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Bai, Y (2007) Intelligent Painting Process Planner for Robotic Bridge Painting. Journal of Construction Engineering and Management, 133(04), 335–42.

Chang, C, Hanna, A S, Lackney, J A and Sullivan, K T (2007) Quantifying the Impact of Schedule Compression on Labor Productivity for Mechanical and Sheet Metal Contractor. Journal of Construction Engineering and Management, 133(04), 287–96.

Ko, C and Cheng, M (2007) Dynamic Prediction of Project Success Using Artificial Intelligence. Journal of Construction Engineering and Management, 133(04), 316–24.

Ling, F Y Y, Hartmann, A, Kumaraswamy, M and Dulaimi, M (2007) Influences on Innovation Benefits during Implementation: Client’s Perspective. Journal of Construction Engineering and Management, 133(04), 306–15.

Maloney, W F, Cameron, I and Hare, B (2007) Tradesmen Involvement in Health and Safety. Journal of Construction Engineering and Management, 133(04), 297–305.

Shaheen, A A, Fayek, A R and AbouRizk, S M (2007) Fuzzy Numbers in Cost Range Estimating. Journal of Construction Engineering and Management, 133(04), 325–34.

Song, Y and Chua, D K H (2007) Temporal Logic Representation Schema for Intermediate Function. Journal of Construction Engineering and Management, 133(04), 277–86.

Vaziri, K, Carr, P G and Nozick, L K (2007) Project Planning for Construction under Uncertainty with Limited Resources. Journal of Construction Engineering and Management, 133(04), 268–76.

  • Type: Journal Article
  • Keywords: Project management; Uncertainty principles; Resource management; Construction industry;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2007)133:4(268)
  • Abstract:
    Much of the project scheduling literature treats task durations as deterministic. In reality, however, task durations are subject to considerable uncertainty, and that uncertainty can be influenced by the resources assigned. The purpose of this paper is to provide the means for contractors to optimally allocate their skilled workers among individual tasks for a single project. Instead of the traditional use of schedules, we develop control policies in the form of planned resource allocation to tasks that capture the uncertainty associated with task durations and the impact of resource allocation on those durations. We develop a solution procedure for the model and illustrate the ideas in an example. The data for the example is collected from a real project.