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Note---An Approximate Algorithm for Multidimensional Zero-One Knapsack Problems---A Parametric Approach

Author

Listed:
  • Jae Sik Lee

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Monique Guignard

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

A new approximate algorithm for multidimensional zero-one knapsack problems with all positive coefficients is presented. The procedure is controlled by three parameters which affect the tradeoff between solution quality and computation time and whose values are set by the users. For 48 test problems with 5 to 20 constraints and 6 to 500 variables, the solution found was on the average within 0.34% of the optimum and the computation time was the shortest compared with three other well-known heuristics.

Suggested Citation

  • Jae Sik Lee & Monique Guignard, 1988. "Note---An Approximate Algorithm for Multidimensional Zero-One Knapsack Problems---A Parametric Approach," Management Science, INFORMS, vol. 34(3), pages 402-410, March.
    • Handle: RePEc:inm:ormnsc:v:34:y:1988:i:3:p:402-410
    DOI: 10.1287/mnsc.34.3.402
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    Citations

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    Cited by:

    1. Dimitris Bertsimas & Ramazan Demir, 2002. "An Approximate Dynamic Programming Approach to Multidimensional Knapsack Problems," Management Science, INFORMS, vol. 48(4), pages 550-565, April.
    2. Sabah Bushaj & İ. Esra Büyüktahtakın, 2024. "A K-means Supported Reinforcement Learning Framework to Multi-dimensional Knapsack," Journal of Global Optimization, Springer, vol. 89(3), pages 655-685, July.
    3. Hanafi, Said & Freville, Arnaud, 1998. "An efficient tabu search approach for the 0-1 multidimensional knapsack problem," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 659-675, April.

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    Keywords

    programming: integer algorithms; heuristic;

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