The Problem of Machine Part Operations Optimal Scheduling in the Production Industry Based on a Customer’s Order

Round 1

Reviewer 1 Report

Comment 1: The introduction starts by discussing the impact of outsourced production and then shifts towards the effects of the COVID-19 pandemic on supply chains. While both topics are relevant, the purpose of the paper isn't immediately clear. The introduction could benefit from a more concise statement of the research problem or question.

Comment 2: The introduction mentions the impact of the COVID-19 pandemic and its effects on SMEs, which is timely and relevant. However, it could be more explicit about why the reader should care about this research. How does this research address a critical issue or gap in the field?

Comment 3: The introduction is quite lengthy and covers a wide range of topics, from outsourcing to various scheduling problems and solution methods. It might benefit from a clearer structure and a smoother transition between different topics. Breaking it into subsections could help improve readability.

Comment 4: The introduction mentions the importance of optimal scheduling but doesn't explicitly state the specific objectives or research questions that the paper aims to address. Providing clear research objectives can help readers understand the scope and purpose of the study.

Comment 5: The paper briefly mentions the use of the Brown and Dacin [63] scale for evaluating CSR. It raises a valid concern about whether this scale accurately reflects consumers' understanding of CSR obligations. To strengthen this point, the section could provide specific examples or evidence from the study that illustrate potential discrepancies between the scale and consumers' perceptions.

Comment 6: The Experimental testing of the proposed GA section provides a clear description of the application's implementation details, including the programming language used (Delphi) and the database management system (Microsoft Access). This clarity is beneficial for readers who want to understand the technical aspects of the implementation.

Comment 7: The Experimental testing of the proposed GA mentions that execution times are given in minutes in the table. It's important to clarify whether there is an option for users to input execution times in different units (e.g., hours) or if the application exclusively uses minutes.

Comment 8: The discussion emphasizes the novelty of the optimization approach for capacity planning in SMEs. It would be helpful to explicitly state the unique contributions of the proposed approach, especially in comparison to existing methods or models in the literature.

Comment 9: The discussion section appropriately discusses the limitations of the provided model, such as the exclusion of production disturbances and the absence of intermediary warehouses. However, it would be more informative to discuss the potential impact of these limitations on real-world applications and whether they significantly affect the model's practicality.

Comment 10: The paper briefly mentions that addressing the excluded production disturbances could be a future research priority. It would be helpful to expand on this point by discussing other potential avenues for future research, such as further optimizing the algorithm, enhancing user-friendliness, or conducting real-world case studies to validate the model's effectiveness.

Moderate editing of the English language required

Author Response

Reviewer 1:

Dear reviewers, thank you for your detailed comments, below are our responses to them and the corresponding changes made to the paper. Responses are marked in red color, while modifications of the paper are marked in green color.

Comment 1: The introduction starts by discussing the impact of outsourced production and then shifts towards the effects of the COVID-19 pandemic on supply chains. While both topics are relevant, the purpose of the paper isn't immediately clear. The introduction could benefit from a more concise statement of the research problem or question.

Response: Authors thank the reviewer for this helpful suggestion. As suggested by the reviewer, the following text, which describes more precisely the research problem in this paper is added to the introduction section.

The main objective of this research, is to present a novel optimisation model to resolve capacity planning issues in SMEs based on the job shop scheduling for SMEs which sell a service to minimise the production time (makespan) in a certain period and at the same time to have the maximum profits for the SME. This objective is crucial for surviving on the market in the modern age. These two goals are often opposite in real systems and could create many problems during production planning.

Considering that the cost of working hour of the machine can be different for different machines, minimizing the makespan, does not necessarily mean that costs are at minimum i.e does not guarantiee maximum profits. In this paper, the authors emphasise the term relative makespan, which is a solution to the optimisation problem of minimum production time that guarantees maximum earnings. This model of optimal job scheduling for partial flexible job-shop problems, especially suitable for the SME that sells service in the metalworking industry, has not been considered in the available literature. With an innovative approach and a complete software solution applicable without modifications, the authors make available to SMEs a useful tool that they can use in everyday business and decision-making.

Comment 2: The introduction mentions the impact of the COVID-19 pandemic and its effects on SMEs, which is timely and relevant. However, it could be more explicit about why the reader should care about this research. How does this research address a critical issue or gap in the field?

