A review on learning to solve combinatorial optimisation problems in manufacturing
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 …
rapid development of science and technology and the progress of human society, the …
A review of reinforcement learning based intelligent optimization for manufacturing scheduling
As the critical component of manufacturing systems, production scheduling aims to optimize
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies
Modern manufacturing and logistics systems are supported by increasingly ubiquitous and
powerful computing networks. Within these networks, oceans of data are continuously being …
powerful computing networks. Within these networks, oceans of data are continuously being …
Dynamic production scheduling towards self-organizing mass personalization: A multi-agent dueling deep reinforcement learning approach
Mass personalization is rapidly approaching. In response, manufacturing systems should be
capable of autonomously changing production plans, configurations and schedules under …
capable of autonomously changing production plans, configurations and schedules under …
Deep reinforcement learning applied to an assembly sequence planning problem with user preferences
Deep reinforcement learning (DRL) has demonstrated its potential in solving complex
manufacturing decision-making problems, especially in a context where the system learns …
manufacturing decision-making problems, especially in a context where the system learns …
Multi agent reinforcement learning for online layout planning and scheduling in flexible assembly systems
L Kaven, P Huke, A Göppert, RH Schmitt - Journal of Intelligent …, 2024 - Springer
Manufacturing systems are undergoing systematic change facing the trade-off between the
customer's needs and the economic and ecological pressure. Especially assembly systems …
customer's needs and the economic and ecological pressure. Especially assembly systems …
Deep reinforcement learning in smart manufacturing: A review and prospects
To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
[HTML][HTML] Reinforcement Learning in Manufacturing Control: Baselines, challenges and ways forward
Abstract The field of Neural Combinatorial Optimization (NCO) offers multiple learning-
based approaches to solve well-known combinatorial optimization tasks such as Traveling …
based approaches to solve well-known combinatorial optimization tasks such as Traveling …
Deep reinforcement learning in production systems: a systematic literature review
Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …
challenges for production systems. These not only have to cope with an increased product …
A deep reinforcement learning based hyper-heuristic for modular production control
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly
configurable products require an adaptive and robust control approach to maintain …
configurable products require an adaptive and robust control approach to maintain …