A benchmark dataset for the retail multiskilled personnel planning under uncertain demand

Tracking #: 797-1777

Authors:

	Name	ORCID
	César Augusto Henao	https://orcid.org/0000-0001-8253-5794
	Andrés Felipe Porto	https://orcid.org/0000-0003-1110-1547
	Virginia I. González	https://orcid.org/0000-0003-3676-4865

Responsible editor:

Tobias Kuhn

Submission Type:

Resource Paper

Abstract:

In this data article, we present and describe datasets designed to address multiskilled personnel assignment problems (MPAP) under uncertain demand. The data article introduces simulated datasets and a real dataset obtained from a retail store in Chile. The real dataset provides details on the structure of the store, including the number of departments and workers, the type of labor contract, the cost parameter values, and the average demand across all store departments. The simulated datasets, consisting of 18 categorized text files, were generated through Monte Carlo simulation to encapsulate information about the stochastic demand for store departments. These text files are classified based on: (i) type of sample (in-sample or out-of-sample), (ii) type of truncation method (zero-truncated or percentile-truncated), and (iii) demand coefficient of variation (5%, 10%, 20%, 30%, 40%, 50%). This categorization allows academics and practitioners to select the scenarios that meet with their specific research or application needs, increasing the flexibility and applicability of the datasets. In addition, researchers and practitioners can use these comprehensive real and simulated datasets to benchmark the performance of diverse optimization methods under uncertain demand, thereby ensuring robust multiskilling levels for similar MPAPs. Furthermore, we offer an Excel workbook with the capability to generate up to 10,000 demand scenarios for varying coefficients of variation in demand.

Manuscript:

ds-paper-797.docx

Supplementary Files (optional):

ds-supplementary-797-1281.pdf

ds-supplementary-797-1282.pdf

Previous Version:

A benchmark dataset for the retail multiskilled personnel planning under uncertain demand

Data repository URLs:

https://zenodo.org/records/10570229

Date of Submission:

Friday, January 26, 2024

Date of Decision:

Friday, February 16, 2024

Nanopublication URLs:

Decision:

Solicited Reviews:

Review #1 submitted on 16/Feb/2024

Review Details

Reviewer has chosen to be Anonymous

Overall Impression: Average
Suggested Decision: Accept
Technical Quality of the paper: Good
Presentation: Good
Reviewer`s confidence: Medium
Significance: Moderate significance
Background: Reasonable
Novelty: Limited novelty
Data availability: All used and produced data (if any) are FAIR and openly available in established data repositories
Length of the manuscript: The length of this manuscript is about right

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

Based on my comments, the authors have added information concerning employee absenteeism and learning and forgetting rates.

Reasons to accept:

The authors have addressed both of my comments.

Reasons to reject:

The extension of the dataset in terms of information on employee absenteeism and learning and forgetting rates is rather limited. I would have preferred to have concrete examples of, for example, when employees became absent in the past. This way, the dataset would allow to recreate scenarios that happened in the past and see directly compare to how it was solved in practice. Nevertheless, the given values can still be used to simulate absences and training.

Nanopublication comments:

Further comments:

Review #2 submitted on 16/Feb/2024

By Tobias Sprodowski ORCID logo

https://orcid.org/0000-0002-6792-5126

Review Details

Reviewer has chosen not to be Anonymous

Overall Impression: Average
Suggested Decision: Undecided
Technical Quality of the paper: Average
Presentation: Good
Reviewer`s confidence: High
Significance: Moderate significance
Background: Reasonable
Novelty: Limited novelty
Data availability: All used and produced data (if any) are FAIR and openly available in established data repositories
Length of the manuscript: The length of this manuscript is about right

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

The authors present a data set for a Chilean retail company's multi-skilled personnel assignment problem, completed with artificial data from a Monte Carlo simulation. While the presentation and literature discussion in this second version is more advanced and give more details, it is still unclear to me why the author stated that there are no sufficient benchmark data sets:

A short investigation brings up that such multi-skilled, multi-ressource problems data sets are widely known in aircraft maintenance (https://www.sciencedirect.com/science/article/pii/S0377221717310585), multi-skilled resource-constrained project scheduling problem as MSRCPSP (https://www.sciencedirect.com/science/article/pii/S0377221722004519#bib0018) and benchmark sets for the latter are widely available: https://www.projectmanagement.ugent.be/research/project_scheduling/mmrcpsp

In my opinion, these relations to MSRCPSP should be discussed as they are quite similar regarding the multiple-skills and constraint resource assignment. I think, a comparison and alignment to the latter benchmark platform would give the reader a better insight of the unique features of this dataset.

