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Article type: Research Article
Authors: Salari, Mostafa | Bagherpour, Morteza | Reihani, Mohammad Hossein
Affiliations: Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran | Deparment of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Note: [] Corresponding author. Mostafa Salari, Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran. Tel.: +98 915 1592269; E-mail: [email protected]
Abstract: Time–cost trade-off problems (TCT) are well-known in project management contents. This approach normally applied for scheduling of a project especially where the project should be completed under pre-determined deadline. This paper aims to extend time-cost trade-off (TCT) problems in order to provide a well-organized mechanism for both scheduling and rescheduling processes of a project. The proposed mechanism includes project scheduling which concerns with the TCT problem, monitoring of project performance during execution phase using earned value management (EVM), and also predicting project future performance through statistical modeling. Once the predicted values of project performance indicate the necessity for rescheduling, the initial TCT problem is modified to determine a new mode for the execution of the project. In the proposed model, also several options have been considered with specific time and cost for an individual activity where options indicate different execution methods (EM) for the implementation of the whole project. Furthermore, due to vagueness and impreciseness associated with data of real case projects, the time and cost behavior of each option presumed as fuzzy numbers. The proposed control mechanism can help project managers to take the advantage of a comprehensive model to schedule, control, and reschedule a project through the life cycle. An illustrative case is then presented to successfully demonstrate the application of the proposed approach.
Keywords: Time-cost trade-off, fuzzy logic, earned value, fuzzy regression, rescheduling
DOI: 10.3233/IFS-141478
Journal: Journal of Intelligent & Fuzzy Systems, vol. 28, no. 4, pp. 1909-1919, 2015
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