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. 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. 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. 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.

中文翻译：

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通过基于仿真的优化，根据实际物流数据进行反应式调度

合理的建设项目管理和监控要求准确的建设进度。准确性在很大程度上取决于可靠的实际物流数据的可用性。此类数据包含有关可用材料，设备，人员，更新的交货日期以及其他现场条件数据的信息。然而，由于不经常收集，变化的运输时间或人工评估，此类数据通常与不同类型的不确定性相关。但是，考虑这些不确定性对于评估有关其对整体施工进度的影响的实际数据很重要。当前，将这样的数据集成到施工进度表中是耗时的，手动的并且因此容易出错的过程。因此，在实践中，进度表的更新频率不会达到应有的水平。为了简化实际数据的处理并将其集成到施工进度表中，提出了一种被动的施工进度表方法。该方法分为四个连续步骤。为了评估和系统分析不确定的实际数据，结合了模糊集理论和α-割方法。因此，实际数据可以集成到离散事件仿真模型中。这些模型用于执行基于仿真的敏感性分析，以评估对施工进度的影响。结果，生成实际时间表，从而可以执行目标-实际时间表比较。如果发现重大偏差或问题，则需要进行调整，并且需要生成新的计划表。因此，对目标时间表的限制有所不同，例如合同规定的交货日期，必须考虑里程碑或资源分配。为了执行此所需的适应，基于仿真的优化被利用。为了验证该方法并显示其优势，创建了一个初始施工计划示例。该示例已扩展为包含不确定的实际物流数据。所提出的方法显示了如何分析有效的实际数据以更新施工进度表。此外，结果显示了无效施工进度的竞争性适应，从而可以实现合同规定的里程碑或其他项目目标。所提出的反应式施工调度方法能够改善当前对不确定的实际物流数据的处理。这可以帮助建筑项目经理减少耗时的工作，从而改善建筑项目的管理和监控，