By Tobias Sprodowski https://orcid.org/0000-0002-6792-5126

Review Details

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Reviewer has chosen not to be Anonymous

Summary of paper in a few sentences (summary of changes and improvements for second round reviews):

The authors present a multiskilled personal planning problem dataset from a home department company in Chile. Although the dataset might help to assess further planning algorithms and approaches, the aforementioned literature is not discussed appropriately and is mostly just enumerated (see first and second paragraph). Also previous work from the authors, where this dataset was already published, is not mentioned: https://doi.org/10.1016/j.dib.2020.106066

Therefore, the authors should mention the difference to their previous article as the dataset seems to be the same and is already available there.

Reasons to accept:

Real data and promising dataset

Reasons to reject:

No real discussion about former approaches, methods or datasets. Just enumerate them is not a scientific evaluation.
Duplicate from https://doi.org/10.1016/j.dib.2020.106066

Nanopublication comments:

Further comments:

By Pieter Smet https://orcid.org/0000-0002-3955-7725

Review Details

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Reviewer has chosen not to be Anonymous

Summary of paper in a few sentences (summary of changes and improvements for second round reviews):

The authors present two datasets for a personnel scheduling problem that involves multiple skills and training. The first dataset comes directly from a software company and presents real-world data. The second dataset is generated by the authors themselves. Having this dataset available may be helpful for other researchers working on similar problems. However, it would have been nice if additional information would have been present in the dataset, eg about employee availability (which is also stochastic) or learning rates. Moreover, the current presentation (specifically the text) is very poor in terms of grammar, which makes it sometimes hard to understand the paper.

Reasons to accept:

- dataset based on real-world data for a challenging personnel scheduling problem

Reasons to reject:

- limited data in the dataset itself (only demand, information regarding employee availability is missing)
- the text itself is of very poor quality which makes the paper hard to understand at times

Nanopublication comments:

Further comments:

Mistakes in the text that should be corrected:

-p2, 1st paragraph: 'Staffing requirements in this industry exhibits..' -> change to exhibit
-p2, 1st paragraph, sentence below: making AN effective workforce plan...
-p2, 2nd paragraph: 'Below, a list of articles is provided that have...
-p2: replace 'database' with dataset?
-p2, 4th paragraph, first sentence: remove 'in the paper written' and 'retail'
-p2, 4th paragraph: 'The database contains real and simulated data TAKEN from...'
-p2, 4th paragraph: 'randomly created' -> 'randomly generated'
-p2, enumeration near the bottom of the page: 'demand uncertain' -> change to 'demand uncertainty'
-p2, enumeration near the bottom of the page: 'The robust multiskilling levels' -> change to 'Robust multiskilling levels'
-p3, 1st paragraph: 'THE real dataset...'
-p3, 1st paragraph: 'Such that the Chilean company...' I do not understand this sentence. I suggest to rewrite.
-p3, 1st paragraph: 'a retail store is conformed by' I do not understand this sentence. I suggest to rewrite to avoid the word 'conformed'
-p3, 2nd paragraph: 'Also, it is provided a file named ‘real-data.txt’ with these sets and parameters written in A Mathematical Programming Language named AMPL' change to 'Also provided with these sets and parameters is a file named ‘real-data.txt’ written in the mathematical programming language AMPL'
-p3, 2nd paragraph: 'Lastly, a full explanation of how the mean demand all departments...' change to 'A complete explanation of how the mean demand for all departments...'
-p4, 1st paragraph: 'THE simulated datasets consists of...'
-p4, 2nd paragraph: 'To generate the in-sample data, Monte Carlo simulation (MCS) was used to randomly create...'
-p4, 2nd paragraph: 'Particularly, it was used a normal probability distribution...' change to 'More specifically, a normal probability distribution was used...'
-p4, 2nd paragraph: 'and for comparison purposes it was created two datasets.' change to '. To enable a comparison (BEWTEEN WHAT??), two datasets were created'.
-p4, 3rd paragraph: 'Similarly, for the out-of-sample data, it was again utilized a MCS to randomly create demand scenarios' change to 'Similarly, a MCS was also used to create random demand scenarios for the out-of-sample data.'
-p4, 3rd paragraph: 'it was created 10,000 demand scenarios ' change to '10,000 demand scenarios were created'

I stopped correct grammar mistakes here, but there are still many left. I strongly suggest the authors to make use of a proofreading service to further correct the text!

By Sahana Athreya https://orcid.org/0009-0005-6217-1328

Review Details

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Reviewer has chosen not to be Anonymous

Summary of paper in a few sentences (summary of changes and improvements for second round reviews):

