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 …
Resource allocation and task scheduling in fog computing and internet of everything environments: A taxonomy, review, and future directions
The Internet of Everything paradigm is being rapidly adopted in develo** applications for
different domains like smart agriculture, smart city, big data streaming, and so on. These IoE …
different domains like smart agriculture, smart city, big data streaming, and so on. These IoE …
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 …
[HTML][HTML] A review of optimization methods for computation offloading in edge computing networks
Handling the massive amount of data generated by Smart Mobile Devices (SMDs) is a
challenging computational problem. Edge Computing is an emerging computation paradigm …
challenging computational problem. Edge Computing is an emerging computation paradigm …
Machine learning-based orchestration of containers: A taxonomy and future directions
Containerization is a lightweight application virtualization technology, providing high
environmental consistency, operating system distribution portability, and resource isolation …
environmental consistency, operating system distribution portability, and resource isolation …
Edge intelligence: A computational task offloading scheme for dependent IoT application
Computational offloading, as an effective way to extend the capability of resource-limited
edge devices in Internet of Things (IoT), is considered as a promising emerging paradigm for …
edge devices in Internet of Things (IoT), is considered as a promising emerging paradigm for …
Optimization of global production scheduling with deep reinforcement learning
Abstract Industrie 4.0 introduces decentralized, self-organizing and self-learning systems for
production control. At the same time, new machine learning algorithms are getting …
production control. At the same time, new machine learning algorithms are getting …
From cloud down to things: An overview of machine learning in internet of things
With the numerous Internet of Things (IoT) devices, the cloud-centric data processing fails to
meet the requirement of all IoT applications. The limited computation and communication …
meet the requirement of all IoT applications. The limited computation and communication …
Application placement in Fog computing with AI approach: Taxonomy and a state of the art survey
With the increasing use of the Internet of Things (IoT) in various fields and the need to
process and store huge volumes of generated data, Fog computing was introduced to …
process and store huge volumes of generated data, Fog computing was introduced to …
Saving time and cost on the scheduling of fog-based IoT applications using deep reinforcement learning approach
Due to the rapid growth of intelligent devices and the Internet of Things (IoT) applications in
recent years, the volume of data that is generated by these devices is increasing …
recent years, the volume of data that is generated by these devices is increasing …