Multi-Objective Optimization for Site Location Problems through Hybrid Genetic Algorithm with Neural Networks

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With the aim of developing a flexible optimization method for managing conflict resolution, this paper concerns itself with site location problems under multi-objectives. As known from the term NIMBY (Not In My Back Yard), disposal site location problems of hazardous waste is an eligible case study associated with environmental and economic concerns. After describing the problem generally as a multi-objective mixed-integer program, we have proposed an intelligence supported approach that extends the hybrid genetic algorithm developed by the author to derive the best-compromise solution. For this purpose, we have developed a novel modeling method of value function using neural networks, and incorporated it into the approach. As a result, we can provide a practical and effective method in which the hybrid strategy maintains its advantages of relying on good matches between the solution methods and the problem properties such as a genetic algorithm for unconstrained discrete optimization and a mathematical program for constrained continuous ones. Finally, by taking an example formulated as a multi-objective mixed-integer linear program, we have examined the effectiveness of the proposed approach numerically.

収録刊行物

  • Journal of chemical engineering of Japan  

    Journal of chemical engineering of Japan 32(1), 51-58, 1999-02 

    The Society of Chemical Engineers, Japan

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被引用文献:  7件

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各種コード

  • NII論文ID(NAID)
    10002067093
  • NII書誌ID(NCID)
    AA00709658
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    00219592
  • NDL 記事登録ID
    4664935
  • NDL 雑誌分類
    ZP1(科学技術--化学・化学工業)
  • NDL 請求記号
    Z53-R395
  • データ提供元
    CJP書誌  CJP引用  NDL  J-STAGE 
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