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Solving the bi-objective multidimensional knapsack problem exploiting the concept of core

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  • Mavrotas, George
  • Figueira, José Rui
  • Florios, Kostas

Abstract

This paper discusses the bi-objective multi-dimensional knapsack problem. We propose the refinement of the core concept that has already effectively been used in the single objective multi-dimensional knapsack. The core concept is based on the divide and conquer principle. Instead of solving the whole problem with n variables, several sub-problems with less than n variables are solved, in several variables which comprise the cores. The quality of the obtained solution can be adjusted according to the core size and there is always a balance between the solution time and quality. First, the core variables are defined, and subsequently the bi-objective integer program is solved, that comprises only the core variables using the Multicriteria Branch and Bound algorithm that generates the complete Pareto set. Small and medium sized examples are solved. Also, a very small example is used to illustrate the method while computational issues are also discussed.

Suggested Citation

  • Mavrotas, George & Figueira, José Rui & Florios, Kostas, 2009. "Solving the bi-objective multidimensional knapsack problem exploiting the concept of core," MPRA Paper 105087, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:105087
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    File URL: https://mpra.ub.uni-muenchen.de/105087/1/MPRA_paper_105087.pdf
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    References listed on IDEAS

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    1. Silvano Martello & Paolo Toth, 1988. "A New Algorithm for the 0-1 Knapsack Problem," Management Science, INFORMS, vol. 34(5), pages 633-644, May.
    2. Pisinger, David, 1995. "A minimal algorithm for the multiple-choice knapsack problem," European Journal of Operational Research, Elsevier, vol. 83(2), pages 394-410, June.
    3. Laumanns, Marco & Thiele, Lothar & Zitzler, Eckart, 2006. "An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method," European Journal of Operational Research, Elsevier, vol. 169(3), pages 932-942, March.
    4. Egon Balas & Eitan Zemel, 1980. "An Algorithm for Large Zero-One Knapsack Problems," Operations Research, INFORMS, vol. 28(5), pages 1130-1154, October.
    5. Pisinger, David, 1995. "An expanding-core algorithm for the exact 0-1 knapsack problem," European Journal of Operational Research, Elsevier, vol. 87(1), pages 175-187, November.
    6. Mavrotas, G. & Diakoulaki, D., 1998. "A branch and bound algorithm for mixed zero-one multiple objective linear programming," European Journal of Operational Research, Elsevier, vol. 107(3), pages 530-541, June.
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    Citations

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    Cited by:

    1. Rong, Aiying & Figueira, José Rui, 2013. "A reduction dynamic programming algorithm for the bi-objective integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 231(2), pages 299-313.
    2. Rong, Aiying & Figueira, José Rui, 2014. "Dynamic programming algorithms for the bi-objective integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 236(1), pages 85-99.
    3. Mavrotas, George & Florios, Kostas & Figueira, José Rui, 2015. "An improved version of a core based algorithm for the multi-objective multi-dimensional knapsack problem: A computational study and comparison with meta-heuristics," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 25-43.
    4. Wishon, Christopher & Villalobos, J. Rene, 2016. "Robust efficiency measures for linear knapsack problem variants," European Journal of Operational Research, Elsevier, vol. 254(2), pages 398-409.
    5. Bas, Esra, 2011. "An investment plan for preventing child injuries using risk priority number of failure mode and effects analysis methodology and a multi-objective, multi-dimensional mixed 0-1 knapsack model," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 748-756.

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    More about this item

    Keywords

    Combinatorial optimization; Branch-and-bound; Multi-objective programming; Multi-dimensional knapsack problems;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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