APPLYING COMPUTATIONAL INTELLIGENCE APPROACHES TO THE STAFF SCHEDULING PROBLEM

Vasileios Perlis, Charilaos Akasiadis, Konstantinos Theofilatos, Grigorios N. Beligiannis, Spyridon D. Lykothanasis

2011

Abstract

Staff scheduling for public organizations and institutions is an NP-hard problem and many heuristic optimization approaches have already been developed to solve it. In the present paper, we present two meta-heuristic computational intelligence approaches (Genetic Algorithms and Particle Swarm Optimization) for solving the Staff scheduling problem. A general model for the problem is introduced and it can be used to express most of real-life preferences and employee requirements or work regulations and cases that do not include overlapping shifts. The Genetic Algorithm (GA) is parameterized, giving the user the opportunity to apply many different kinds of genetic operators and adjust their probabilities. Classical Particle Swarm Optimization (PSO) is modified in order to be applicable in such problems, a mutation operator has been added and the produced PSO variation is named dPSOmo (discrete Particle Swarm Optimization with mutation operator). Both methods are tested in three different cases, giving acceptable results, with the dPSOmo outperforming significantly the GA approach. The PSO variation results are very promising, encouraging further research efforts.

Paper Citation

in Harvard Style

Perlis V., Akasiadis C., Theofilatos K., N. Beligiannis G. and D. Lykothanasis S. (2011). APPLYING COMPUTATIONAL INTELLIGENCE APPROACHES TO THE STAFF SCHEDULING PROBLEM . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 168-173. DOI: 10.5220/0003669701680173

in Bibtex Style

@conference{ecta11,
author={Vasileios Perlis and Charilaos Akasiadis and Konstantinos Theofilatos and Grigorios N. Beligiannis and Spyridon D. Lykothanasis},
title={APPLYING COMPUTATIONAL INTELLIGENCE APPROACHES TO THE STAFF SCHEDULING PROBLEM},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)},
year={2011},
pages={168-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003669701680173},
isbn={978-989-8425-83-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)
TI - APPLYING COMPUTATIONAL INTELLIGENCE APPROACHES TO THE STAFF SCHEDULING PROBLEM
SN - 978-989-8425-83-6
AU - Perlis V.
AU - Akasiadis C.
AU - Theofilatos K.
AU - N. Beligiannis G.
AU - D. Lykothanasis S.
PY - 2011
SP - 168
EP - 173
DO - 10.5220/0003669701680173