A GA-based fuzzy optimal model for construction time-cost trade-off
By: LEU, Sou-Sen
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Contributor(s): CHEN, An-Ting | YANG, Chung-Huei.
Material type: 
ArticlePublisher: 2001International Journal of Project Management 19, 1, p. 47-58Abstract: Owing to different resource utilization, activity duration might need to be adjusted and the project direct cost could also change accordingly. Moreover, activity duration is uncertain due to variations in the outside environment, such as weather, site congestion, productivity level, etc. A new optimal construction time-cost trade-off method is proposed in this paper, in which the effects of both uncertain activity duration and time-cost trade-off are taken into account. Fuzzy set theory is used to model the uncertainties of activity durations. A searching technique using genetic algorithms (GAs) is adopted to seach for the optimal project time-cost trade-off profiles under different risk levels. The method provides an insight into the optimal balance of time and cost under different risks levels defined by decision makers
| Item type | Current location | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
| Periódico | Biblioteca Graciliano Ramos | Periódico | Not for loan |
Owing to different resource utilization, activity duration might need to be adjusted and the project direct cost could also change accordingly. Moreover, activity duration is uncertain due to variations in the outside environment, such as weather, site congestion, productivity level, etc. A new optimal construction time-cost trade-off method is proposed in this paper, in which the effects of both uncertain activity duration and time-cost trade-off are taken into account. Fuzzy set theory is used to model the uncertainties of activity durations. A searching technique using genetic algorithms (GAs) is adopted to seach for the optimal project time-cost trade-off profiles under different risk levels. The method provides an insight into the optimal balance of time and cost under different risks levels defined by decision makers
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