Data-driven AI algorithms for construction machinery

K Liang, J Zhao, Z Zhang, W Guan, M Pan… - Automation in …, 2024 - Elsevier
Based on the transition to Industry 4.0, construction operations are gradually moving
towards large-scale and high-efficiency development. However, excessive manual labor is …

Digital Twins for Discrete Manufacturing Lines: A Review

X Feng, J Wan - Big Data and Cognitive Computing, 2024 - mdpi.com
Along with the development of new-generation information technology, digital twins (DTs)
have become the most promising enabling technology for smart manufacturing. This article …

Generative deep reinforcement learning method for dynamic parallel machines scheduling with adaptive maintenance activities

M Wang, J Zhang, P Zhang, W Xiang, M Jin… - Journal of Manufacturing …, 2024 - Elsevier
In the process industries, where orders arrive at irregular intervals, inappropriate
maintenance frequency often leads to unplanned shutdowns of high-speed parallel …

A deep reinforcement learning-driven multi-objective optimization and its applications on aero-engine maintenance strategy

Z Wei, Z Zhao, Z Zhou, J Ren, Y Tang, R Yan - Journal of Manufacturing …, 2024 - Elsevier
Efficiently optimizing maintenance strategies is vital for the cost reduction of complex
equipment. Most of the current research in this domain primarily focuses on single-objective …

Agent-based Decision Making and Control of Manufacturing System Considering the Joint Production, Maintenance, and Quality by Reinforcement Learning

MR Nazabadi, SE Najafi, A Mohaghar… - … in Management and …, 2024 - dmame-journal.org
Taking an integrated approach towards production, maintenance, and control in
manufacturing systems is crucial due to the profound impact of their interconnections …

Dynamic Real-Time Optimization of Modular Unit Allocation to Off-Site Facilities in Postdisaster Reconstruction Using Deep Reinforcement Learning

A Deria, P Ghannad, YC Lee - Journal of Management in …, 2024 - ascelibrary.org
Postdisaster housing reconstruction (PDHR) requires robust, efficient planning, and
coordination among dispersed prefabrication facilities and jobsites to maximize …

Research on Sustainable Scheduling of Material-Handling Systems in Mixed-Model Assembly Workshops Based on Deep Reinforcement Learning.

B Xia, Y Li, J Gu, Y Peng - Sustainability (2071-1050), 2024 - search.ebscohost.com
In order to dynamically respond to changes in the state of the assembly line and effectively
balance the production efficiency and energy consumption of mixed-model assembly, this …

Maintenance Planning with Deterioration by a Reinforcement Learning Approach-A Semiconductor Simulation Study

C Leenen, M Geurtsen, I Adan… - 2024 Winter Simulation …, 2024 - ieeexplore.ieee.org
Manufacturing companies are often faced with deteriorating production systems, which can
greatly impact their overall performance. Scheduling the preventive maintenance activities …

Engineering Activities in Digital Twins: A Literature Review

C Fresemann - DS 130: Proceedings of NordDesign 2024 …, 2024 - designsociety.org
This research aims to understand and describe engineering activities carried out in or with a
digital twin undertaken by engineers interacting with it. A literature-based study is presented …

Rainbow Deep Reinforcement Learning in the Chinese Stock Market

J Chen, H Fu, Y Xue, Y Zhu - Available at SSRN 4885011, 2024 - papers.ssrn.com
This paper first analyzes the impact of monthly gold stock recommendations by securities
firms on investor sentiment, and then constructs a quantitative stock selection investment …