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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/2511 |
| Date | January 2006 |
| Creators | Cowling, Peter I., Colledge, N.J., Dahal, Keshav P., Remde, Stephen M. |
| Publisher | Springer-Verlag |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Conference paper, Accepted Manuscript |
| Rights | © 2006 Springer-Verlag. Reproduced in accordance with the publisher's self-archiving policy. Original publication is available at http://www.springerlink.com. |
| Relation | http://www.springerlink.com |
Page generated in 0.002 seconds