Objective Functions for Minimizing Rescheduling Changes in Production Control

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

See the attached file

Comments for author File: Comments.pdf

Author Response

Thanks for the reviewer's work. We summarized our answers and modifications in the attached file and as below.

Reviewer:

The paper presents a method which can reschedule a previously schedule production plant in case of uncertain events. The rescheduling uses multiple objective functions. 6 objective functions are performance indicators and the 10 objective functions provide that minimal modification of the original plan. The authors use a modified version of one of their previous result, a multi-objective search method. They present a numerical example to show the efficiency of the proposed approach.

General comments:

What I am missing the most is a short description of the previous multi-objective search method we know only it follows the tabu search principle. Without it, the paper is not understandable alone. I suggest to extend the manuscript with the necessary information.

Authors: Thank you for your valuable comment. We agree that the paper originally lacked a self-contained description of the previously developed multi-objective search method. In response, we have revised the manuscript accordingly. Specifically, we have:

• Added the pseudocode of the algorithm to provide a clear overview of its structure and logic.
• Included a detailed explanation of the neighborhood modification operators used during the search process.
• Described the solution comparison model applied to guide the multi-objective selection mechanism.

We believe that these additions significantly improve the clarity and comprehensibility of the manuscript, making it more self-contained and accessible to readers unfamiliar with our previous work.

Reviewer: The novelty of the research is written only in lines 269-276 without any details. How it has been done? This part should be the center of the paper.

Authors: Thank you for your valuable comment. In the revised manuscript, we have extended the description of the proposed objective functions by detailing their computational logic to make their impact and role more transparent. Furthermore, we would like to emphasize that the novelty of our work is not limited to the list of new objective functions. The paper also presents the use  of the  relative comparison model for evaluating alternative schedules, and demonstrates the applicability and benefits of the proposed contributions through a detailed case study.

Reviewer: I also would like to read about the computational needs of the algorithm. How long does it take to reschedule the illustrative example?

Authors: Thank you for your comment. In response, we have supplemented the manuscript with the actual computational times required to solve the illustrative case study. These values are now included in the revised version to provide a clearer picture of the algorithm’s efficiency and practical applicability.

Reviewer: Comments:

Literatures [6-8] are review articles and they are 20+ year old. Newer papers should be added or replaced. – Authors: Thanks, changed.

Line 104: int in – Authors: Thanks, changed.

Figure 1. In the row of M4 there is an orange rectangle (changeover) there is a red one (late activity). How it is possible, if after this red rectangle there are green rectangles (timely activities) and there is no changeover?

Authors: Thank you for your observation. The situation shown in Figure 1 is indeed possible because the jobs are processed on the same machine with identical setup requirements. However, they have different due dates. The red rectangle represents a late job that will not be completed by its deadline, while the subsequent green jobs are completed on time according to their own due dates. Since no setup change is required between them, no additional changeover is shown.

Line 194: The text refers to light blue rectangle in Figure 2, which is not exist.

Line 195: The text refers to small rectangles in Figure 2, which are not exist.

Authors: There are two versions of the order-oriented Gantt chart used in the software. The simpler version displays only the orders, while the more detailed one also includes the individual jobs. The text refers to the second version, which is shown in Figure 5. The referenced description has been moved accordingly to accompany Figure 5.

Line 199: There is an unnecessary dot (.) after word finish . – Authors: Thanks, changed.

Line 470: The doi of this publication is 10.1007/978-3-662-03088-2 – Authors: Thanks, changed.

Line 505: The doi link does not work – Authors: Thanks, changed.

Line 507: The doi link points to a different publication – Authors: Thanks, changed.

Line 509: The doi link does not work (a dot is missing at the end) – Authors: Thanks, changed.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

1) Contribution Clarity in the Abstract and Introduction:
Both the abstract and the introduction sections need to make the article's contribution more explicit. The originality of the authors' work and how it improves the state of the art should be made clear. The abstract's clarity and impact would be greatly increased by including a brief synopsis of the study's goals, methods, and main conclusions.

