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 …
Resource scheduling in edge computing: A survey
With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless
networks, the surging demand for data communications and computing calls for the …
networks, the surging demand for data communications and computing calls for the …
Machine Learning for industrial applications: A comprehensive literature review
M Bertolini, D Mezzogori, M Neroni… - Expert Systems with …, 2021 - Elsevier
Abstract Machine Learning (ML) is a branch of artificial intelligence that studies algorithms
able to learn autonomously, directly from the input data. Over the last decade, ML …
able to learn autonomously, directly from the input data. Over the last decade, ML …
Industrial applications of digital twins
A digital twin (DT) is classically defined as the virtual replica of a real-world product, system,
being, communities, even cities that are continuously updated with data from its physical …
being, communities, even cities that are continuously updated with data from its physical …
A reinforcement learning based RMOEA/D for bi-objective fuzzy flexible job shop scheduling
The flexible job shop scheduling problem (FJSP) is significant for realistic manufacturing.
However, the job processing time usually is uncertain and changeable during …
However, the job processing time usually is uncertain and changeable during …
Learning to dispatch for job shop scheduling via deep reinforcement learning
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem
This paper presents an end-to-end deep reinforcement framework to automatically learn a
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems
Y Zhang, H Zhu, D Tang, T Zhou, Y Gui - Robotics and Computer-Integrated …, 2022 - Elsevier
Personalized orders bring challenges to the production paradigm, and there is an urgent
need for the dynamic responsiveness and self-adjustment ability of the workshop …
need for the dynamic responsiveness and self-adjustment ability of the workshop …
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 …
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 …