东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (2): 164-169.DOI: 10.12068/j.issn.1005-3026.2019.02.003

• 信息与控制 • 上一篇    下一篇

基于两层混合遗传算法的IT外包进度风险控制

王雷震1,2, 朱锦文1, 卢福强1,2, 汪定伟1   

  1. (1. 东北大学 信息科学与工程学院, 辽宁 沈阳110819; 2. 东北大学秦皇岛分校 管理学院, 河北 秦皇岛066004)
  • 收稿日期:2017-11-20 修回日期:2017-11-20 出版日期:2019-02-15 发布日期:2019-02-12
  • 通讯作者: 王雷震
  • 作者简介:王雷震(1965-),男,河北行唐人,东北大学副教授,博士; 汪定伟(1948-),男,江西彭泽人,东北大学教授,博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(71401027); 河北省自然科学基金资助项目(G2016501086); 中央高校基本科研业务费专项资金资助项目(N172304016); 河北省高等学校科学技术研究重点项目(ZD2016202).

IT Outsourcing Schedule Risk Control Based on Two-Level Hybrid Genetic Algorithm

WANG Lei-zhen1,2, ZHU Jin-wen1, LU Fu-qiang 1,2, WANG Ding-wei1   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China; 2. School of Management, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.
  • Received:2017-11-20 Revised:2017-11-20 Online:2019-02-15 Published:2019-02-12
  • Contact: LU Fu-qiang
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摘要: 针对IT(information technology)外包项目的两层进度风险控制优化问题,设计了两层混合遗传算法.该算法是在传统遗传算法中引入模拟退火和自适应机制,并结合优化问题的两层特点而设计的,能够克服传统遗传算法易于早熟、局部搜索能力较差的弱点.在算例分析中,首先分析了两层数学模型在IT外包项目进度风险控制中的管理意义,进而将两层混合遗传算法的仿真结果与两层粒子群优化算法和传统遗传算法的仿真结果进行比较,验证了改进算法的效率和有效性.

关键词: IT外包, 进度风险, 混合算法, 遗传算法, 模拟退火

Abstract: Focusing on the optimization problem of schedule risk control in information technology(IT)outsourcing project, a two-level hybrid genetic algorithm(TLHGA)is proposed. The TLHGA incorporates simulated annealing, adaptive mechanism and the two-level feature of optimization problem to improve the traditional genetic algorithm(TGA), which could overcome the shortcomings of TGA such as early mature and weak local searching ability. In the experimental analyses, the management meanings of the two-level mathematical model in IT outsourcing schedule risk control is analyzed. Next, the simulation results of TLHGA are compared with the TGA and two-level particle swarm optimization algorithm, which verifies the rationality and effectiveness of the improved algorithm.

Key words: IT outsourcing, schedule risk control, hybrid algorithm, genetic algorithm, simulated annealing

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