Review Reports - A Scheduling Algorithm for Multi-Workshop Production Based on BOM and Process Route

Round 1

Reviewer 1 Report

The problem presented in this article is current, especially among companies that deal with production management. Time pressure and process variability are very important in dynamically producing enterprises. The research is very interesting and presented in an original way. However, the methodology of the analysis itself is poorly described. The study shows that the presented model and algorithm can significantly solve the problem of scheduling the production of multilayer. However, the conclusions are fairly obvious and short. In conclusion, the conclusions should be detailed, and there are many of them in the research carried out. They should be distinguished, where exactly the model and algorithm can be used.

Author Response

Dear reviewer, thank you for your suggestions. We have revised the manuscript according to your suggestions. Please refer to the document for relevant contents "Answer to queries(Reviewer 1).docx". thank you!

Author Response File: Author Response.docx

Reviewer 2 Report

This paper proposed a mathematical model for scheduling in multi-workshop production. A modified PSO algorithm is proposed to solve this problem. Experiments are conducted to test the performance of the proposed algorithm.

Overall, it is an interesting topic. The structure is good and the writing is good. Some changes are required to further improve it.

Authors’ Affiliations are not provided.
Please provide the full name of BOM in the abstract. Also, its full name should be provided at its first appearance in the main text.
The overview of JSP should be extended. Here are some useful examples.

(1) Many-Objective Evolutionary Algorithm With Reference Point-Based Fuzzy Correlation Entropy for Energy-Efficient Job Shop Scheduling With Limited Workers, IEEE Transactions on Cybernetics, 2021.

(2) A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems. IEEE/CAA Journal of Automatica Sinica, 6(4), 904-916, 2019, etc.

Line 143, “in addition” -> “In addition”.
Please increase the font size of figures in Figs. 1 and 2.
Lines 163-164 should be adjusted.
Line 168, “the Scheduling task”->” the scheduling task”.
Line 171, “In amulti-workshop environment”->” In a multi-workshop environment”.
Line 188, “the process network diagram was constructed by using the constraints of BOM and process route.” The reason for using them is not mentioned in Section 1.
A modified PSO is utilized. However, the reason for proposing a new one is not sufficiently provided. Literature review on PSO is not sufficient. Some references should be noted. (1) Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions. IEEE Transactions on Evolutionary Computation 23 (4), 718-731, 2019. (2) Surrogate-assisted particle swarm optimization algorithm with Pareto active learning for expensive multi-objective optimization. IEEE/CAA Journal of Automatica Sinica, 6(3), 838-849,2019, etc.
Line 211, a space is required before “But in this paper”.
All equations should be numbered. Please pay attention to the equations in Section 2.
Line numbers are missing for the first paragraph of Section 4.
Citations should be added for Particle swarm optimization (PSO) in the first paragraph of Section 4.
Figure 8, the judging process of “Does moving B cause overdue” lacks a “NO” output.
Figure 11, legends should be added for each line type.
Citations for the comparisons should be added. Why do you choose GA and ACO as comparisons? It is recommended to compare the proposed algorithm with some PSO variants.

Author Response

Author Response File: Author Response.docx