ESRA, AN EDUCATIONAL SOFTWARE FOR INTRODUCING STOCHASTIC SCHEDULING TO CIVIL ENGINEERING STUDENTS
Classical scheduling techniques are commonly employed tools in civil engineering schools worldwide for teaching project planning and management. Techniques such as the Critical Path Method (CPM), Precedence diagram method (PDM), Gantt chart or the Program Evaluation and Review Technique (PERT) present the advantage of their simplicity, ease of understanding and that they are implemented in the most accepted project management software such as Ms Project or Primavera P6. These scheduling techniques, however, present important limitations in dealing with the uncertainty inherent to construction project management. On the one hand, the deterministic approach of CPM to project planning learning reduces sensitivity and understanding of the factors that significantly alter and challenge the success of a project, while on the other hand PERT shows too limited capabilities in uncertainty modelling and underestimates the project duration standard deviation.
Schedule Risk Analysis (SRA) is a stochastic method that has been pointed out to promote that students start managing projects more effectively and more efficiently. I this study, we employ an educational SRA software (ESRA) to help students understand the underlying assumption of stochastic scheduling, as well as to make explicit the advantages of stochastic scheduling compared to classical methods such as CPM or PERT. ESRA affords modelling both the uncertainty in the duration of activities, and the relationship between these uncertainties, expanding the range of planning problems which students can now assess. This research was implemented in four stages through a workshop. First we introduced the theoretical foundations of Monte Carlo Simulation, the method underlying most uncertainty assessment methods. Second students employed ESRA to see how this method works. Third, students worked around a case-study of the construction projects management. Finally, they were asked to compare the results of the stochastic assessment with those of the deterministic assessment, and to think of real-world planning problems in which having uncertainty into account would be of help in having a more reliable estimate of the outcome of the process.