Dealing with Uncertainty in the MRCPSP/Max Using Discrete Differential Evolution and Entropy
Round 1
Reviewer 1 Report
Interesting research, but there are some shortcomings.
In the Abstract is Differential Evolution (DE), but in the Keywords is Discrete Differential Evolution (DE). Why is it different named?
More recent studies should be added and the main motivation and contributions of the research should be better explained in the Introduction.
The Discussion section and future research directions are missing.
Line403 please move the web page into the Reference list.
Line 448 “However, not only are the results similar, they are no 447 larger than 11% when compared against the.” Against what?
Author Response
We have edited the wording and revised the content and structure to explain better the reviewers' valuable comments. Furthermore, we have highlighted all our corrections for clarity. Finally, we appreciate the reviewers' precious time and effort reviewing this paper. Please see the attached file for our reply.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The paper “Dealing with Uncertainty in the MRCPSP/max Using Discrete Differential Evolution and Entropy is quite interesting and has the potential to contribute to the methodological approaches to deal with uncertainty in project management. The authors have articulated the method to a certain level of detail and evaluated the working of the method by using an example. Also, they have tried to check the robustness and efficiency of the method by comparing it with a benchmarked model. While the paper has value, some concerns need to be resolved.
The authors have used the Corona virus Pandemic and consequent uncertainty in project management as a pretext. However, no evidence of the use of the Corona virus context was found in the paper. They have rather used a random example to evacuate and justify their method. If the authors are using the pretext of the Corona virus Pandemic, it would be worthwhile to present an example of a project management challenge related to the Corona Virus Pandemic than a random example as the uncertainties in that context could be different and specific to other normal projects (if at all!). If they want to use the random example, then the context of Pandemic and its uncertainty is redundant and should be avoided.
Relating to the literature and theoretical background of the study, a better alignment of discussion made in sections 1 and 2.1 concerning uncertainty is needed as both sections discussed uncertainty in project management (scheduling) but in isolation.
Also, they should clarify what is meant by “the concept of modes resembles real-life project management in a more realistic version”. (Lines 106-107)
Further, in addition to uncertainty, does not entropy cause oscillations. If so, it is not clear from the articulation that where and how they have taken oscillation into account. Oscillation might occur because of various influential factors such as availability of resources, competencies, etc. If it is not considered then it should be mentioned as a limitation.
They have adopted the MRCPSP/max method. While the multi-mode RCPSP/max (MRCPSP/max) is defined mathematically, it needs to be first defined conceptually for easy comprehension.
In the methods section, equation 8 is not clear- is it an equation or an expression? If it is an expression then it should be included in the text and should not be given the equation form. Moreover, the equations presented are not properly referenced in the text and their basis is not adequately clear.
Further, in the methodology section the first statement “In this study, project planners aim to minimize project makespan while maximizing resilience by searching for an optimal sequence of activities” is not clear. Is it an assumption by the authors for the conceptualisation of the study?
What is meant by population in statement i<population in the Pseudo code developed? (Line 277)
In section 3.2 on Mutation, what is the implication of the scaling factor (F)? How the value of F is selected? If it is arbitrarily selected in the first iteration (if so), does it get optimised and influence the feasible solutions? (Line 360-356 and Table 1]. The authors need to clarify such aspects for a better understanding of the method they have developed and how does it work. Similar clarity is needed in section 3.3 on Crossover.
The results section needs to be articulated more clearly to show why the authors consider their method is more robust and better and how it can handle uncertainty effectively compared to other methods.
Author Response
We have edited the wording and revised the content and structure to explain better the reviewers' valuable comments. Furthermore, we have highlighted all our corrections for clarity. Finally, we appreciate the reviewers' precious time and effort reviewing this paper. Please see the attached file for our reply.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
The authors have addressed the concerns raised to a certain extent. However, the implications, robustness and comparative advantages of the method should have been articulated in the conclusion section more clearly.
The paper needs better articulation to enhance readability.
Author Response
Please see the attachment
Author Response File: 
Author Response.docx