Response: Authors thank to the reviewer for pointing out the importance to make the reader interested for the research. Since the sentences that mention of the supply chain and COVID-19 do not have the relevant role for the clarification of the paper purpose, they have been removed. In the response for the previous comment, in the text added to the introduction section reserach gap to which this paper refers is defined more precisely. The reader is introduced to the concept of relative makespan, i.e. minimization of production time, but in such a way that the profit is at maximum, taking into account only the cost of the working hour of the machines. In this way, the reader is referred to further study of the research.

Comment 3: The introduction is quite lengthy and covers a wide range of topics, from outsourcing to various scheduling problems and solution methods. It might benefit from a clearer structure and a smoother transition between different topics. Breaking it into subsections could help

Response: Authors thank to the reviewer for this observation. The introduction section is redesigned, some parts are omitted, other are transferred to section 2 Literature review. In this way, the introduction section is more readable.

Comment 4: The introduction mentions the importance of optimal scheduling but doesn't explicitly state the specific objectives or research questions that the paper aims to address. Providing clear research objectives can help readers understand the scope and purpose of the study.

Response: Authors thank to the reviewer for this comment. In the redesigned introduction section at the end of the section there is a clear statement of the research objectives, with a discussion of the possible application of theoretical and practical research results.

Comment 5: The paper briefly mentions the use of the Brown and Dacin [63] scale for evaluating CSR. It raises a valid concern about whether this scale accurately reflects consumers' understanding of CSR obligations. To strengthen this point, the section could provide specific examples or evidence from the study that illustrate potential discrepancies between the scale and consumers' perceptions

Response: Authors do not understand which part of the paper exactly this comment refers to because the mentioned reference is not mentioned in the paper.

Comment 6: The Experimental testing of the proposed GA section provides a clear description of the application's implementation details, including the programming language used (Delphi) and the database management system (Microsoft Access). This clarity is beneficial for readers who want to understand the technical aspects of the implementation.

Response: Authors thank to the reviewer for this comment.

Comment 7: The Experimental testing of the proposed GA mentions that execution times are given in minutes in the table. It's important to clarify whether there is an option for users to input execution times in different units (e.g., hours) or if the application exclusively uses minutes.

Response: Authors thank to the reviewer for this observation. In this stage of the research, the application use only minutes are there is no option for other time units. However, in a future upgrades of the application, the option of different time units will be provided. In the Experimental testing section the following statement is added regarding the different time units.

In this stage of the present research execution times are intended to be entered in minutes, while the option of entering them in other time units will be provided in the future upgrade of the application.

Comment 8: The discussion emphasizes the novelty of the optimization approach for capacity planning in SMEs. It would be helpful to explicitly state the unique contributions of the proposed approach, especially in comparison to existing methods or models in the literature.

Response: Many thanks to the reviewer for this helpful suggestion. In the Discussion and Conclusion section the following text is added which explicitly state the unique contribution of the proposed approach.

The proposed approach, unlike most other models in the literature, takes into account the average cost price of production per unit of time and thus gives the possibility of determining the makespan when the production costs are average, i.e. so that there is always a profit. If the cost price of the working hour of the machine is not takes into account, it can happen that the makespan is minimal, but the earnings are minimal or even in extreme cases non-existent, because most of the operations in such a schedule are performed on machines whose working hour cost is high, and thus do not provide financial resource for survival on the market.

Comment 9: The discussion section appropriately discusses the limitations of the provided model, such as the exclusion of production disturbances and the absence of intermediary warehouses. However, it would be more informative to discuss the potential impact of these limitations on real-world applications and whether they significantly affect the model's practicality.

Response: As the reviewer correctly noted, in the part related to the discussion, there is a lack of reference to practical applicability of the proposed approach regarding to the model’s limitations. In the mentioned section, the following text is added:

The most common disruptions in production, such as a machine breakdown, or an urgent purchase order, do not greatly affect the practical applicability of the model. The proposed model is flexible and allows variations on a daily basis. A break point on the timeline in the operation schedule caused by, for example, a machine failure becomes the starting point of a new operation schedule in which the defective machine is removed from the schedule until it is repaired, so that the production process continues according to the newly formed operation schedule. The lack of interoperational warehouses, however, limits the practical applicability of the proposed model in real conditions, since waiting time, transportation time, and storage costs are not taken into account.

