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Authors: Vasileios Perlis 1 ; Charilaos Akasiadis 1 ; Konstantinos Theofilatos 1 ; Grigorios N. Beligiannis 2 and Spyridon D. Lykothanasis 1

Affiliations: 1 University of Patras, Greece ; 2 Univ. of Western Greece, Greece

ISBN: 978-989-8425-83-6

Keyword(s): Staff scheduling, Heuristics, Genetic algorithm, Particle swarm optimization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Hybrid Systems ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Soft Computing ; Swarm/Collective Intelligence

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. (More)

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Paper citation in several formats:
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

@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},
}

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

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