Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/117872
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Type: | Journal article |
Title: | Decision support for project rescheduling to reduce software development delays based on ant colony optimization |
Author: | Zhang, W. Yang, Y. Liu, X. Zhang, C. Li, X. Xu, R. Wang, F. Babar, M.A. |
Citation: | International Journal of Computational Intelligence Systems, 2018; 11(1):894-910 |
Publisher: | Taylor & Francis |
Issue Date: | 2018 |
ISSN: | 1875-6883 1875-6883 |
Statement of Responsibility: | Wei Zhang, Yun Yang, Xiao Liu, Cheng Zhang, Xuejun Li, Rongbin Xu, Futian Wang, Muhammad Ali Babar |
Abstract: | 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. |
Keywords: | Decision support; ant colony optimization; project management; rescheduling; optimization |
Rights: | © 2018, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/). |
DOI: | 10.2991/ijcis.11.1.68 |
Grant ID: | http://purl.org/au-research/grants/arc/LP0990393 |
Published version: | http://dx.doi.org/10.2991/ijcis.11.1.68 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
Files in This Item:
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hdl_117872.pdf | Published version | 2.52 MB | Adobe PDF | View/Open |
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