Comment 10: The paper briefly mentions that addressing the excluded production disturbances could be a future research priority. It would be helpful to expand on this point by discussing other potential avenues for future research, such as further optimizing the algorithm, enhancing user-friendliness, or conducting real-world case studies to validate the model's effectiveness.

Response: In accordance with the remark of the reviewer, the part related to future research directions is expanded with the following text:

 The second most important goal of future research is testing the proposed model in real conditions and testing its efficiency and effectiveness, as well as further improving the algorithm based on the results obtained from the real-world case study.

 

 

Author Response File: Author Response.docx

Reviewer 2 Report

The article used the Genetic algorithm to solve small and medium-sized businesses' job shop scheduling problems. Despite being well-written, the authors should answer the following main issues

The article has a very limited novelty. The only mentioned novelty is the DPPX operator, which is just a modified version of PPX for this particular problem.

How do the authors distinguish between the small and big-size businesses? Why are results affected by the size?

In most cases, the GA showed no or even marginal improvement over the other algorithms, how this can be justified?

The manuscript is very long. Please omit the unnecessary sections. For example, the definition of GA operations, the discussion on the application's GUI, and most of the pseudo-codes can be removed.

There are several repetitions in the manuscript. For example, section 2.1 and section 2.2 are very similar.

Author Response

Reviewer 2:

Dear reviewers, thank you for your detailed comments, below are our responses to them and the corresponding changes made to the paper. Responses are marked in red color, while modifications of the paper are marked in green color.

Comment 1. The article has a very limited novelty. The only mentioned novelty is the DPPX operator, which is just a modified version of PPX for this particular problem.

Response: Authors thank to the reviewer for this observation. In addition to the DPPX operator, the proposed model, unlike most other models in the literature, takes into account the average cost price of production per unit of time and thus gives the possibility of determining the makespan when the production costs are average, i.e. so that there is always a profit. If the cost price of the working hour of the machine is not takes into account, it can happen that the makespan is minimal, but the earnings are minimal or even in extreme cases non-existent, because most of the operations in such a schedule are performed on machines whose working hour cost is high, and thus do not provide financial resource for survival on the market.

Comment 2. How do the authors distinguish between the small and big-size businesses? Why are results affected by the size?

Response: Small and big-size businesses are distinguished by the number of operations and machines as mentioned and the part related to the literature review as follows: The smaller issiues can range from nine to twenty jobs and can use anywhere from four to ten machines. Medium-sized issues might range anywhere from 25 to 40 jobs and use anywhere from 15 to 20 machines. When dealing with large-scale issues, the number of jobs can range anywhere from 45 to 60, and the number of machines can range anywhere from 25 to 30. The following sentence has been added in the introduction section.

The proposed model does not take into account the existence of interoperation warehouses, production disruptions, and urgent purchase orders, which in the case of large companies has a greater impact on the results.

Comment 3. In most cases, the GA showed no or even marginal improvement over the other algorithms, how this can be justified?

Response: In the Table 11 and Table 12 in section Experimental testing the results of the algorithm testing are presented compared to the other existing algorithm The results of both series of experiments clearly show that RMGA is very effective on small and medium-scale problems, and its effectiveness decreases slightly with large scale problems. In seven of the ten instance problems considered, RMGA outperforms the best known lower bound. The improvements are small compared to the other algorithms, as the reviewer correctly noted, but the objectives of the presented research was not to find the best algorithm but to include the financial component, i.e. the average cost of the working hour of the machine in order to find a solution to the optimisation problem of minimum production time that guarantees maximum earnings

 

Comment 4. The manuscript is very long. Please omit the unnecessary sections. For example, the definition of GA operations, the discussion on the application's GUI, and most of the pseudo-codes can be removed.

Response: Thanks to the reviewer for this comment. Introduction section has been divided onto Introduction and Literature review section, with Literature section significant reduction. Also, definitions of GA operations, the discussion on the application's GUI, and most of the pseudo-codes have been removed.

Comment 5. There are several repetitions in the manuscript. For example, section 2.1 and section 2.2 are very similar.

Response: Thanks to the reviewer for this comment. Section 2.1 is ommited from manuscript, and the other subsections form section 2 (now section 3) are renumbered accordingly.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

All my comments have been answered.