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Exact/Heuristic Hybrids Using rVNS and Hyperheuristics for Workforce Scheduling

  • Stephen Remde
  • Peter Cowling
  • Keshav Dahal
  • Nic Colledge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4446)

Abstract

In this paper we study a complex real-world workforce scheduling problem. We propose a method of splitting the problem into smaller parts and solving each part using exhaustive search. These smaller parts comprise a combination of choosing a method to select a task to be scheduled and a method to allocate resources, including time, to the selected task. We use reduced Variable Neighbourhood Search (rVNS) and hyperheuristic approaches to decide which sub problems to tackle. The resulting methods are compared to local search and Genetic Algorithm approaches. Parallelisation is used to perform nearly one CPU-year of experiments. The results show that the new methods can produce results fitter than the Genetic Algorithm in less time and that they are far superior to any of their component techniques. The method used to split up the problem is generalisable and could be applied to a wide range of optimisation problems.

Keywords

Genetic Algorithm Schedule Problem Local Search Variable Neighbourhood Variable Neighbourhood Search 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Stephen Remde
    • 1
  • Peter Cowling
    • 1
  • Keshav Dahal
    • 1
  • Nic Colledge
    • 1
  1. 1.MOSAIC Research Group, University of Bradford, Great Horton Road, Bradford, BD7 1DPUnited Kingdom

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