Since project monitoring aims to make decisions, which have future impacts on a project’s success, accurate forecasting of project characteristics is of greater importance. This paper proposes the detection of problems in scheduling projects by introducing the forecasting of successors and renewable resource features. For this purpose, this paper presents the novel applications of forecasting models ”long short term memory” (LSTM) and ”gated recurrent unit” (GRU) in this study. Subsequently, forecasting results of successors and renewable resource characteristics of projects are determined by covering historical records’ observed values from real-life engineering projects’ datasets. Results show that LSTM and GRU models’ proposed applications can reduce errors from one up to seven steps-ahead forecastings. Moreover, forecasting the increased number of successors may require more renewable resources to effectively complete the jobs in projects. Generally, the proposed models are reliable and robust to application in forecasting the project scheduling task.

由于项目监视旨在做出可能对项目成功产生未来影响的决策，因此准确预测项目特征就显得尤为重要。本文通过介绍对继任者和可再生资源特征的预测，提出了对调度项目中问题的检测。为此，本文介绍了预测模型“长期短期记忆”（LSTM）和“门控循环单位”（GRU）在本研究中的新颖应用。随后，通过覆盖现实工程项目数据集中的历史记录观察值来确定项目的后续项目和可再生资源特征的预测结果。结果表明，LSTM和GRU模型提出的应用程序可以将误差从一项预测减少到多达七个步骤。而且，预测继任者数量的增加可能需要更多的可再生资源来有效完成项目中的工作。通常，所提出的模型对于在预测项目调度任务中的应用是可靠且健壮的。