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The Trade Off Between Diversity and Quality for Multi-objective Workforce Scheduling

  • Peter Cowling
  • Nic Colledge
  • Keshav Dahal
  • Stephen Remde
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3906)

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.

Keywords

Genetic Algorithm Schedule Problem Precedence Constraint Schedule Priority Strength Pareto Evolutionary Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter Cowling
    • 1
  • Nic Colledge
    • 1
  • Keshav Dahal
    • 1
  • Stephen Remde
    • 1
  1. 1.MOSAIC Research GroupUniversity of BradfordBradfordGreat Britain

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