Technical Papers
Jul 3, 2014

Improved Critical Chain Project Management Framework for Scheduling Construction Projects

Publication: Journal of Construction Engineering and Management
Volume 140, Issue 12

Abstract

Construction projects are subject to a wide range of constraints, such as project complexity, resource scarcity, and duration uncertainty. The critical chain project management (CCPM) has emerged as a method for construction scheduling. This paper proposes an improved CCPM framework to enhance the implementation of CCPM in construction project management practices. The framework addresses two major challenges in CCPM-based construction scheduling, including buffer sizing and multiple resources leveling. Buffers play a key role in ensuring successful schedule management. However, buffers generated by the existing sizing methods are either unnecessarily large, which wastes resources, or insufficiently robust against various uncertainties. Resource leveling is another critical challenge in CCPM-based construction scheduling because it requires a fundamentally different approach from the resource leveling used in traditional scheduling methods. The proposed framework improves buffer sizing by integrating into the buffer sizing process various uncertainties that affect construction scheduling but are not factored in by current practice. These uncertainties are assessed in five dimensions with their respective metrics developed in the framework. Furthermore, the framework explores the feasibility of multiple resources leveling in CCPM-based construction scheduling, with a novel method that manages the trade-offs between activity duration and resource usages based on a multimodal activity execution structure. Three case studies were undertaken in this paper. The results showed that the proposed framework outperformed existing buffer sizing methods by generating buffers with reasonable sizes and sufficient robustness against uncertainties. The results also proved the feasibility and effectiveness of performing multiple resources leveling in CCPM-based construction scheduling.

Get full access to this article

View all available purchase options and get full access to this article.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (NNSFC) under Grants #70802045 and #71102142. The authors thank the NNSFC for its support. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NNSFC.

