Dataset for solving a hybrid flexibility strategy on personnel scheduling problem in the retail industry

This dataset is related with the Data in Brief article entitled: “Dataset for solving a hybrid flexibility strategy on personnel scheduling problem in the retail industry”, which was published in Data in Brief Journal. This data article describes datasets from a home improvement retail store located in Santiago, Chile. The datasets have been developed to simultaneously solve a staffing and tour scheduling problem that incorporates flexible contracts and multiskilled staff. In turn, this Data in Brief article is related to the published article "Hybrid flexibility strategy on personnel scheduling: Retail case study" (Porto et al., 2019).

The datasets contain real, processed, and simulated data. Regarding the real and processed datasets, they are presented for three different store sizes (4, 5 or 6 departments). Real datasets include information about the employment-contract characteristics, cost parameters, and a forecast of the number of employees required in each department for each day of the week and each time period into which the operating day is divided. As regards to the data processed for the case study, they include the set of skill sets considering that the employees can be trained in a maximum of two store departments. Regarding the simulated datasets, they include information about the random parameter of staff demand in each store department. The simulated data are presented in 90 text files classified by: (i) Store size (4, 5 or 6 departments). (ii) Coefficient of variation (10, 20, 30%). (iii) Instance identification number (10 instances per scenario that resulted from combining the store sizes and coefficients of variation).

Researchers can use the datasets for benchmarking the performance of different approaches with the one presented by Porto et al. (2019), and in consequence, they can find solutions to the same (or similar) type of personnel scheduling problem. The dataset includes an Excel workbook that can be used to randomly generate staff demand instances according to a chosen coefficient of variation.

本数据集关联发表于《Data in Brief》期刊的题为"零售行业人员排班问题混合灵活性策略求解数据集"的研究简报。该数据简报针对智利圣地亚哥的一家家居装饰零售门店的数据集进行了详细说明。本数据集旨在同时解决包含弹性合约与多技能员工的人员配置与排班调度问题。此外，该《Data in Brief》简报还关联发表于2019年的研究论文"混合灵活性策略在人员排班中的应用：零售行业案例研究"（Hybrid flexibility strategy on personnel scheduling: Retail case study, Porto et al., 2019）。

本数据集包含真实数据、经处理数据与模拟数据三类。针对真实与经处理数据集，其按照三种不同门店规模（4个、5个或6个部门）进行划分。真实数据集涵盖了雇佣合约特征、成本参数，以及一周内每日、每日各营业时段内各部门所需员工数量的预测信息。针对本案例研究的经处理数据，则包含了技能集合信息，其中设定员工最多可接受两个门店部门的技能培训。至于模拟数据集，其涵盖了各门店部门的员工需求随机参数信息。模拟数据以90个文本文件形式呈现，分类依据如下：(i) 门店规模（4个、5个或6个部门）；(ii) 变异系数（10%、20%、30%）；(iii) 实例标识编号（结合门店规模与变异系数组合得到的每种场景下，均包含10个实例）。

研究人员可利用本数据集对不同方法与Porto等人2019年提出的方法进行性能基准测试，进而为同类（或相似）人员排班问题求解可行方案。本数据集包含一个Excel工作簿，可根据选定的变异系数随机生成员工需求实例。