Multiskilled personnel assignment problem under uncertain demand: A benchmarking analysis.

The personnel assignment problem in different service industries aims to minimize the staff surplus/shortage costs. However, uncertainty in the staff demand challenges the accomplishment of that objective. This research studies the personnel assignment problem considering uncertain demand and multiskilled workforce configured through a 2-chaining strategy. We develop a two-stage stochastic optimization (TSSO) approach to calculate the multiskilling requirements that minimize the training costs and the expected costs of staff surplus/shortage. Later, we evaluate and compare the performance of the TSSO approach solutions with the solutions of two alternative optimization approaches under uncertainty - robust optimization (RO) and closed-form equation (CF). These two alternative approaches were published in Henao et al. [1] and Henao et al. [2], respectively. In addition, we compare the performance of the TSSO approach solutions with the solution of the deterministic (DT) approach and the solutions of myopic multiskilling approaches. To make performance comparisons between the different approaches, we used both real and simulated data derived from a retail store operating in Chile. The results show that, for different demand variability levels, TSSO, RO, and CF always belong to the set of approaches with the lowest average total cost. That is, in this group, there are no statistical differences from one approach to another, so these approaches are the most cost-effective. We also provide insights to retail decision-makers for addressing two key aspects. First, the methodology allows to address two fundamental multiskilling issues: how much multiskilling to add and how it should be added. Second, it is provided understanding on how to select the most suitable approach according to the balance between the conservatism and the reliability associated with the solutions delivered by each approach. Finally, we identify some methodological challenges for future research, such as the evaluation of k-chaining strategies with k≥2.

A two-stage stochastic optimization model for the retail multiskilled personnel scheduling problem: a k-chaining policy with k≥2.

Considering an uncertain demand, this study evaluates the potential benefits of using a multiskilled workforce through a k-chaining policy with k≥2. For the service sector and, particularly for the retail industry, we initially propose a deterministic mixed-integer linear programming model that determines how many employees should be multiskilled, in which and how many departments they should be trained, and how their weekly working hours will be assigned. Then, the deterministic model is reformulated using a two-stage stochastic optimization (TSSO) model to explicitly incorporate the uncertain personnel demand. The methodology is tested for a case study using real and simulated data derived from a Chilean retail store. We also compare the TSSO approach solutions with the myopic approaches' solutions (i.e., zero and total multiskilling). The case study is oriented to answer two key questions: how much multiskilling to add and how to add it. Results show that TSSO approach solutions always report maximum reliability for all levels of demand variability considered. It was also observed that, for high levels of demand variability, a k-chaining policy with k≥2 is more cost-effective than a 2-chaining policy. Finally, to evaluate the conservatism level in the solutions reported by the TSSO approach, two truncation types in the probability density function (pdf) associated with the personnel demand were considered. Results show that, if the pdf is only truncated at zero (more conservative truncation) the levels of required multiskilling are higher than when the pdf is truncated at 5th and 95th percentiles (less conservative truncation).

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

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. This Data in Brief article is related to the published article "Hybrid flexibility strategy on personnel scheduling: Retail case study" [1]. 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 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. [1], 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.