Large scale foundation models for intelligent manufacturing applications: a survey

H Zhang, SD Semujju, Z Wang, X Lv, K Xu… - Journal of Intelligent …, 2025 - Springer
Although the applications of artificial intelligence especially deep learning have greatly
improved various aspects of intelligent manufacturing, they still face challenges for broader …

A deep reinforcement learning based hyper-heuristic for modular production control

M Panzer, B Bender, N Gronau - International Journal of …, 2024 - Taylor & Francis
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly
configurable products require an adaptive and robust control approach to maintain …

Smart mobile robot fleet management based on hierarchical multi-agent deep Q network towards intelligent manufacturing

Y Bai, Y Lv, J Zhang - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
With the advent of intelligent manufacturing era, smart mobile robots have taken the major
roles on transporting materials through intelligent dynamic production environment. It is …

Assessing generalizability in deep reinforcement learning based assembly: a comprehensive review

L Kolb, M Panzer, N Gronau - Journal of Intelligent Manufacturing, 2024 - Springer
The increasing complexity of production environments and fluctuations in short-term
demand requires adaptive and robust processes. To cope with the inherent challenges …

Designing an adaptive and deep learning based control framework for modular production systems

M Panzer, N Gronau - Journal of Intelligent Manufacturing, 2023 - Springer
In today's rapidly changing production landscape with increasingly complex manufacturing
processes and shortening product life cycles, a company's competitiveness depends on its …

[HTML][HTML] The Use of Reinforcement Learning for Material Flow Control: An Assessment by Simulation

Z He, M Thürer, W Zhou - International Journal of Production Economics, 2024 - Elsevier
One of the main objectives of Material Flow Control (MFC) is to ensure delivery performance.
Traditional MFC realizes this through independent decisions at two levels: order release and …

An agent-based cooperative co-evolutionary framework for optimizing the production planning of energy supply chains under uncertainty scenarios

S Chen, C Ma, W Wang, E Zio - International Journal of Production …, 2024 - Elsevier
Nowadays, energy and power companies compete to get the raw materials and equipment
they need on time, as project times lengthen, costs spiral, stock-out continues to plague …

Managing production for mass customized manufacturing–case studies

J Patalas-Maliszewska, K Kowalczewska… - … on Intelligent Systems in …, 2023 - Springer
Mass, customised production is a strategy, dictated by the need to dynamically and quickly
meet customer requirements. Industry 4.0 (I4. 0) technologies can be an excellent tool to …

Reinforcement learning and digital twin-driven optimization of production scheduling with the digital model playground

A Seipolt, R Buschermöhle, V Haag… - Discover Internet of …, 2024 - Springer
The significance of digital technologies in the context of digitizing production processes,
such as Artificial Intelligence (AI) and Digital Twins, is on the rise. A promising avenue of …

Framework for automatic production simulation tuning with machine learning

MC May, A Finke, K Theuner, G Lanza - Procedia CIRP, 2024 - Elsevier
Production system simulation is a powerful tool for optimizing the use of resources on both
the planning and control level. However, creating and tuning such models manually is a …