Scheduling for minimizing total actual flow time by neural networks

Ikuo Arizono, Hiroshi Ohta, Akio Yamamoto

Research output: Contribution to journalArticle

43 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)503-511
Number of pages9
JournalInternational Journal of Production Research
Volume30
Issue number3
DOIs
Publication statusPublished - 1992
Externally publishedYes

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Combinatorial optimization
Scheduling
Neural networks
Optimization problem
Flow time
Network model
Artificial neural network

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Management Science and Operations Research
  • Strategy and Management

Cite this

Scheduling for minimizing total actual flow time by neural networks. / Arizono, Ikuo; Ohta, Hiroshi; Yamamoto, Akio.

In: International Journal of Production Research, Vol. 30, No. 3, 1992, p. 503-511.

Research output: Contribution to journalArticle

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