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

Kaveh Sheibani
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Kaveh Sheibani: ORLab Analytics, Vancouver, Canada

International Journal of Applied Evolutionary Computation (IJAEC), 2017, vol. 8, issue 2, 58-72

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jaec00:v:8:y:2017:i:2:p:58-72

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