南京林业大学学报(自然科学版) ›› 2012, Vol. 36 ›› Issue (06): 125-129.doi: 10.3969/j.jssn.1000-2006.2012.06.025

• 研究论文 • 上一篇    下一篇

公路施工网络计划多目标遗传优化

廖小辉1,2,黄新2*,陈磊3   

  1. 1.衢州学院建筑工程学院,浙江衢州324000;
    2.南京林业大学土木工程学院,江苏南京210037;
    3.衢州学院信息与电子工程学院, 浙江衢州324000
  • 出版日期:2012-11-30 发布日期:2012-11-30
  • 基金资助:
    收稿日期:2011-10-18修回日期:2012-04-24
    基金项目:浙江省自然科学基金项目(Y1100210);衢州市科技局项目(20112101);高等学校博士学科重点专项科研基金资助项目(20093204110008)
    第一作者:廖小辉,讲师,博士生。*通信作者:黄新,教授。Email: huangxin@njfu.com.cn。
    引文格式:廖小辉,黄新,陈磊. 公路施工网络计划多目标遗传优化[J]. 南京林业大学学报:自然科学版,2012,36(6):125-129.

Multiobjective genetic optimization of highway construction network plan

LIAO Xiaohui1,2, HUANG Xin2*, CHEN Lei3   

  1. 1. College of Civil Engineering and Architecture, Quzhou College, Quzhou 324000, China;
    2.College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China;
    3. College of Electronic and Information Engineering, Quzhou Colle
  • Online:2012-11-30 Published:2012-11-30

摘要: 通过对公路施工网络计划优化方法进行分析,建立了符合公路施工网络计划特点的质量-工期-费用的优化模型;利用拥挤度计算和非劣排序,以及精英保留策略的遗传算法,并采用工序染色体编码的方法,缩小了公路施工网络计划优化程序对有效解的搜索空间;通过轮盘赌选择、算术交叉、变异等操作,得到一个Pareto最优解集,供决策者从中选择出最符合实际情况的方案;提出了改进的NSGA-Ⅱ多目标优化方法。通过工程实例,采用改进的NSGA-Ⅱ对施工方案进行优化,利用MATLAB 7.0编程仿真,可获得Pareto的最优解集。

Abstract: Through the analysis of network planning optimization method for highway construction, the qualitydurationcostoptimization model with the characteristics of network planning for highway construction was established. The multiobjective optimization method based on NSGAⅡ was put up. The method was GA that using nondominated sort about crowding distance and elitism strategy. Through chromosome coding based on process, the search space of solution was optimized. Through the roulette wheel selection, arithmetic crossover and mutation operation, the Pareto optimal solution collection, that could allow decisionmakers to choose, was got. At last, through engineering examples, this paper confirmed that the optimization method could solve the Pareto optimal solution collection from establishing the optimization model, using MATLAB 7.0 simulation program.

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