Abstract
Schedule network is essential for project schedule management. Critical Chain Method (CCM) is the most commonly used method on a schedule network to avoid project extension. The key to CCM lies in setting the proper buffer size. However, little work has considered the interdependence of nodes into buffer size calculating. In this paper, we present Edge-shared GraphSAGE, a model based on Graph Neural Network (GNN) for improving the result of buffer size prediction. Edge-shared GraphSAGE constructs undirected edges between schedule networks of projects sharing resources with each other. Fed by historical data of previous projects, the model predicts Safe-time Utilization Rate of each node of current project, so as to calculate the predicted size of the buffer. To the best of our knowledge, this is the first time that GNN is used in calculating buffer size. In several real projects, the proposed method outperforms Rule-based method and Machine Learning method.
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This work was supported in part by the National Key Research and Development Program of China under Grants 2020YFC1807104.
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Zhao, Y., Xu, Y., Wang, Z. (2022). Edge-Shared GraphSAGE: A New Method of Buffer Calculation for Parallel Management of Big Data Project Schedule. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_17
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DOI: https://doi.org/10.1007/978-3-031-10983-6_17
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