Discrete-Event Simulation versus Constrained Graphic Modelling of Construction Processes

Ian Flood
Valeh Nowrouzian


Effective construction project planning and control requires the development of a model of the project’s construction processes.  The Critical Path Method (CPM) is the most popular project modelling method in construction since it is relatively simple to use and reasonably versatile in terms of the range of processes it can represent.  Several other modelling techniques have been developed over the years, each with their own advantages and disadvantages.  Linear scheduling, for example, has been designed to provide highly insightful visual representations of a construction process, but unfortunately is largely incapable of representing non-repetitive construction work.  Discrete-event simulation is generally agreed to be the most versatile of all modelling methods, but it lacks the simplicity in use of CPM and so has not been widely adopted in construction.  A new graphical constraint-based method of modelling construction processes, Foresight, has been developed with the goal of offering the simplicity in use of CPM, the visual insight of linear scheduling, and the versatility of simulation.  Earlier work has demonstrated the modelling versatility of Foresight.  As part of a continuing study, this paper focuses on a comparison of the Foresight approach with discrete-event construction simulation methods, specifically Stroboscope (a derivative of CYCLONE). Foresight is shown to outperform Stroboscope in terms of the simplicity of the resultant models for a series of case studies involving a number of variants of an earthmoving operation and of a sewer tunnelling operation.  A qualitative comparison of the two approaches also highlights the superior visual insight provided by Foresight over conventional simulation, an attribute essential to both the effective verification and optimization of a model.


Construction process, Foresight, Process modelling, Construction simulation, Stroboscope, Model complexity, Visual insight

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DOI: http://dx.doi.org/10.5130/ajceb-cs.v2i1.3765


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