The paper introduces a valuable contribution to the field of workforce optimization by presenting a comprehensive database designed to address Multiskilled Personnel Assignment Problems (MPAP) in the context of uncertain demand. The dataset includes both simulated and real-world data, offering a rich resource for researchers and practitioners seeking to benchmark and optimize various methods under conditions of demand uncertainty.
The real dataset, sourced from a Chilean retail store, provides essential information about the store's structure, including the number of departments and workers, labor contract types, cost parameter values, and average demand across all store departments. This real-world data adds a practical dimension to the dataset, making it particularly relevant for applications in retail workforce management.
The simulated dataset, generated through Monte Carlo simulations, further enhances the utility of the database. The inclusion of 18 text files categorized by type of sample, truncation method, and demand coefficient of variation provides a diverse set of scenarios for testing and evaluating different optimization approaches. This categorization allows academics and practitioners to select scenarios that align with their specific research or application needs, enhancing the flexibility and applicability of the dataset.
The paper not only offers datasets but also provides an Excel workbook capable of generating up to 10,000 demand scenarios with varying coefficients of variation. This feature adds an extra layer of practicality and scalability, allowing users to customize and generate demand scenarios tailored to their specific requirements.
The keywords associated with the paper, including multiskilling, personnel scheduling, retail, stochastic programming, and workforce flexibility, aptly capture the core themes and applications of the presented database. Researchers and practitioners in these domains will find this resource particularly valuable for testing and refining optimization methods in the face of uncertain demand scenarios.
In conclusion, the paper provides a commendable contribution to the field, offering a well-structured and versatile database for addressing multiskilled personnel assignment problems under uncertain demand. The inclusion of both real and simulated datasets, along with the Excel workbook for scenario generation, makes this resource a valuable tool for advancing research and practical applications in workforce optimization.

Reasons to accept:

1. Comprehensive Dataset:
- The paper presents a comprehensive dataset that combines real-world data from a Chilean retail store with simulated datasets. This dual-source approach enriches the dataset, providing a diverse and representative collection of scenarios for addressing multiskilled personnel assignment problems under uncertain demand.

2. Practical Relevance:
- The inclusion of a real-world dataset from a Chilean retail store adds practical relevance to the research. Information about the store's structure, labor contracts, and average demand enhances the applicability of the dataset to real-world workforce management scenarios, particularly in the retail industry.

3. Versatility for Benchmarking:
- The dataset's versatility for benchmarking diverse optimization methods is a strong point. Categorized simulated datasets, along with an Excel workbook capable of generating various demand scenarios, offer users flexibility in testing and evaluating different approaches under different conditions.

4. Scalability:
- The Excel workbook's capability to generate up to 10,000 demand scenarios with different coefficients of variation enhances the scalability of the dataset. This feature accommodates the diverse needs of researchers and practitioners, allowing for a broad range of experiments and analyses.

5. Relevance to Key Keywords:
- The paper aligns well with key keywords in the field, including multiskilling, personnel scheduling, retail, stochastic programming, and workforce flexibility. This alignment ensures that the paper addresses and contributes to essential themes within the broader domain.

6. Potential Impact on Research and Practice:
- The paper has the potential to significantly impact both academic research and practical applications in workforce optimization. Researchers can leverage the dataset to test and refine optimization methods, while practitioners can benefit from insights gained in addressing multiskilled personnel assignment problems under uncertain demand.

7. Contribution to Knowledge:
- By providing a database that encompasses both simulated and real-world scenarios, the paper contributes to the advancement of knowledge in the field. This resource fills a gap by offering a tool for researchers and practitioners to explore and address challenges related to workforce optimization in uncertain demand environments.

8. Clear Presentation of Resources:
- The paper effectively communicates the availability and structure of the dataset, making it accessible to potential users. The mention of the Excel workbook for scenario generation further adds to the clarity and usability of the resources provided.

9. Reproducibility and Transparency:
- The paper's inclusion of simulated datasets and details about the Monte Carlo simulation process contributes to the reproducibility of the research. This transparency enhances the credibility of the dataset and facilitates the validation of results by other researchers.

Considering these reasons, the paper demonstrates a strong contribution to the field and offers a valuable resource for both researchers and practitioners engaged in addressing multiskilled personnel assignment problems under uncertain demand.

Reasons to reject:

N/A

Nanopublication comments:

Further comments:

While the paper makes a valuable contribution to the field, there are some areas where improvements could be considered:
1. Clarity in Methodology Description:
• The paper could benefit from a more detailed and explicit explanation of the methodology used for Monte Carlo simulation to generate the simulated dataset. Providing step-by-step details would enhance the transparency of the simulation process for readers.
2. Data Validation and Quality Assurance:
• It would be beneficial to include information on data validation and quality assurance processes applied to both the real and simulated datasets. Ensuring the accuracy and reliability of the data is crucial for the credibility and usability of the database.
3. Discussion of Limitations:
• The paper lacks a discussion of potential limitations associated with the dataset. Acknowledging and addressing any constraints or shortcomings in the data, such as assumptions made during simulation or constraints in the real-world data, would provide a more balanced perspective for users.
4. Comparison with Existing Databases:
• While the paper mentions the utility of the dataset for benchmarking, a more thorough discussion comparing this database with existing datasets in the field would strengthen the paper. Highlighting the unique features and advantages of this dataset in comparison to others would enhance its appeal to researchers.
5. Illustrative Examples:
• Including illustrative examples or case studies demonstrating the application of the dataset in solving multiskilled personnel assignment problems under uncertain demand would provide practical insights for readers. This could help bridge the gap between the theoretical presentation and real-world application.
6. Future Directions and Use Cases:
• Including a section on potential future directions for research and practical applications based on the dataset would inspire further exploration in the field. This could involve suggesting specific research questions or highlighting industries beyond retail where the dataset could be applied.
Addressing these areas of improvement would enhance the overall quality and applicability of the paper, providing a more comprehensive resource for researchers and practitioners in the domain of multiskilled personnel assignment problems under uncertain demand.