Scheduling for minimizing total actual flow time by neural networks
作者:
IKUO ARIZONO,
AKIO YAMAMOTO,
HIROSHI OHTA,
期刊:
International Journal of Production Research
(Taylor Available online 1992)
卷期:
Volume 30,
issue 3
页码: 503-511
ISSN:0020-7543
年代: 1992
DOI:10.1080/00207549208942908
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Scheduling problems are considered as combinatorial optimization problems. Hopfield and Tank (1985) showed that some combinatorial optimization problems can be solved using artificial neural network systems. However, their network model for solving the combinatorial optimization problems often attains a local optimum solution depending on the initial state of the network. Recently, some stochastic neural network models have been proposed for the purpose of avoiding convergence to a local optimum solution. In this paper a scheduling problem for minimizing the total actual flow time is solved by using the Gaussian machine model which is one of the stochastic neural network models.
点击下载:
PDF (268KB)
返 回