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中国管理科学 ›› 2008, Vol. 20 ›› Issue (6): 123-129.

• 论文 • 上一篇    下一篇

多资源受限条件下工程集成管理优化问题研究

李强1, 张静2   

  1. 1. 唐山市曹妃甸投资公司项目管理部, 河北唐山 063000;
    2. 中国矿业大学管理学院, 江苏徐州 221116
  • 收稿日期:2008-06-11 修回日期:2008-12-01 出版日期:2008-12-31 发布日期:2008-08-20
  • 作者简介:李强(1983- ),男(汉族),江西吉安人,唐山市曹妮甸公司项目管理部,工程师,硕士,研究方向:工程项目管理优化与项目技术经济评价.

Research On Multi-Resource Constrained Project Integration Management Optimization Problem

LI Qiang1, ZHANG Jing2   

  1. 1. Project Management Department, Cao Fie Dian Investment Ltd.Co, Tangshan 063000, China; 2.School of Management, China University of Mining and Technology, Xuzhou 221116, China
  • Received:2008-06-11 Revised:2008-12-01 Online:2008-12-31 Published:2008-08-20

摘要: 本文建立了多资源受限条件下工程集成管理优化的模型,该模型的求解属于国际上公认的NP—hard难题之一,利用基于优先权的编码技术使得模型的求解成为可能,提出同时具有惯性权重和限定因子参数的改进版本微粒群算法,编制其matlab求解源程序,运用在以管道水平定向钻穿越工程为实例的集成管理优化模型中,微粒群算法程序在求解过程表现出了高效的搜索能力,获得了满意的优化结果。最后,着重讨论了在微粒群算法参数设计中微粒个体意识与集体意识的比较分析和微粒群种群规模与协同搜索能力的关系。

关键词: 多资源受限, 集成管理, NP-hard难题, 微粒群算法, 优先权的编码

Abstract: This paper set up multi-resource constrained o ptimization model of project integration management which belongs to international legalized NP(non-deterministic polynomial completeness)-hard problem,and by using priority-based encoding so that made solving the model possible.And then,this paper imported particle swarmoptimization and advised a new version of particle swarmoptimization which had both inertia weight and constriction factor parameters.The author wrote Matlab procedure and applied it in integration management optimization problem that took pipeline horizontal direction drilling project for example.Particle swarmoptimization performed high efficient search capability in solving process and gained approving optimum results.In the end,this paper put stress on analyzing particle individual consciousness versus collective consciousness and the relationship between particle swarmsize and ability of cooperation searching in particle swarmoptimization parameter design.

Key words: multi-resource constrained, integration management, NP-hard problem, PSO, priority-based encoding

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