Results 301 to 310 of about 5,322,533 (364)

Deep reinforcement learning for dynamic scheduling of a flexible job shop

International Journal of Production Research, 2022
The ability to handle unpredictable dynamic events is becoming more important in pursuing agile and flexible production scheduling. At the same time, the cyber-physical convergence in production system creates massive amounts of industrial data that ...
Renke Liu, R. Piplani, C. Toro
semanticscholar   +1 more source

Dynamic Scheduling and Path Planning of Automated Guided Vehicles in Automatic Container Terminal

IEEE/CAA Journal of Automatica Sinica, 2022
The uninterrupted operation of the quay crane (QC) ensures that the large container ship can depart port within lay-time, which effectively reduces the handling cost for the container terminal and ship owners.
Lijun Yue, H. Fan
semanticscholar   +1 more source

Dynamic Scheduling, Operation Control and Their Integration in High-Speed Railways: A Review of Recent Research

IEEE transactions on intelligent transportation systems (Print), 2022
Railway system performances depend on effective dynamic scheduling and train operation control. The fast expansion and increasing complexity of high-speed railway (HSR) networks raise new challenges in maintaining the punctuality and efficiency in daily ...
Xuewu Dai, Hui Zhao, Shengping Yu, D. Cui, Qi Zhang, Hai-rong Dong, T. Chai   +6 more
semanticscholar   +1 more source

Dynamic Scheduling for Heterogeneous Federated Learning in Private 5G Edge Networks

IEEE Journal on Selected Topics in Signal Processing, 2022
Private 5G edge networks support secure and private service, spectrum flexibility, and edge intelligence. In this paper, we aim to design a dynamic scheduling policy to explore the spectrum flexibility for heterogeneous federated learning (FL) in private
Kun Guo, Zihan Chen, H. Yang, Tony Q. S. Quek   +3 more
semanticscholar   +1 more source

Security-Aware Dynamic Scheduling for Real-Time Optimization in Cloud-Based Industrial Applications

IEEE Transactions on Industrial Informatics, 2021
Nowadays, large number of cloud-based techniques have been used in industrial control systems (ICS), which also brings many security threats. The emergence of security-aware industrial control has paved the way of security-aware scheduling in cloud-based
Shunmei Meng, Weijia Huang, Xiaochun Yin, M. Khosravi, Qianmu Li, Shaohua Wan, Lianyong Qi   +6 more
semanticscholar   +1 more source

Dynamic Scheduling and Routing for TSN based In-vehicle Networks

2021 IEEE International Conference on Communications Workshops (ICC Workshops), 2021
The future autonomous vehicle is not only processing the copious amount of indispensable data generated by its onboard sensors but also utilizing the data from other vehicles, roadside unit (RSU) etc.
Ammad Ali Syed, S. Ayaz, T. Leinmüller, Madhu Chandra   +3 more
semanticscholar   +1 more source

Dynamic Scheduling for Emergency Tasks in Space Data Relay Network

IEEE Transactions on Vehicular Technology, 2021
As a platform for data transmission, space data relay network (SDRN), which has high coverage and continuous communications, provides data relay services for the spacecrafts and satellites by allocating antenna resource.
Cuiqin Dai, Chong Li, Shu Fu, Jian Zhao, Qianbin Chen   +4 more
semanticscholar   +1 more source

A Machine Learning Approach for Energy-Efficient Intelligent Transportation Scheduling Problem in a Real-World Dynamic Circumstances

IEEE transactions on intelligent transportation systems (Print), 2023
This paper provides a novel intelligent scheduling strategy for a real-world transportation dynamic scheduling case from an engine workshop of general motor company (GMEW), which is a key production line throughout the manufacturing process.
Jianhui Mou, Kaizhou Gao, Peiyong Duan, Jun-Qiang Li, A. Garg, Rohit Sharma   +5 more
semanticscholar   +1 more source

Dynamics in scheduled networks

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2009
When studying real or virtual systems through complex networks theories, usually time restrictions are neglected, and a static structure is defined to characterize which node is connected to which other. However, this approach is oversimplified, as real networks are indeed dynamically modified by external mechanisms. In order to bridge the gap, in this
Massimiliano, Zanin, Lucas, Lacasa, Miguel, Cea   +2 more
openaire   +2 more sources

Deep Learning-Based Dynamic Scheduling for Semiconductor Manufacturing With High Uncertainty of Automated Material Handling System Capability

IEEE transactions on semiconductor manufacturing, 2020
Recently, the transportation capability of the automated material handling system (AMHS) has emerged as a major barrier to the semiconductor fabrication facility (FAB), because it can limit the FAB production capacity.
H. Kim, Dae-Eun Lim, Sangmin Lee
semanticscholar   +1 more source