2) Details of the Optimization Algorithm:
The name of the optimization algorithm that was employed to solve the issue should be mentioned by the authors. It is currently unknown if the algorithm was created by the authors or adapted an existing technique found in the literature.

3) "RESCHEDULING" uses capital letters:
The word "RESCHEDULING" appears at the start of the introduction in all capital letters. It should be made clear why this stylistic choice was made, whether it was a formatting error or meant to highlight a crucial idea.

4) Multi-objective Results Presentation:
The Pareto front and trade-offs between the six objectives are not visualized in the manuscript. It is highly recommended that authors use pairwise Pareto fronts (e.g., 2D plots) to illustrate to readers the trade-offs between the optimization approach's performance and its goals.

5) Results in Tabular Form:
The authors ought to provide thorough tables that provide an overview of the findings. Three representative solutions ought to be chosen for every test case or scenario and thoroughly examined.

6) Absence of descriptions of constraints:
The authors state that the model has ten constraints, but they don't explain what these constraints mean. Each constraint should be explained in detail in a separate section, along with a justification for its inclusion in the model based on domain-specific requirements or existing literature.

7) Managing Restrictions:
The formulation and integration of constraints into the optimization framework should be explained in the manuscript. Furthermore, it should be made explicit how constraint violations are handled, whether it be through constraint dominance, repair strategies, or penalty functions.

8) Evaluation of Comparative Performance:
The authors should evaluate the suggested method's performance against baseline techniques or other well-known optimization algorithms in order to confirm its efficacy. To prove the approach's superiority or suitability for the given problem, a comparative analysis is necessary.

9) Figure Readability and Quality:
The figures are currently of poor quality, making them difficult to read. Every figure should have better clarity and resolution.

Author Response

Thanks for the reviewer's work. We summarized our answers and modifications in the attached file and as below.

Reviewer: 1) Contribution Clarity in the Abstract and Introduction:

Both the abstract and the introduction sections need to make the article's contribution more explicit. The originality of the authors' work and how it improves the state of the art should be made clear. The abstract's clarity and impact would be greatly increased by including a brief synopsis of the study's goals, methods, and main conclusions.

Authors: Thank you for your helpful suggestion. In response, we have revised both the Abstract and the Conclusions sections in accordance with your recommendations. The updated paper now clearly outlines the goals, methodology, and key findings of the study. We have also emphasized the novel contributions of our research to make the originality and relevance of the work more explicit in the context of the existing literature.

 

Reviewer: 2) Details of the Optimization Algorithm:

The name of the optimization algorithm that was employed to solve the issue should be mentioned by the authors. It is currently unknown if the algorithm was created by the authors or adapted an existing technique found in the literature.

Authors: Thank you for your comment. We used a proprietary optimization method developed by the authors. The revised manuscript now includes detailed descriptions of this approach, including pseudocode and explanatory text. We elaborate on the search algorithm as well as the modifying operators used. Additionally, the mathematical formulation of our comparative evaluation model is also incorporated, providing a comprehensive understanding of the solution approach.

 

Reviewer: 3) "RESCHEDULING" uses capital letters:

The word "RESCHEDULING" appears at the start of the introduction in all capital letters. It should be made clear why this stylistic choice was made, whether it was a formatting error or meant to highlight a crucial idea.

Authors: Thank you for pointing this out. We initially believed that the use of all capital letters for “RESCHEDULING” at the beginning of the introduction was required by the journal’s formatting guidelines. However, we have now corrected it to use standard capitalization with only the first letter capitalized.

 

Reviewer: 4) Multi-objective Results Presentation:

The Pareto front and trade-offs between the six objectives are not visualized in the manuscript. It is highly recommended that authors use pairwise Pareto fronts (e.g., 2D plots) to illustrate to readers the trade-offs between the optimization approach's performance and its goals.

Authors: Thank you for your valuable comment. We would like to clarify that our approach does not rely on a Pareto-based multi-objective optimization framework. Instead, we employ a proprietary relative change-oriented model. The paper  focused specifically on minimizing deviations during rescheduling. The main aim of the paper is not to analyze trade-offs between production performance indicators, but to emphasize the importance of minimizing schedule changes in the rescheduling context. In our model, the individual objectives can be distinguished by assigned priorities, which allow effective management of trade-off relationships. However, fine-tuning these priorities is a complex task that could fill an entire paper on its own, and thus falls beyond the scope of the current manuscript.

