Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities
With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
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 …
Privacy-preserving traffic flow prediction: A federated learning approach
Existing traffic flow forecasting approaches by deep learning models achieve excellent
success based on a large volume of data sets gathered by governments and organizations …
success based on a large volume of data sets gathered by governments and organizations …
Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning
L Wang, X Hu, Y Wang, S Xu, S Ma, K Yang, Z Liu… - Computer Networks, 2021 - Elsevier
Job-shop scheduling problem (JSP) is used to determine the processing order of the jobs
and is a typical scheduling problem in smart manufacturing. Considering the dynamics and …
and is a typical scheduling problem in smart manufacturing. Considering the dynamics and …
Finding key players in complex networks through deep reinforcement learning
Finding an optimal set of nodes, called key players, whose activation (or removal) would
maximally enhance (or degrade) a certain network functionality, is a fundamental class of …
maximally enhance (or degrade) a certain network functionality, is a fundamental class of …
Knowledge-based reinforcement learning and estimation of distribution algorithm for flexible job shop scheduling problem
Y Du, J Li, X Chen, P Duan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Inthis study, a flexible job shop scheduling problem with time-of-use electricity price
constraint is considered. The problem includes machine processing speed, setup time, idle …
constraint is considered. The problem includes machine processing speed, setup time, idle …
UAV trajectory planning in wireless sensor networks for energy consumption minimization by deep reinforcement learning
Unmanned aerial vehicles (UAVs) have emerged as a promising candidate solution for data
collection of large-scale wireless sensor networks (WSNs). In this paper, we investigate a …
collection of large-scale wireless sensor networks (WSNs). In this paper, we investigate a …
Analysis and control of autonomous mobility-on-demand systems
Challenged by urbanization and increasing travel needs, existing transportation systems
need new mobility paradigms. In this article, we present the emerging concept of …
need new mobility paradigms. In this article, we present the emerging concept of …
Solving dynamic traveling salesman problems with deep reinforcement learning
A traveling salesman problem (TSP) is a well-known NP-complete problem. Traditional TSP
presumes that the locations of customers and the traveling time among customers are fixed …
presumes that the locations of customers and the traveling time among customers are fixed …