Technical Papers
Jun 27, 2014

Improved Strategy for Resource Allocation in Repetitive Projects Considering the Learning Effect

Publication: Journal of Construction Engineering and Management
Volume 140, Issue 11

Abstract

Considering the learning effect while planning and scheduling repetitive construction projects can help provide a realistic forecast of their duration and resource requirements. This paper has developed an improved learning curve (LC) to replace the log-linear LC for monitoring improvement in workers’ performance. The improved LC assumes that the manual work time of the specific unit converges to a nonzero constant rather than zero under a large number of repetitions. It also takes into account the influence of workers’ prior experience and machinery in the learning process. An improved line-of-balance (LOB) model is then presented by integrating the proposed LC and a mechanism of resource allocation. The proposed LOB model can make use of the learning effect to minimize the total resource usage of a project, while meeting the requirements for work continuity and the target deadline of each activity. An illustrative example has been cited to demonstrate the capability of the model. The technique can help planners use the learning effect to get a more realistic and optimal schedule about resource utilization.

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Acknowledgments

The authors would like to acknowledge the Natural Science Foundation of China (Contract No. 71271081) and the Fundamental Research Funds for the Central Universities 13ZD08). The authors are grateful to the anonymous referee for a careful scrutiny of details and for comments that helped improve this paper.

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Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 140Issue 11November 2014

History

Received: Jul 22, 2013
Accepted: May 9, 2014
Published online: Jun 27, 2014
Published in print: Nov 1, 2014
Discussion open until: Nov 27, 2014

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Authors

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Lihui Zhang, Ph.D. [email protected]
Associate Professor, School of Economics and Management, North China Electric Power Univ., No. 2 Beinong Rd., Huilongguan, Changping, Beijing 102206, P.R. China (corresponding author). E-mail: [email protected]
Ph.D. Candidate, School of Economics and Management, North China Electric Power Univ., No. 2 Beinong Rd., Huilongguan, Changping, Beijing 102206, P.R. China. E-mail: [email protected]
Ph.D. Candidate, School of Economics and Management, North China Electric Power Univ., No. 2 Beinong Rd., Huilongguan, Changping, Beijing 102206, P.R. China. E-mail: [email protected]

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