A Genetic Algorithm approach to a Workforce Planning problem

Applied to Erasmus MC

Master thesis (2021)

Authors

A. Gamboa Hernandez Electrical Engineering, Mathematics and Computer Science

Contributors

J.H. Weber Discrete Mathematics and Optimization - EEMCS (mentor)
J.T. van Essen Discrete Mathematics and Optimization - EEMCS (mentor)
S. Hinrichs-Krapels Policy Analysis - TPM (coach)
C. van Wyk Erasmus MC (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Aline Gamboa Hernandez
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Published Date

25-11-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This thesis aimed to aid ErasmusMC in the workforce planning process of deciding how to implement training among the nursing personnel in order to build a more flexible workforce, so the hospital can be prepared for fluctuating and unexpected demand, while minimizing the costs of doing so.

To accomplish this, a mathematical model was developed taking into account the structure of the nursing personnel within the hospital, and the structure of how training among nurses is implemented. Since the planning was aimed to be done on a daily basis, the contractual obligations between nurses and the hospital had to be respected as well.

The resulted model was tested using real data provided by Erasmus MC, and with the mathematical optimization solver Gurobi. Consequently, a Genetic Algorithm (GA) approach was proposed; this heuristic accomplished to outperform Gurobi when applied to the biggest two instances considered in this project.