• DocumentCode
    239176
  • Title

    Heuristic optimization for software project management with impacts of team efficiency

  • Author

    Nanlin Jin ; Xin Yao

  • Author_Institution
    Dept. of Comput. Sci. & Digital Technol., Northumbria Univ., Newcastle upon Tyne, UK
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    3016
  • Lastpage
    3023
  • Abstract
    Most of the studies on project scheduling problems assume that every assigned participant or every team of the same number of participants, completes tasks with an equal efficiency, but this is usually not the case for real world problems. This paper presents a more realistic and complex model with extra consideration on team efficiency which are quantitatively measured on employee-task assignment. This study demonstrates the impacts of team efficiency in a well-studied software project management problem. Moreover, this study illustrates how a heuristic optimization method, population-based incremental learning, copes with such added complexity. The experimental results show that the resulting near optimal solutions not only satisfy constraints, but also reflect the impacts of team efficiency. The findings will hopefully motivate future studies on comprehensive understandings of the quality and efficiency of team work.
  • Keywords
    learning (artificial intelligence); optimisation; project management; software management; team working; employee-task assignment; heuristic optimization method; population-based incremental learning; project scheduling problems; software project management problem; team efficiency; Genetic algorithms; Heuristic algorithms; Optimization; Project management; Software; Software algorithms; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2014 IEEE Congress on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-6626-4
  • Type

    conf

  • DOI
    10.1109/CEC.2014.6900527
  • Filename
    6900527