Title :
A neural network architecture for faster dynamic scheduling in manufacturing systems
Author :
Dagli, Cihan ; Huggahalli, Ram
Author_Institution :
Dept. of Eng. Manage., Missouri Univ., Rolla, MO, USA
Abstract :
Computation of optimum schedules for dynamic scheduling of tasks introduces an overhead delay in the processing time of the programs executed in an automated system. This is particularly so if serial scheduling algorithms are used. It is proposed that tasks be dynamically scheduled with the Lawler scheduling algorithm and that, to minimise the additional delay due to the computation of optimum schedules, a neural network be used for retrieving optimal solutions. A neural network architecture that can recognise binary vector representations of scheduling problems and retrieve optimal schedules in negligible time is described
Keywords :
manufacturing data processing; neural nets; optimisation; parallel architectures; production control; scheduling; Lawler scheduling algorithm; binary vector representations; dynamic scheduling; manufacturing systems; neural network architecture; optimisation; overhead delay; production control; Computer aided manufacturing; Computer networks; Dynamic scheduling; High performance computing; Intelligent networks; Job shop scheduling; Manufacturing systems; Neural networks; Optimal scheduling; Processor scheduling;
Conference_Titel :
Robotics and Automation, 1991. Proceedings., 1991 IEEE International Conference on
Conference_Location :
Sacramento, CA
Print_ISBN :
0-8186-2163-X
DOI :
10.1109/ROBOT.1991.131983