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An Incorporation of the Fuzzy Greedy Search Heuristic With Evolutionary Approaches for Combinatorial Optimization in Operations Management

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  • Kaveh Sheibani

    (ORLab Analytics, Vancouver, Canada)

Abstract

Although greedy algorithms are important, nowadays it is well assumed that the solutions they obtain can be used as a starting point for more sophisticated methods. This paper describes an evolutionary approach which is based on genetic algorithms (GA). A constructive heuristic, so-called fuzzy greedy search (FGS) is employed to generate an initial population for the proposed GA. The effectiveness and efficiency of the proposed hybrid method are demonstrated on permutation flow-shop scheduling as one of the most widely studied hard combinatorial optimization problems in the area of operational research.

Suggested Citation

  • Kaveh Sheibani, 2017. "An Incorporation of the Fuzzy Greedy Search Heuristic With Evolutionary Approaches for Combinatorial Optimization in Operations Management," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 8(2), pages 58-72, April.
    • Handle: RePEc:igg:jaec00:v:8:y:2017:i:2:p:58-72
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