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Tabu Search Candidate List Strategies in Scheduling

  • Balasubramanian Rangaswamy
  • Anant Singh Jain
  • Fred Glover
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 9)

Abstract

Candidate list strategies form an important cornerstone of Tabu Search (TS), yet are often neglected in TS research. In this paper, we review candidate list construction principles and illustrate basic concepts through simple numerical examples from the resource-constrained scheduling domain. We also provide computational results which document that significant gains are made possible by intelligent implementations of candidate list strategies, even where other Tabu Search components are restricted to relatively simple levels.

Keywords

Schedule Problem Tabu Search Candidate List Choice Rule Good Move 
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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References

  1. [1]
    Baar, T., R Brucker and S. Knust, (1997). “Tabu-Search Algorithms for the Resource-Constrained Project Scheduling Problem, ” Technical Report, Universität Osnabrück.Google Scholar
  2. [2]
    Dell’Amico, M. and M. Trubian, (1993). “Applying tabu search to the job-shop scheduling problem,” Annals of Operations Research 41, pp. 231–252.CrossRefGoogle Scholar
  3. [3]
    Demeulemeester, E. and W. Herroelen, (1992). “ A branch-and-bound procedure for the multiple resource-constrained project scheduling problem,” Management Science 38, pp. 1803–1818.CrossRefGoogle Scholar
  4. [4]
    James, R. J. W., J. T. Buchanan (1997). “Performance Enhancements to Tabu Search for the Early/Tardy Scheduling Problem, ” To appear in the European Journal of Operational Research, Special Issue on Tabu Search.Google Scholar
  5. [5]
    Glover, F., (1992). “Ejection chains, Reference Structures and Alternating Path Methods for Traveling Salesman Problems,” University of Colorado. Shortened version published in Discrete Applied Mathematics 65, (1996), pp. 223–253.Google Scholar
  6. [6]
    Glover, F., (1996). “Tabu Search and Adaptive Memory Programming — Advances, Applications and Challenges, ” Interfaces in Computer Science and Operations Research. Barr, Helgason and Kennington, eds., Kluwer Academic Publishers.Google Scholar
  7. [7]
    Glover, F. and M. Laguna, (1997). Tabu Search,Kluwer Academic Publishers.Google Scholar
  8. [8]
    Glover, E (1997). Tabu Search, To appear in the European Journal of Operational Research, Special Issue on Tabu Search.CrossRefGoogle Scholar
  9. [9]
    Kolisch, R., A. Sprecher, A. Drexl, (1995). “Characterization and generation of a general class of resource-constrained project scheduling problems,” Management Science 41, pp. 1693–1703.CrossRefGoogle Scholar
  10. [10]
    Lokketangen, A. and Glover, F. “ Candidate list and exploration strategies for solving 0\ 1 MIP problems using a pivot neighborhood,” MIC-97 Second International Conference on Metaheuristics, Versailles, France, July 21–24, 1997.Google Scholar
  11. [11]
    Minghe Sun, J. E. Aronson, P. McKeown and D. Drinka (1997). “A Tabu Search Heuristic Procedure for the FCT Problem,” To appear in the European Journal of Operational Research, Special Issue on Tabu Search.Google Scholar
  12. [12]
    Mingozzi, A., V. Maniezzo, S. Ricciardelli and L. Bianco, (1994). “An exact algorithm for project scheduling with resource constraints based on a new mathematical formulation,” Technical Report n.32, Department of Mathematics, University of Bologna, Italy.Google Scholar
  13. [13]
    Morton and Pentico, (1993). Heuristic Scheduling Systems, Wiley Series in Engineering and Technology Management, New York.Google Scholar
  14. [14]
    Nowicki, E. and C. Smutnicki, (1996). “A Fast Taboo Search Algorithm for the job shop problem, ” Management Science 42, 797–813.CrossRefGoogle Scholar
  15. [15]
    Rego, C. (1997). “Relaxed Tours and Path Ejections for the Traveling Salesman Problems, ” Universidade Portucalense, Portgual, To appear in the European Journal of Operational Research.Google Scholar
  16. [16]
    Sampson, S. E. and E. N. Weiss, (1993). “ Local Search Techniques for the Generalized Resource Constrained Project Scheduling Problem, ”Naval Research Logistics 40\5,pp. 665–676.Google Scholar
  17. [17]
    Woodruff, D. L. and M. L. Spearman (1992). “ Sequencing and Batching for Two Classes of Jobs with Deadlines and Setup Times, ” Production and Operations Management, 1\1, pp. 87–102.Google Scholar

Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Balasubramanian Rangaswamy
    • 1
  • Anant Singh Jain
    • 2
  • Fred Glover
    • 3
  1. 1.Graduate School of Business and AdministrationUniversity of Colorado at BoulderBoulderUSA
  2. 2.Department of Applied Physics, Electrical and Mechanical EngineeringUniversity of DundeeDundeeScotland, UK
  3. 3.US West Chair in Systems ScienceUniversity of Colorado at BoulderBoulderUSA

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