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Performance Prediction and Preselection for Optimization and Heuristic Solution Procedures

Author

Listed:
  • Nicholas G. Hall

    (Fisher College of Business, The Ohio State University, Columbus, Ohio 43210)

  • Marc E. Posner

    (Integrated Systems Engineering, The Ohio State University, Columbus, Ohio 43210)

Abstract

The operations research literature contains numerous studies on the design and application of optimization and heuristic solution procedures. These studies identify a particular optimization problem, suggest a general solution procedure, and then customize that procedure to improve its efficiency and/or accuracy. In contrast, this paper shows how to use existing solution procedures more effectively. We develop a methodology for predicting the relative performance of alternative procedures, using easily computed problem characteristics. This methodology enables us, for any given data set, to preselect a solution procedure. We apply this preselection methodology to the 0-1 knapsack problem for which two successful optimization procedures, dynamic programming and branch-and-search, are available. Extensive computational testing indicates that substantial savings in average computation time are achieved. The benefits of our work include faster and cheaper identification of effective solution procedures, as well as an improved understanding of the relationship between problem characteristics and the performance of various procedures. Our methodology can be applied to many optimization problems to develop easily implemented guidelines for selecting appropriate solution procedures.

Suggested Citation

  • Nicholas G. Hall & Marc E. Posner, 2007. "Performance Prediction and Preselection for Optimization and Heuristic Solution Procedures," Operations Research, INFORMS, vol. 55(4), pages 703-716, August.
    • Handle: RePEc:inm:oropre:v:55:y:2007:i:4:p:703-716
    DOI: 10.1287/opre.1070.0398
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    References listed on IDEAS

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    1. Silvano Martello & Paolo Toth, 1984. "A Mixture of Dynamic Programming and Branch-and-Bound for the Subset-Sum Problem," Management Science, INFORMS, vol. 30(6), pages 765-771, June.
    2. Silvano Martello & Paolo Toth, 1988. "A New Algorithm for the 0-1 Knapsack Problem," Management Science, INFORMS, vol. 34(5), pages 633-644, May.
    3. Nicholas G. Hall & Marc E. Posner, 2001. "Generating Experimental Data for Computational Testing with Machine Scheduling Applications," Operations Research, INFORMS, vol. 49(6), pages 854-865, December.
    4. Silvano Martello & Paolo Toth, 1997. "Upper Bounds and Algorithms for Hard 0-1 Knapsack Problems," Operations Research, INFORMS, vol. 45(5), pages 768-778, October.
    5. Joseph Adams & Egon Balas & Daniel Zawack, 1988. "The Shifting Bottleneck Procedure for Job Shop Scheduling," Management Science, INFORMS, vol. 34(3), pages 391-401, March.
    6. C. N. Potts & L. N. Van Wassenhove, 1988. "Algorithms for Scheduling a Single Machine to Minimize the Weighted Number of Late Jobs," Management Science, INFORMS, vol. 34(7), pages 843-858, July.
    7. Richard M. Karp, 1977. "Probabilistic Analysis of Partitioning Algorithms for the Traveling-Salesman Problem in the Plane," Mathematics of Operations Research, INFORMS, vol. 2(3), pages 209-224, August.
    8. Egon Balas & Eitan Zemel, 1980. "An Algorithm for Large Zero-One Knapsack Problems," Operations Research, INFORMS, vol. 28(5), pages 1130-1154, October.
    9. David Pisinger, 1997. "A Minimal Algorithm for the 0-1 Knapsack Problem," Operations Research, INFORMS, vol. 45(5), pages 758-767, October.
    10. Nicholas G. Hall & Hichem Kamoun & Chelliah Sriskandarajah, 1997. "Scheduling in Robotic Cells: Classification, Two and Three Machine Cells," Operations Research, INFORMS, vol. 45(3), pages 421-439, June.
    11. V. Chvatal, 1979. "A Greedy Heuristic for the Set-Covering Problem," Mathematics of Operations Research, INFORMS, vol. 4(3), pages 233-235, August.
    12. David Pisinger, 1999. "Core Problems in Knapsack Algorithms," Operations Research, INFORMS, vol. 47(4), pages 570-575, August.
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    Cited by:

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