Reasons to accept:

The literature discussion and comparison to other datasets were improved and more elaborated. The model is presented in a more detailed and comprehensive way. The presentation of the results shows the most important features of the dataset.

Reasons to reject:

The authors coined the term MPAP, but neglected to look for similar approaches and existing benchmarks, which are already there. And it is a pity because the work on its own is good, but if a short search brings up similar benchmark sets, the conclusion from my side is, that the literature research was not carried out sufficiently.

Nanopublication comments:

Further comments:

RESPONSE TO REVIEWERS

Dear Tobias Kuhn, editors-in-chief, and reviewers,

We express our sincere gratitude for your insightful comments on our manuscript titled "A benchmark dataset for retail multiskilled personnel planning under uncertain demand" (#776-1756). We thank you for your invitation to prepare a revision of the manuscript that carefully and fully responds to all the points made by the reviewers. Based on the instructions provided, we have uploaded three documents: (i) a revised and unmarked version of the manuscript, (ii) a version of the manuscript with all changes highlighted (in red), and (iii) a document in which we have provided a point-by-point overview of all comments and our corresponding changes to the manuscript.

Throughout the revision process, we have carefully considered the feedback provided by the reviewers and editor. Some key changes implemented in the revised manuscript include: (i) Significant structural adjustments, including the addition of new sections absent in the previous version. (ii) A comprehensive discussion of the existing literature on personnel scheduling problems involving multiskilled employees in a retail environment, along with an explicit comparison of our datasets with those available in repositories or other published articles. These modifications highlight how our datasets address a significant gap in the literature: the lack of accessible datasets for solving Multiskilled Personnel Assignment Problems (MPAPs) in the context of uncertain demand in retail settings. (iii) Extensive revisions aimed at improving clarity and readability throughout the manuscript. We have rewritten numerous sentences and paragraphs to ensure linguistic accuracy and coherence. Additionally, at the Editor's suggestion, we utilized tools like DeepL to enhance grammar and overall text flow.

Finally, we sincerely appreciate the opportunity to submit this revised version. Thank you for your time and consideration.

2 Comments

meta-review by editor

Submitted by Tobias Kuhn on Fri, 02/16/2024 - 07:12

The authors need to expand the discussion of related work with respect to similar approaches and existing benchmarks, as indicated by one of the reviewers.

Tobias Kuhn (https://orcid.org/0000-0002-1267-0234)

Response to the editor and the reviewers

Submitted by César Augusto Henao on Fri, 03/01/2024 - 12:57

Dear Tobias Kuhn, editors-in-chief and reviewers,

Thank you for your feedback on our manuscript entitled "A benchmark dataset for retail multiskilled personnel planning under uncertain demand" (#797-1777). Based on the instructions provided, we have uploaded three documents: (i) a revised version of the manuscript in Pdf and Word files, (ii) all image files needed to produce the final paper, and (iii) a document containing the responses to the reviewers.

We have addressed the reviewer's comment by expanding the discussion of related work in the new version of the manuscript. Most importantly, we have added Subsection 2.1, which identifies a set of research articles that addressed PSPs with applications in industries other than retail, sometimes considering multiskilled workers and sometimes not. The list of articles is particularly valuable as all the articles listed in Table 1 are characterized by having an associated data repository with public access, thus providing valuable datasets for researchers or practitioners to conduct experiments and/or benchmarking.

We believe that this new subsection strengthens and expands the contribution of our data article by providing readers with a compilation of datasets that have already been published by other authors and can be used for experimentation or benchmarking in case studies other than retail.

Please let us know if there are any additional requirements.

Best regards,

César A. Henao

Data Science

A benchmark dataset for the retail multiskilled personnel planning under uncertain demand

Tracking #: 797-1777

Authors:

Responsible editor:

Submission Type:

Abstract:

Manuscript:

Supplementary Files (optional):

Previous Version:

Tags:

Data repository URLs:

Date of Submission:

Date of Decision:

Decision:

2 Comments

meta-review by editor

Response to the editor and the reviewers