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Purpose – The purpose of this paper is to address the problem of scheduling under uncertainty in construction projects. The existing methods for determining a project schedule are based on assumption of complete knowledge of project parameters; but in reality there is uncertainty in construction projects, deriving from a multitude of context‐dependent sources and often provided as outcome of a risk analysis process. Thus, classical deterministic analysis might provide a schedule which is not sufficiently protected against possible disruptions. Design/methodology/approach – A quantitative methodology is developed for planning construction projects under uncertainty aimed at determining a reliable resource feasible project schedule by taking into account the available probabilistic information to produce solutions that are less sensitive to perturbations that occur on line. The methodology relies on a computer‐supported system that allows to identify, analyze and quantify the schedule reliability and the impact of possible disruptions on the duration of the project. Findings – It is found that the proposed methodology can exploit more information about the uncertain parameters than the commonly‐used deterministic method, and it provides an improved understanding of the schedule reliability in presence of uncertainty. The schedule generated with a classical deterministic method sets a completely unrealistic planned project delivery date of about 1,250 days, with a probability around 50 per cent to be exceeded. This behavior can be very unsatisfactory for construction projects for which high penalties are usually associated to heavy due date violations. Originality/value – This paper presents an approach for robust scheduling of construction project problem under uncertainty. We provide a tool able to support managers in developing a workable and realistic project schedule to be used as a guideline for project control and monitoring.
Engineering Computations – Emerald Publishing
Published: Nov 15, 2011
Keywords: Construction industry; Uncertainty management; Construction projects; Project scheduling; Resource management
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