Reactive scheduling based on actual logistics data by applying simulation-based optimization

Abstract

Abstract Background A reasonable management and monitoring of construction projects requires accurate construction schedules. Accuracy depends highly on availability of reliable actual logistics data. Such data contain information about available material, equipment, personnel, updated delivery dates, and other data on site conditions. However, such data is often associated with different types of uncertainties due to infrequent collections, varying transport times, or manual assessments. Nonetheless, consideration of these uncertainties is important for evaluating actual data regarding their impact on the overall construction progress. Currently, the integration of such data into construction schedules is a time-consuming, manual and, thus, error-prone process. Therefore, in practice schedules are not updated as often as they should be. Methods To ease the handling of actual data and their integration into construction schedules, a reactive construction scheduling approach is presented. The approach is structured into four successive steps. To evaluate and systematically analyze uncertain actual data, fuzzy set theory and α-cut method are incorporated. Thus, actual data can be integrated into discrete-event simulation models. These models are used to perform simulation-based sensitivity analyzes, which evaluate impacts on construction schedules. As a result, an actual schedule is generated, such that a target-actual schedule comparison can be performed. If significant deviations or problems are identified, adaption is necessary and a new schedule needs to be generated. Thereby, different restrictions on the target schedule, such as contracted delivery dates, milestones or resource allocation must be considered. To perform this required adaption simulation-based optimization is utilized. Results To validate the method and show its advantages, an initial construction schedule example is created. The example is extended to incorporate uncertain actual logistics data. The proposed method shows how efficient actual data can be analyzed to update construction schedules. Further, the results show a competitive adaption of invalid construction schedules, such that contracted milestones, or other project objectives can be achieved. Conclusion The presented reactive construction scheduling method has the ability to improve current treatment of uncertain actual logistics data. This helps construction project managers to improve the management and monitoring of construction projects by reducing the time-consuming, error-prone process of updating inconsistent schedules.