 

Reviewer: 5) Results in Tabular Form:

The authors ought to provide thorough tables that provide an overview of the findings. Three representative solutions ought to be chosen for every test case or scenario and thoroughly examined.

Authors: Thank you for your suggestion. As previously mentioned, the primary focus of this paper is not to evaluate the efficiency or convergence of the algorithm itself; therefore, we do not provide detailed results from multiple runs. Instead, we demonstrate how the different groups of objective functions (the g-type objectives) influence the rescheduling outcomes. This was illustrated through the case study, where two scenarios were compared—R1 with the g-objectives incorporated and R2 without them. Certainly, a more detailed analysis of the method’s convergence behavior, parameter effects, and robustness could be the subject of a future dedicated study.

 

Reviewer: 6) Absence of descriptions of constraints:

The authors state that the model has ten constraints, but they don't explain what these constraints mean. Each constraint should be explained in detail in a separate section, along with a justification for its inclusion in the model based on domain-specific requirements or existing literature.

Authors: Thank you for your valuable comment. We would like to kindly clarify a potential misunderstanding regarding terminology. In our manuscript, the term "g-objectives" refers to specific objective functions introduced to measure schedule stability aspects, not to constraints. We understand that the term "constraints" might have caused some confusion, but these g-objectives are used to quantify the extent of changes during rescheduling rather than to impose hard limitations. We hope this clarification helps in better understanding our approach.  In the revised manuscript, we have extended the description of the proposed objective functions by detailing their computational logic to make their impact and role more transparent.

 

Reviewer: 7) Managing Restrictions:

The formulation and integration of constraints into the optimization framework should be explained in the manuscript. Furthermore, it should be made explicit how constraint violations are handled, whether it be through constraint dominance, repair strategies, or penalty functions.

Authors: Thank you for your valuable comment. As we clarified earlier, the elements referred to are not constraints but optimization objectives. To address this point, the revised manuscript now includes a comprehensive comparative and evaluation model that supports the multi-objective optimization framework. We hope that the supplemented version clarifies the essence of our approach and its distinction from constraint handling.

 

Reviewer: 8) Evaluation of Comparative Performance:

The authors should evaluate the suggested method's performance against baseline techniques or other well-known optimization algorithms in order to confirm its efficacy. To prove the approach's superiority or suitability for the given problem, a comparative analysis is necessary.

Authors: Thank you for your comment. We would like to emphasize that the primary aim of this paper is not to evaluate the efficiency of the search algorithm or to compare it with other optimization techniques. Rather, our goal is to highlight the importance of minimizing changes during rescheduling. To support this, we presented a detailed case study that demonstrates the impact of stability-oriented objective functions. A direct performance comparison with other algorithms is not feasible in this context, as existing methods do not incorporate or support the type of g-objectives introduced in our model.

 

Reviewer: 9) Figure Readability and Quality:

The figures are currently of poor quality, making them difficult to read. Every figure should have better clarity and resolution.

Authors: Thanks for the comment. We agree and we improved it.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I attached a file

Comments for author File: Comments.pdf

Author Response

Comment 1: Line 449: A space is missing after the dot.

Reply 1: Probably the link by line 445. Thanks, corrected.

Comment 2: Line 705: The doi link still points to a different publication

Reply 2: Probably the link by [18] by line 701. Thanks, corrected.

Comment 3: Line 509: The doi link does not work, a dot is missing at the end of the doi.https://doi.org/10.32968/psaie.2022.1.7 should be replaced by
https://doi.org/10.32968/psaie.2022.1.7.

Reply 3: Probably the link by [19] by line 703. Thanks, corrected.

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have adequately addressed all the previously raised comments and concerns. I believe that the article meets the standards of the journal and can be accepted in its current form.

Author Response

Thank You very much for Your work.