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Decision support for project rescheduling to reduce software development delays based on ant colony optimization

Zhang, Wei, Yang, Yun, Liu, Xiao, Zhang, Cheng, Li, Xuejun, Xu, Rongbin, Wang, Futian and Ali Babar, Muhammad 2018, Decision support for project rescheduling to reduce software development delays based on ant colony optimization, International Journal of Computational Intelligence Systems, vol. 11, no. 1, pp. 894-910.

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Title Decision support for project rescheduling to reduce software development delays based on ant colony optimization
Author(s) Zhang, Wei
Yang, Yun
Liu, XiaoORCID iD for Liu, Xiao orcid.org/0000-0001-8400-5754
Zhang, Cheng
Li, Xuejun
Xu, Rongbin
Wang, Futian
Ali Babar, Muhammad
Journal name International Journal of Computational Intelligence Systems
Volume number 11
Issue number 1
Start page 894
End page 910
Total pages 17
Publisher Atlantis Press
Place of publication Paris, France
Publication date 2018
ISSN 1875-6883
Summary Delays often occur during some activities in software development projects. Without handling of project delays effectively, many software development projects fail to meet their deadlines. If extra employees with same or similar skills and domain knowledge can be rescheduled for the remaining activities of the delayed projects, it can be possible to reduce or even eliminate existing delays in concurrent software development projects of similar nature. However, it is evident that employee rescheduling may result in delaying other activities, which may lead to the problem of delay propagation. Hence, it is important to investigate how to reduce or even eliminate the delay in one project without impacting other projects. By nature this is an NP-hard problem. Therefore, we propose a novel generic rescheduling strategy based on adaptive ant colony optimization algorithm to provide decision support for software project managers to select appropriate employees to deal with project delays. We have carried out a set of comprehensive experiments to evaluate the performance of the proposed strategy. In addition, three real world software project instances are also utilized to evaluate our strategy. The results show that our strategy is effective, efficient and able to outperform its representative counterparts significantly.
Language eng
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Grant ID ARC LP0990393
Copyright notice ©2018, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution non-commercial licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30108448

Document type: Journal Article
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.