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

  • Authors:

  • Peter Cowling;Nic Colledge;Keshav Dahal;Stephen Remde

  • Affiliations:

  • MOSAIC Research Group, University of Bradford, Bradford, Great Britain;MOSAIC Research Group, University of Bradford, Bradford, Great Britain;MOSAIC Research Group, University of Bradford, Bradford, Great Britain;MOSAIC Research Group, University of Bradford, Bradford, Great Britain

  • Venue:
  • EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
  • Year:
  • 2006

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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.