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 language | English |
|---|---|
| Pages (from-to) | 503-511 |
| Number of pages | 9 |
| Journal | International Journal of Production Research |
| Volume | 30 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1992 |
| Externally published | Yes |
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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 journal › Article
}
TY - JOUR
T1 - Scheduling for minimizing total actual flow time by neural networks
AU - Arizono, Ikuo
AU - Ohta, Hiroshi
AU - Yamamoto, Akio
PY - 1992
Y1 - 1992
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0026836078&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0026836078&partnerID=8YFLogxK
U2 - 10.1080/00207549208942908
DO - 10.1080/00207549208942908
M3 - Article
AN - SCOPUS:0026836078
VL - 30
SP - 503
EP - 511
JO - International Journal of Production Research
JF - International Journal of Production Research
SN - 0020-7543
IS - 3
ER -