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The trade off between diversity and quality for multi-objective workforce scheduling
Cowling, Peter I. ; Colledge, N.J. ; Dahal, Keshav P. ; Remde, Stephen M.
Cowling, Peter I.
Colledge, N.J.
Dahal, Keshav P.
Remde, Stephen M.
Publication Date
2006
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© 2006 Springer-Verlag. Reproduced in accordance with the publisher's self-archiving policy. Original publication is available at http://www.springerlink.com.
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Abstract
In this paper we investigate and compare multi-objective and
weighted single objective approaches to a real world workforce scheduling
problem. For this difficult problem we consider the trade off in solution quality
versus population diversity, for different sets of fixed objective weights. Our
real-world workforce scheduling problem consists of assigning resources with
the appropriate skills to geographically dispersed task locations while satisfying
time window constraints. The problem is NP-Hard and contains the Resource
Constrained Project Scheduling Problem (RCPSP) as a sub problem. We investigate
a genetic algorithm and serial schedule generation scheme together with
various multi-objective approaches. We show that multi-objective genetic algorithms
can create solutions whose fitness is within 2% of genetic algorithms using
weighted sum objectives even though the multi-objective approaches know
nothing of the weights. The result is highly significant for complex real-world
problems where objective weights are seldom known in advance since it suggests
that a multi-objective approach can generate a solution close to the user
preferred one without having knowledge of user preferences.
Version
Accepted Manuscript
Citation
Cowling, P. I., Colledge, N. J., Dahal, K. P. and Remde, S. M. (2006)
The trade off between diversity and quality for multi-objective workforce
scheduling. In: Evolutionary computation in combinatorial optimization.
Proceedings of the 6th European Conference (EvoCOP 2006) Budapest,
Hungary, April 10-12, 2006. pp 13-24.
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Conference paper