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Multi-skilled workforce scheduling with training and welfare considerations

Peña Diana A., Osorio Andrés F., Orejuela Juan P., Idarraga Juan C.

Engineering Management in Production and Services

2023

Vol. 15, no. 3

27--41

Flexibility in workforce scheduling in services is necessary to reduce the impact of demand uncertainty, absenteeism, and desertion while maintaining high service levels. This paper studies the workforce scheduling problem, including multiple skill accumulation, training, and welfare, as well as flexibility for employees and the company. All these elements are modelled and included in a mixed-integer linear programming (MILP) model that maximises their accumulated skill level. A real case study based on the scheduling of lab assistants to laboratory practices at a university in Colombia is used to generate numerical experiments. Different experiments were conducted, and the results show that the level of skill achieved is highly sensitive to the number of assistants and the number of allocations. The experiments also showed that, while keeping the same number of lab assistants, it is possible to include flexibility and welfare constraints. Finally, the proposed model can generate schedules that achieve high levels of skills and meet the different constraints of the model, including balance, accumulation, demand and welfare.

The Extended Shift Minimization Personnel Task Scheduling Problem

Kyngäs Nico, Nurmi Kimmo

Annals of Computer Science and Information Systems

2021

Vol. 26

65--74

In workforce scheduling, shift generation is the process of determining the shift structure, along with the tasks to be carried out in particular shifts. Application areas of shift generation include hospitals, retail stores, contact centers, cleaning, home care, guarding, manufacturing and delivery of goods. We present an extension to the Shift Minimization Personnel Task Scheduling Problem that is a problem in which a set of tasks with fixed start and finish times have to be allocated to a heterogeneous workforce. The objective in the SMPTSP is to minimize the number of employees required to carry out the given set of tasks. In the ESMPTSP, another objective is to maximize the number of feasible (shift, employee) pairs. We provide a mathematical formulation of the extended problem. We present an efficient ruin and recreate heuristic along with computational results for existing SMPTSP data sets and to a new data set. The presented heuristic is suitable for application in large real-world scenarios. The new instances, along with our best solutions, have been made available online.