Multiobjective Optimal Design of Sewerage Rehabilitation by Using the Nondominated Sorting Genetic Algorithm-II

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• 作者：Yu-Hao Lin ; Yi-Ping Chen ; Ming-Der Yang ; Tung-Ching Su
• 关键词：Sewerage rehabilitation ; Multi ; objective optimization ; Non ; dominated sorting genetic algorithm (NSGA ; II) ; Pareto surface (PS)
• 刊名：Water Resources Management
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：30
• 期：2
• 页码：487-503
• 全文大小：3,006 KB
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• 作者单位：Yu-Hao Lin (1)
  Yi-Ping Chen (2)
  Ming-Der Yang (3)
  Tung-Ching Su (4)
  
  1. Centre for Environmental Restoration and Disaster Reduction, National Chung Hsing University, 250 Kuo Kuang Rd, Taichung, 402, Taiwan
  2. Department of Business Administration, Da-Yeh University, 168 University Rd., Dacun, Changhwa, 515, Taiwan
  3. Department of Civil Engineering, National Chung Hsing University, 250 Kuokuang Rd., Taichung, 402, Taiwan
  4. Department of Civil Engineering and Engineering Management, National Quemoy University, 1 Da Xue Rd., Kinmen, 892, Taiwan
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geotechnical Engineering
  Meteorology and Climatology
  Civil Engineering
  Environment
  
• 出版者：Springer Netherlands
• ISSN：1573-1650

文摘

Application of multiobjective optimization in sewerage rehabilitation management is not widespread due to the limitation of data collection and complex optimization process. Thus, a few researches in literature focused on sewerage rehabilitation optimization, and only considered two-objective optimization usually between the service life and the direct cost instead of a social cost. A sewerage rehabilitation multiobjective optimization decision support system (SRMOS) was developed for sewerage rehabilitation management in this study. The nondominated sorting genetic algorithm-II was used to design a set of Pareto surfaces with desirable rehabilitation effectiveness at the lowest cost by providing optimal plans comprising a construction method and substitute material. The SRMOS was applied to a real sewerage system to provide tradeoff solutions for three conflicting objectives, which are minimizing rehabilitation cost, maximizing pipe service, and minimizing traffic disruption. Compared with the experts' manual estimation, the plan derived from the SRMOS enables saving nearly 20 % of the rehabilitation cost. The contours of the rehabilitation cost show the equivalent relation between the traffic disruption and service life of pipes. The results indicate that increasing the number of objectives can make up the drawback of cost hard to be quantified and can also facilitate deriving practical plans for reference in decision-making. Keywords Sewerage rehabilitation Multi-objective optimization Non-dominated sorting genetic algorithm (NSGA-II) Pareto surface (PS)