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
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;
Conference_Titel :
Evolutionary Computation (CEC), 2014 IEEE Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6626-4
DOI :
10.1109/CEC.2014.6900527