Stochastic neural networks for solving job-shop scheduling. II - Architecture and simulations

Yoon Pin Simon Foo, Yoshiyasu Takefuji

Research output: Contribution to conferencePaperpeer-review

49 Citations (Scopus)


The authors introduce a neural computation architecture based on a stochastic Hopfield neural network model for solving job-shop scheduling. A computation circuit computes the total completion times (costs) of all jobs, and the cost difference is added to the energy function of the stochastic neural network. Using a simulated annealing algorithm, the temperature of the system is slowly decreased according to an annealing schedule until the energy of the system is at a local or global minimum. By choosing an appropriate annealing schedule, near-optimal and optimal solutions to job-shop problems can be found. The architecture of the system is diagrammed at both the functional and circuit levels. Simulation results are presented.

Original languageEnglish
Number of pages8
Publication statusPublished - 1988 Dec 1
Externally publishedYes

ASJC Scopus subject areas

  • Engineering(all)


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