Value-Based Multiple Software Projects Scheduling with Genetic Algorithm

  • Authors:
  • Junchao Xiao;Qing Wang;Mingshu Li;Qiusong Yang;Lizi Xie;Dapeng Liu

  • Affiliations:
  • Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing, China 100190;Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing, China 100190;Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing, China 100190 and Key Laboratory for Computer Science, Institute of Software, Chinese Aca ...;Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing, China 100190;Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing, China 100190;Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing, China 100190

  • Venue:
  • ICSP '09 Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes
  • Year:
  • 2009

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Abstract

Scheduling human resources to multiple projects under various resource requirements, constraints and value objectives is a key problem that many software organizations struggle with. This paper gives a value-based human resource scheduling method among multiple software projects by using a genetic algorithm. The method synthesizes the constraints such as those of schedule and cost as well as the value objectives among different projects, and also the construction of comprehensive value function for evaluating the results of human resource scheduling. Under the guidance of value function, capable human resources can be scheduled for project activities by using the genetic algorithm and make the near-maximum value for organizations. Case study and the simulation results show that the method can perform the scheduling and reflect the value objectives of different projects effectively, and the results provide a concrete decision support for project managers.