The trade off between diversity and quality for multi-objective workforce scheduling

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.

URI

http://hdl.handle.net/10454/2511

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.

Link to published version

http://www.springerlink.com

Type

Conference paper