References

Ahlemann, F., ElArbi, F., Kaiser, M. G., and Heck, A. (2013). “A process framework for theoretically grounded prescriptive research in the project management field.” Int. J. Proj. Manage., 31(1), 43–56.
Al-Hinai, N., and ElMekkawy, T. Y. (2011). “Robust and stable flexible job shop schedulingwith random machine breakdowns using hybrid genetic algorithm.” Int. J. Prod. Econ., 132(2), 279–291.
Bevilacqua, M., Ciarapica, F. E., and Giacchetta, G. (2009). “Critical chain and risk analysis applied to high-risk industry maintenance: A case study.” Int. J. Proj. Manage., 27(4), 419–432.
Bie, L., Cui, N., and Zhang, X. (2012). “Buffer sizing approach with dependence assumption between activities in critical chain scheduling.” Int. J. Prod. Res., 50(24), 7343–7356.
Chang, T., Ibbs, C., and Crandall, K. (1990). “Network resource allocation with support of a fuzzy expert system.” J. Constr. Eng. Manage., 239–260.
Chen, P. H., and Shahandashti, S. M. (2009). “Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints.” Autom. Constr., 18(4), 434–443.
Chu, C. C. (2008). “Buffer sizing and critical chain project management.” Comput. Integr. Manuf. Syst., 18(5), 1029–1035 (in Chinese).
Cohen, I., Mandelbaum, A., and Shtub, A. (2004). “Multi-project scheduling and control: A process-based comparative study of the critical chain methodology and some alternatives.” Proj. Manage. J., 35(2), 39–50.
Crandall, K. C. (1985). Resource allocation with project manager control, ASCE, Reston, VA, 1–17.
Damci, A., Arditi, D., and Polat, G. (2013). “Multiresource leveling in line-of-balance scheduling.” J. Constr. Eng. Manage., 1108–1116.
Easa, S. (1989). “Resource leveling in construction by optimization.” J. Constr. Eng. Manage., 302–316.
Goldratt, E. M. (1997). Critical chain, North River Press, Great Barrington, MA.
Hall, N. G. (2012). “Project management: Recent developments and research opportunities.” J. Syst. Sci. Syst. Eng., 21(2), 129–143.
Hazir, O., Haouari, M., and Erel, E. (2010). “Robust scheduling and robustness measuresfor the discrete time/cost trade-off problem.” Eur. J. Oper. Res., 207(2), 633–643.
Hegazy, T. (1999). “Optimization of resource allocation and leveling using genetic algorithms.” J. Constr. Eng. Manage., 167–175.
Jun, D. H., and El-Rayes, K. (2011). “Multiobjective optimization of resource leveling and allocation during construction scheduling.” J. Constr. Eng. Manage., 1080–1088.
Herroelen, W., and Leus, R. (2001). “On the merits and pitfalls of critical chain scheduling.” J. Oper. Manage., 19(5), 559–577.
Kastor, A., and Sirakoulis, K. (2009). “The effectiveness of resource levelling tools for resource constraint project scheduling problem.” Int. J. Proj. Manage., 27(5), 493–500.
Kolisch, R., and Drexl, A. (1997). “Local search for nonpreemptive multi-mode resource-constrained project scheduling.” IIE Trans., 29(11), 987–999.
Kumar, V. (1987). “Entropic measures of manufacturing flexibility.” Int. J. Prod. Res., 25(7), 957–966.
Leu, S. S., Chen, A. T., and Yang, C. H. (1999). “A fuzzy optimal model for construction resource leveling scheduling.” Can. J. Civ. Eng., 26(6), 673–684.
Long, L. D., and Ohsato, A. (2008). “Fuzzy critical chain method for project scheduling under resource constraints and uncertainty.” Int. J. Proj. Manage., 26(6), 688–698.
Newbold, R. C. (1998). Project management in the fast lane: Applying the theory of constraints, Saint Lucie Press, Boca Raton, FL.
Peng, W. L., and Xu, H. (2012). “The scheduling problem of active critical chain method.” Inf. Technol. J., 11(7), 829–839.
Pozzi, L. (2003). “The coefficient of relative risk aversion: a Monte Carlo study investigating small sample estimator problems.” Econ. Modell., 20(5), 923–940.
Rand, G. K. (2000). “Critical chain: The theory of constraints applied to project management.” Int. J. Proj. Manage., 18(3), 173–177.
Raz, T., Barnes, R., and Dvir, D. (2003). “A critical look at critical chain project management.” Proj. Manage. J., 34(4), 24–32.
Rothe, I., Susse, H., and Voss, K. (1996). “The method of normalization to determine invariants.” IEEE Trans. Pattern Anal. Mach. Intell., 18(4), 366–376.
Rozenes, S., Vitner, G., and Spraggett, S. (2006). “Project control: Literature review.” Proj. Manage. J., 37(4), 5–14.
Schuhmacher, M., Meneses, M., Xifró, A., and Domingo, J. L. (2001). “The use of Monte-Carlo simulation techniques for risk assessment: Study of a municipal waste incinerator.” Chemosphere, 43(4–7), 787–799.
Senouci, A., and Eldin, N. (2004). “Use of genetic algorithms in resource scheduling of construction projects.” J. Constr. Eng. Manage., 869–877.
Shou, Y. Y., and Yao, K. T. (2000). “Estimation of project buffers in critical chain project management.” Proc., 2000 IEEE Int. Conf. on Management of Innovation and Technology, Vol. 1, Institution of Engineers, Singapore, 162–167.
Son, J., and Mattila, K. (2004). “Binary resource leveling model: Activity splitting allowed.” J. Constr. Eng. Manage., 887–894.
Trietsch, D., and Baker, K. R. (2012). “PERT 21: Fitting PERT/CPM for use in the 21st century.” Int. J. Proj. Manage., 30(4), 490–502.
Tukel, O. I., Rom, W. O., and Eksioglu, S. D. (2006). “An investigation of buffer sizing techniques in critical chain scheduling.” Eur. J. Oper. Res., 172(2), 401–416.
Usábel, M. A. (1998). “Applications to risk theory of a Monte Carlo multiple integration method.” Insur. Math. Econ., 23(1), 71–83.
Yang, L., Li, S., and Huang, X. (2009). “A buffer sizing approach in critical chain scheduling with attributes dependent.” Ind. Eng. Manage., 1, 11–14 (in Chinese).
Yingfa, S., and Hong, Y. (2010). “The risk study of e-governance based on PEST analysis model.” Proc., Int. Conf. on E-Business and E-Government (ICEE), The Korean Institute of Electrical Engineers (KIEE), Seoul, Korea, 563–566.
Zhao, Z. Y., You, W. Y., and Zuo, J. (2010). “Application of innovative critical chain method for project planning and control under resource constraints and uncertainty.” J. Constr. Eng. Manage., 1056–1060.

Information & Authors

Information

Published In

Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 140Issue 12December 2014

History

Received: Mar 4, 2014
Accepted: Jun 6, 2014
Published online: Jul 3, 2014
Published in print: Dec 1, 2014
Discussion open until: Dec 3, 2014

Permissions

Request permissions for this article.

Authors

Affiliations

Guofeng Ma
Associate Professor, Dept. of Construction Management and Real Estate, Tongji Univ., Shanghai 200092, China.
Aimin Wang
Assistant Professor, Dept. of Innovation and Strategy, Shanghai Jiao Tong Univ., Shanghai 200240, China.
Nan Li, M.ASCE [email protected]
Assistant Professor, Dept. of Construction Management, Tsinghua Univ., Beijing 100084, China (corresponding author). E-mail: [email protected]
Lingyun Gu
Graduate Student, Dept. of Construction Management and Real Estate, Tongji Univ., Shanghai 200092, China.
Qi Ai
Graduate Student, Dept. of Construction Management and Real Estate, Tongji Univ., Shanghai 200092, China.

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share