Integration of Uncertain Real-Time Logistics Data for Reactive Scheduling Using Fuzzy Set Theory

Abstract

This paper considers the integration of uncertain real-time logistics data for reactive construction scheduling. In order to manage a construction project efficiently, an accurate schedule representing the current project progress is inevitable. The quality and up-to-dateness of such a schedule depends on the availability of real-time data. Typically, real-time logistics data contain information about the availability of material, equipment and personnel as well as delivery dates and site conditions. The accuracy and inherent uncertainty depends on the location where the real-time data was acquired. Currently, the integration of such data into a construction schedule is a very time-consuming, manual and, thus, errorprone process. Therefore, this paper proposes a methodology that enables an automatic integration of such uncertain data into construction schedules. By integrating uncertainties into the existing schedule their impacts on the construction work can be evaluated. For this, discrete event simulation is applied. In order to model uncertain input parameters for simulation models this methodology applies the fuzzy sets theory. In combination with alpha-cut sampling technique, discrete model input parameters are obtained. By applying reactive scheduling with several discrete event simulation experiments, the results can be used to modify construction schedules according to agreed timeframes and costs. In order to demonstrate and validate the presented approach an example is conducted.

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Reactive Adaptation of Construction Schedules by Applying Simulation-Based Optimization

“…Discrete values, in this case discrete availability constraints, are required for evaluating the impacts on the schedule. Further information how to model and integrate real-time logistics data can be found in preceding research by the authors in [24]. For the target-actual comparison the results of the single discrete experiments are statistically interpreted.…”