Scheduling for minimising total actual flow time by neural networks

Scheduling for minimising total actual flow time by neural networks

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Article ID: iaor1992969
Country: United Kingdom
Volume: 30
Issue: 3
Start Page Number: 503
End Page Number: 511
Publication Date: Mar 1992
Journal: International Journal of Production Research
Authors: , ,
Keywords: neural networks, networks: scheduling
Abstract:

Scheduling problems are considered as combinatorial optimization problems. Hopfield and Tank 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.

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