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Multiobjective Resource Allocation Problems By Multistage Hybrid Genetic Algorithm



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學術與思想(二)







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商品訊息描述:

Multiobjective Resource Management Problems (m-RMP) involves deciding how to divide a resource of limited availability among multiple demands in a way that optimizes current objectives. RMP is widely used to plan the optimal allocating or management resources process among various projects or business units for the maximum product and the minimum cost. “Resources” might be manpower, assets, raw materials, capital or anything else in limited supply. The solution method of RMP, however, has its own problems; this book identifies four of them along with the proposed methods to solve them. Mathematical models combined with effective multistage Genetic Algorithm (GA) approach help to develop a method for handling the m-RMP. The proposed approach not only can solve relatively large size problems but also has better performance than the conventional GA. And the proposed method provides more flexibility to m-RMP model which is the key to survive under severely competitive environment. We also believe that the proposed method can be adapted to other production-distribution planning and all m-RAP models.
In this book, four problems with m-RMP models will be clearly outlined and a multistage hybridized GA method for finding the best solution is then implemented. Comparison results with the conventional GA methods are also presented. This book also mentions several useful combinatorial optimization models in process system and proposed effective solution methods by using multistage GA.

Note:Part of this book, once published in international journals SCI (Science Direct) inside, be accepted have five articles.

作者簡介:

林吉銘 (Chi-Ming Lin)

電子信箱:chiminglin.tw@gmail.com

學歷
日本國立兵庫教育大學 教育學碩士
日本早稻田大學資訊生產系統研究所5年研究
日本公立前橋工科大學工學研究所 工學博士

經歷
教育部 專員
國立台北教育大學 兼任講師
台北市立教育大學 兼任講師
中央警察大學 兼任講師
國立台南師範大學 兼任講師
美和技術學院 專任講師
長庚技術學院 專任講師
桃園縣公、私立托兒所 評鑑委員
開南大學 專任講師(現職)

商品訊息簡述:

  • 出版社:蘭臺網路

    新功能介紹
  • 出版日期:2012/10/01
  • 語言:英文


Multiobjective Resource Allocation Problems By Multistage Hybrid Genetic Algorithm

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