A review on learning to solve combinatorial optimisation problems in manufacturing

C Zhang, Y Wu, Y Ma, W Song, Z Le… - IET Collaborative …, 2023 - Wiley Online Library
An efficient manufacturing system is key to maintaining a healthy economy today. With the
rapid development of science and technology and the progress of human society, the …

[HTML][HTML] Deep reinforcement learning-based dynamic scheduling for resilient and sustainable manufacturing: A systematic review

C Zhang, M Juraschek, C Herrmann - Journal of Manufacturing Systems, 2024 - Elsevier
Dynamic scheduling plays a pivotal role in smart manufacturing by enabling real-time
adjustments to production schedules, thereby enhancing system resilience and promoting …

Design of a Machine Learning-based Decision Support System for Product Scheduling on Non Identical Parallel Machines

KAB Hamou, Z Jarir, S Elfirdoussi - Engineering, Technology & Applied …, 2024 - etasr.com
Production planning in supply chain management faces considerable challenges due to the
dynamics and unpredictability of the production environment. Decision support systems …

A blueprint description of production scheduling models using asset administration shells

A Gannouni, P Sapel, A Abdelrazeq… - Production & …, 2024 - Taylor & Francis
ABSTRACT Production Planning and Control involves combinatorial optimization problems
subject to domain-related constraints. Hence, decision-support systems are required to …

Towards practicality: Navigating challenges in designing predictive-reactive scheduling

F Erlenbusch, N Stricker - Procedia CIRP, 2024 - Elsevier
Today's agile production systems face an ever-increasing complexity due to individualized
mass production, a volatile customer demand and dynamic events which disrupt the …

Reinforcement learning approach for multi-agent flexible scheduling problems

H Zhou, B Gu, C Jin - Journal of Physics: Conference Series, 2023 - iopscience.iop.org
Scheduling plays an important role in automated production. Its impact can be found in
various fields such as the manufacturing industry, the service industry and the technology …

Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics

M Schneevogt, K Binninger, N Klarmann - arXiv preprint arXiv:2409.11820, 2024 - arxiv.org
This paper explores the potential application of Deep Reinforcement Learning in the
furniture industry. To offer a broad product portfolio, most furniture manufacturers are …

On The Effectiveness Of Bottleneck Information For Solving Job Shop Scheduling Problems Using Deep Reinforcement Learning

C Waubert de Puiseau, L Zey, M Demir… - ESSN: 2701 …, 2023 - repo.uni-hannover.de
Job shop scheduling problems (JSSPs) have been the subject of intense studies for
decades because they are often at the core of significant industrial planning challenges and …

A Standardised Environment for the Application of AI in Production Scheduling Research

C McGowan - 2024 - etheses.dur.ac.uk
Production scheduling contains a wide variety of nondeterministic polynomial time hard
problems that are differentiated by machine setups, constraints and optimisation targets …

Autonomous assembly and disassembly by cognition using hybrid assembly cells

U Frieß, L Oberfichtner, A Hellmich… - International …, 2023 - inderscienceonline.com
Current political, economic, and ecological developments put severe pressure on European
industries. Significant value chains depend uniliterally on single suppliers for many …