Forecasting the scheduling issues in engineering project management: Applications of deep learning models-Reference-Cited by-同舟云学术

Forecasting the scheduling issues in engineering project management: Applications of deep learning models

Published:2021-10 Issue: Volume:123 Page:85-93
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

Liu Sai,Hao Wenqi

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference31 articles.

1. Using real project schedule data to compare earned schedule and earned duration management project time forecasting capabilities;de Andrade;Autom. Constr.,2019

2. Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty;Grossmann;Comput. Chem. Eng.,2016

3. A deep reinforcement learning approach for chemical production scheduling;Hubbs;Comput. Chem. Eng.,2020

4. Data-driven approach for spatiotemporal distribution prediction of fault events in power transmission systems;Sun;Int. J. Electr. Power Energy Syst.,2019

5. A probabilistic sequence classification approach for early fault prediction in distribution grids using long short-term memory neural networks;Skydt;Measurement,2020

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data analytics for project delivery: unlocking the potential of an emerging field;International Journal of Managing Projects in Business;2024-04-02

2. Synergizing Global and Local Strategies for Dynamic Project Management: An Advanced Machine Learning-Enhanced Framework;IEEE Access;2024

3. The Relevance of Artificial Intelligence in Project Management;Engineering Cyber-Physical Systems and Critical Infrastructures;2024

4. A Full Process Mechanized Transmission Line Engineering Duration Prediction Model Based on Bayesian-LSTM;2023 7th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE);2023-10-20

5. Artificial Intelligence Applications in Project Scheduling: A Systematic Review, Bibliometric Analysis, and Prospects for Future Research;Management Systems in Production Engineering;2023-05-03