is optimised and the distribution centre and the 30 demand points are located as shown in Figure 4.

Figure 4. Customer demand information of company D

 

Point 4: The conservatism issue should be discussed. The authors should consider whether there are other possible approaches to reduce conservatism or not.

 

Response 4: Thanks to your suggestions. By adding a standard dataset to the article and comparing it with commonly used algorithms to show the generality of the new approach to other vehicle routing problems.

To achieve a reliable comparison, two cases from the publicly available Solomon benchmark dataset were used, the 50-customer case and the 100-customer c101 case. The 50 customer dataset was modified to bring the problem closer to reality, with modifications to customer location and goods demand, and variable service times given based on the amount of customer demand.

Point 5: The convergence is guaranteed but the time of convergence is uncertain. How can authors skip on this drawback for the studied case study?

 

Response 5: We evaluate the convergence time by going through a number of experiments, in the article we take the average of 50 experiments to obtain the convergence time.

Point 6: The conclusions part just states what the authors have done. The advantages and drawbacks of the proposed approach should be highlighted

 

Response 6: Thanks to your suggestion. I have improved this according to your suggestions.

The improved algorithm is superior to the commonly used algorithms in terms of computation time and accuracy when solving problems under variable service times. The same advantage of the improved ACA is present in the comparison for the benchmark dataset, with a 41% improvement in computation time over the commonly used GA algorithm. The improved ACA has a unique advantage over the GA in computing problems with variable service times.

Although several experiments have been done to take an average, the presence of chance cannot be ruled out. Furthermore, although the improved algorithm has ad-vantages in solving practical problems and computing for benchmark datasets, the in-crease in computation time is larger as the size of the data to be computed increases, which is still insufficient for handling large data, and the introduction of machine learning is considered in future research to improve computational efficiency.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

thanks authors for revision paper according reviwer's concern.

Reviewer 3 Report

Thanks for response and clarification. 

Reviewer 4 Report

All my comments have been addressed by authors. I recommend the acceptance of the paper.

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.