Heuristic optimization for software project management with impacts of team efficiency

Nanlin Jin*, Xin Yao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3016-3023
Number of pages8
ISBN (Print)9781479914883
DOIs
Publication statusPublished - 16 Sep 2014
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Conference

Conference2014 IEEE Congress on Evolutionary Computation, CEC 2014
Country/TerritoryChina
CityBeijing
Period6/07/1411/07/14

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Theoretical Computer Science

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