A Hyperheuristic Approach to Scheduling a Sales Summit

  • Peter Cowling
  • Graham Kendall
  • Eric Soubeiga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2079)

Abstract

The concept of a hyperheuristic is introduced as an approach that operates at a higher lever of abstraction than current metaheuristic approaches. The hyperheuristic manages the choice of which lowerlevel heuristic method should be applied at any given time, depending upon the characteristics of the region of the solution space currently under exploration. We analyse the behaviour of several different hyperheuristic approaches for a real-world personnel scheduling problem. Results obtained show the effectiveness of our approach for this problem and suggest wider applicability of hyperheuristic approaches to other problems of scheduling and combinatorial optimisation.

keywords

hyperheuristics metaheuristics heuristics personnel scheduling local search choice function 

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References

  1. 1.
    Aickelin, U., Dowsland, K.: Exploiting Problem Structure in a Genetic Algorithm Approach to a Nurse Rostering Problem. J. Scheduling 3 (2000) 139–153MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Burke, E.K.: Cowling, P., De Causmaecker, P., Vanden Berghe, G.A.: Memetic Approach to the Nurse Rostering Problem. Int. J. Appl. Intell. to appearGoogle Scholar
  3. 3.
    Burke, E., De Causmaecker, P., Vanden Berghe, G.A.: Hybrid Tabu Search Algorithm for the Nurse Rostering Problem. Selected Papers of the 2nd Asia-Pacific Conference on Simulated Evolution and Learning (SEAL’ 98). Lecture Notes in Artificial Intelligence, Vol. 1585: Springer, Berlin Heidelberg New York (1998) 186–194Google Scholar
  4. 4.
    Back, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. IOP Publishing and Oxford University Press (1997)Google Scholar
  5. 5.
    Dodin, B., Elimam, A.A., Rolland, E.: Tabu Search in Audit Scheduling. Eur. J. Oper. Res. 106 (1998) 373–392MATHCrossRefGoogle Scholar
  6. 6.
    Dowsland, K.A.: Nurse Scheduling with Tabu Search and Strategic Oscillation. Eur. J. Oper. Res. 106 (1998) 393–407MATHCrossRefGoogle Scholar
  7. 7.
    Easton, F.F., Mansour, N.A.: Distributed Genetic Algorithm for Deterministic and Stochastic Labor Scheduling Problems. Eur. J. Oper. Res. 118 (1999) 505–523MATHCrossRefGoogle Scholar
  8. 8.
    Mladenovic, N., Hansen, P.: Variable Neighborhood Search. Comput. Oper. Res. 24 (1997) 1097–1100MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Hart, E., Ross, P., Nelson, J.: Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule. Evol. Comput. 6 (1998) 61–80CrossRefGoogle Scholar
  10. 10.
    Mason, A.J., Ryan, D.M., Panton. D.M.: Integrated Simulation, Heuristic and Optimisation Approaches to Staff Scheduling. Oper. Res. 46 (1998) 161–175MATHCrossRefGoogle Scholar
  11. 11.
    Meisels, A., Lusternik, N.: Experiments on Networks of Employee Timetabling Problems. Practice And Theory of Automated Timetabling II: Selected papers. Lecture Notes in Computer Science, Vol. 408. Springer, Berlin Heidelberg New York (1997) 130–155Google Scholar
  12. 12.
    Tsang, E., Voudouris, C.: Fast Local Search and Guided Local Search and their Application to British Telecom’s Workforce Scheduling Problem. Oper. Res. Lett. 20 (1997) 119–127MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Peter Cowling
    • 1
  • Graham Kendall
    • 1
  • Eric Soubeiga
    • 1
  1. 1.Automated Scheduling, Optimisation and Planning (ASAP) Research Group, School of Computer Science and Information TechnologyThe University of NottinghamNottinghamUK

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