What is it about?
I am pleased to share our new publication in the journal "Data Science." Our contribution focuses on the research areas of Personnel Scheduling and Workforce Flexibility. This work was carried out in collaboration with my colleagues Andrés Porto and Virginia González. Unlike our previous works, which focused on presenting Research Articles, this time we decided to write a Data Article. We recognized that despite the numerous research articles and solution methods outlined in the literature for addressing personnel scheduling problems (PSPs), there is a need for datasets that provide academics and practitioners with the necessary data to validate their mathematical models and/or benchmark their solutions against those reported by other studies.
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Why is it important?
Particularly, this data article offers a three fold contribution. First, our data article offers a Literature Review on research articles addressing PSPs in industries such as healthcare, transportation, service restaurants, software companies, and construction. This list of articles is particularly valuable because all the identified articles are associated with publicly accessible Data Repositories, offering valuable Datasets for researchers and practitioners to conduct experiments and/or benchmarking. Second, our data article offers an exhaustive Literature Review on research articles with case studies in Retail, specifically focusing on PSPs with multiskilled employees. Here, we also identify articles with publicly accessible Data Repositories and provide a detailed discussion on each repository's characteristics, including a description of the type of data each repository contains. Third, our data article introduces datasets for addressing multiskilled personnel assignment problems under uncertain demand. It includes both simulated and real datasets from a prominent retail store, which have been used in multiple of our published research articles, confirming their applicability and validity. Researchers and practitioners can use these datasets to benchmark the performance of various optimization methods under uncertain demand.
Read the Original
This page is a summary of: A benchmark dataset for the retail multiskilled personnel planning under uncertain demand, Data Science, June 2024, IOS Press,
DOI: 10.3233/ds-240060.
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Resources
Benchmarking dataset for multiskilled workforce planning with uncertain demand
These datasets are related to the Data Article entitled: “A benchmark dataset for the retail multiskilled personnel planning under uncertain demand”, submitted to the Data Science Journal. This data article describes datasets from a home improvement retailer located in Santiago, Chile. The datasets were developed to solve a multiskilled personnel assignment problem (MPAP) under uncertain demand. Notably, these datasets were used in the published article "Multiskilled personnel assignment problem under uncertain demand: A benchmarking analysis" authored by Henao et al. (2022). Moreover, the datasets were also used in the published articles authored by Henao et al. (2016) and Henao et al. (2019) to solve MPAPs. The datasets include real and simulated data. Regarding the real dataset, it includes information about the store size, number of employees, employment-contract characteristics, mean value of weekly hours demand in each department, and cost parameters. Regarding the simulated datasets, they include information about the random parameter of weekly hours demand in each store department. The simulated data are presented in 18 text files classified by: (i) Sample type (in-sample or out-of-sample). (ii) Truncation-type method (zero-truncated or percentile-truncated). (iii) Coefficient of variation (5, 10, 20, 30, 40, 50%).
Multiskilled personnel assignment problem under uncertain demand: A benchmarking analysis
This is the research article associated with this data article published in IOS Press
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