Heuristic optimization for software project management with impacts of team efficiency

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

Standard

Heuristic optimization for software project management with impacts of team efficiency. / Jin, Nanlin; Yao, Xin.

Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers (IEEE), 2014. p. 3016-3023 6900527.

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

Harvard

Jin, N & Yao, X 2014, Heuristic optimization for software project management with impacts of team efficiency. in Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014., 6900527, Institute of Electrical and Electronics Engineers (IEEE), pp. 3016-3023, 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/CEC.2014.6900527

APA

Jin, N., & Yao, X. (2014). Heuristic optimization for software project management with impacts of team efficiency. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 3016-3023). [6900527] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC.2014.6900527

Vancouver

Jin N, Yao X. Heuristic optimization for software project management with impacts of team efficiency. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers (IEEE). 2014. p. 3016-3023. 6900527 https://doi.org/10.1109/CEC.2014.6900527

Author

Jin, Nanlin ; Yao, Xin. / Heuristic optimization for software project management with impacts of team efficiency. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers (IEEE), 2014. pp. 3016-3023

Bibtex

@inproceedings{4de6ce585f36476e95b2851d8f5b8fc0,
title = "Heuristic optimization for software project management with impacts of team efficiency",
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.",
author = "Nanlin Jin and Xin Yao",
year = "2014",
month = sep,
day = "16",
doi = "10.1109/CEC.2014.6900527",
language = "English",
isbn = "9781479914883",
pages = "3016--3023",
booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
note = "2014 IEEE Congress on Evolutionary Computation, CEC 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

}

RIS

TY - GEN

T1 - Heuristic optimization for software project management with impacts of team efficiency

AU - Jin, Nanlin

AU - Yao, Xin

PY - 2014/9/16

Y1 - 2014/9/16

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84908568367&partnerID=8YFLogxK

U2 - 10.1109/CEC.2014.6900527

DO - 10.1109/CEC.2014.6900527

M3 - Conference contribution

AN - SCOPUS:84908568367

SN - 9781479914883

SP - 3016

EP - 3023

BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Y2 - 6 July 2014 through 11 July 2014

ER -