Results 201 to 210 of about 3,088,335 (262)

Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0

Computers & industrial engineering, 2020
Driven by the recent advances in industry 4.0 and industrial artificial intelligence, Automated Guided Vehicles (AGVs) has been widely used in flexible shop floor for material handling.
Hao Hu, Xiaoliang Jia, Qixuan He, Shi-Yi Fu, Kuo Liu   +4 more
semanticscholar   +1 more source

Automatic detection of the best performing priority rule for the resource-constrained project scheduling problem

Expert systems with applications, 2020
Priority rules are applied in many commercial software tools for scheduling projects under limited resources because of their known advantages such as the ease of implementation, their intuitive working, and their fast speed.
Weikang Guo, M. Vanhoucke, J. Coelho, Jingyu Luo   +3 more
semanticscholar   +1 more source

Dynamic Scheduling of Multiclass Many-Server Queues with Abandonment: The Generalized cμ/h Rule

Operational Research, 2020
In “Dynamic Scheduling of Multiclass Many-Server Queues with Abandonment: The Generalized cμ/h Rule,” Long, Shimkin, Zhang, and Zhang propose three scheduling policies to cope with any general cost...
Zhenghua Long, N. Shimkin, Hailun Zhang, Jiheng Zhang   +3 more
semanticscholar   +1 more source

A Johnson's-Rule-Based Genetic Algorithm for Two-Stage-Task Scheduling Problem in Data-Centers of Cloud Computing

IEEE Transactions on Cloud Computing, 2019
One of the keys to making cloud data-centers (CDCs) proliferate impressively is the implementation of efficient task scheduling. Since all the resources of CDCs, even including operating systems (OSes) and application programs, can be stored and managed ...
Yonghua Xiong, Suzhen Huang, Min Wu, Jinhua She, Keyuan Jiang   +4 more
semanticscholar   +1 more source

Real-Time Scheduling for Dynamic Partial-No-Wait Multiobjective Flexible Job Shop by Deep Reinforcement Learning

IEEE Transactions on Automation Science and Engineering, 2022
In modern discrete flexible manufacturing systems, dynamic disturbances frequently occur in real time and each job may contain several special operations in partial-no-wait constraint due to technological requirements.
Shu Luo, Linxuan Zhang, Yushun Fan
semanticscholar   +1 more source

A Reinforcement Learning Approach for Flexible Job Shop Scheduling Problem With Crane Transportation and Setup Times

IEEE Transactions on Neural Networks and Learning Systems, 2022
Flexible job shop scheduling problem (FJSP) has attracted research interests as it can significantly improve the energy, cost, and time efficiency of production.
Yu Du, Jun-Qiang Li, Cheng-dong Li, Peiyong Duan   +3 more
semanticscholar   +1 more source

Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning

Applied Soft Computing, 2020
In modern manufacturing industry, dynamic scheduling methods are urgently needed with the sharp increase of uncertainty and complexity in production process. To this end, this paper addresses the dynamic flexible job shop scheduling problem (DFJSP) under
Shu Luo
semanticscholar   +1 more source