• DocumentCode
    3020354
  • Title

    Software Project Rescheduling with Genetic Algorithms

  • Author

    Ge, Yujia

  • Author_Institution
    Coll. of Comput. Sci. & Inf. Eng., Zhejiang Gongshang Univ., Hangzhou, China
  • Volume
    1
  • fYear
    2009
  • fDate
    7-8 Nov. 2009
  • Firstpage
    439
  • Lastpage
    443
  • Abstract
    Rescheduling techniques have been proposed in the areas such as job shop problem in recent years by researchers. But it has not been widely explored in software project environment under uncertainty. This paper proposes a software project scheduling/rescheduling framework based on genetic algorithm. Our previous formulates software project scheduling and rescheduling situation as a multi-objective optimization problem. The proposed method will help a manager to do scheduling with the option he made to put the project back on track. Case studies by simulation data show the effectiveness of the rescheduling method in supporting decision making in a dynamic environment. This paper reports comprehensive experiments to determine the parameters of stability and efficiency. Empirical study is directed to balance the effect of stability and efficiency in different situations. Evaluations of possible impact of the available control options are also done in the model to support control decision making in software process.
  • Keywords
    decision making; genetic algorithms; software development management; decision making; genetic algorithms; job shop problem; multi-objective optimization problem; rescheduling techniques; software project rescheduling; Decision making; Financial management; Genetic algorithms; Job shop scheduling; Optimal control; Optimal scheduling; Processor scheduling; Project management; Software tools; Stability; genetic algorithm; rescheduling; software project;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-3835-8
  • Electronic_ISBN
    978-0-7695-3816-7
  • Type

    conf

  • DOI
    10.1109/AICI.2009.434
  • Filename
    5376259