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Estimating project and activity duration: a risk management approach using network analysis

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  • Nashwan Dawood

Abstract

Variations in the durations of activities are commonplace in the construction industry. This is due to the fact that the construction industry is influenced greatly by variations in weather, productivity of labour and plant, and quality of materials. Stochastic network analysis has been used by previous researchers to model variations in activities and produce more effective and reliable project duration estimates. A number of techniques have been developed in previous literature to solve the uncertain nature of networks, these are: PERT (program evaluation and review techniques), PNET (probabilistic network evaluation technique), NRB, (narrow reliability bounds methods) and MCS (Monte Carlo simulation). Although these techniques have proved to be useful in modelling variations in activities, dependence of activity duration is not considered. This can have a severe impact on realistically modelling projects. In this context, the objective of the present research is to develop a methodology that can accurately model activity dependence and realistically predict project duration using a risk management approach. A simulation model has been developed to encapsulate the methodology and run experimental work. In order to achieve this, the following tasks are tackled: identify risk factors that cause activity variations using literature reviews and conducting interviews with contractors; model risk factors and their influence on activity variations through conducting case studies and identifying any dependence between them; develop a computer based simulation model that uses a modified Monte Carlo technique to model activity duration and dependence of risk factors; and run experimental work to validate and verify the model.

Suggested Citation

  • Nashwan Dawood, 1998. "Estimating project and activity duration: a risk management approach using network analysis," Construction Management and Economics, Taylor & Francis Journals, vol. 16(1), pages 41-48.
  • Handle: RePEc:taf:conmgt:v:16:y:1998:i:1:p:41-48
    DOI: 10.1080/014461998372574
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    Citations

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    Cited by:

    1. Pfeifer, Jeremy & Barker, Kash & Ramirez-Marquez, Jose E. & Morshedlou, Nazanin, 2015. "Quantifying the risk of project delays with a genetic algorithm," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 34-44.
    2. Emad Mohamed & Parinaz Jafari & Adam Chehouri & Simaan AbouRizk, 2021. "Simulation-Based Approach for Lookahead Scheduling of Onshore Wind Projects Subject to Weather Risk," Sustainability, MDPI, vol. 13(18), pages 1-27, September.
    3. Babaei, Mohsen & Rashidi-baqhi, Amin, 2022. "Universal generating function -based narrow reliability bounds to evaluate reliability of project completion time," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    4. Salim Rostami & Stefan Creemers & Roel Leus, 2018. "New strategies for stochastic resource-constrained project scheduling," Journal of Scheduling, Springer, vol. 21(3), pages 349-365, June.
    5. Chen, Yang & Zhang, Xiaoling & Chau, K.W. & Yang, Linchuan, 2022. "How the institutional change in urban redevelopment affects the duration of land redevelopment approval in China?," Land Use Policy, Elsevier, vol. 119(C).
    6. Balouka, Noemie & Cohen, Izack, 2021. "A robust optimization approach for the multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 291(2), pages 457-470.

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