Job-shop scheduling based on modified tank-hopfield linear programming networks

Simon Y. Foo, Yoshiyasu Takefuji, Harold Szu

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

The Tank-Hopfield linear programming network is modified to solve job-shop scheduling, a classical optimization problem. Using a linear energy function, the approach described in this paper avoids the traditional problems associated with most Hopfield networks using quadratic energy functions. Although this approach requires more hardware (in terms of processing elements and resistive interconnects) than a recent approach by Zhou et al. (IEEE Trans. Neural Networks 2, 175-179, 1991) the neurons in the modified Tank-Hopfield network do not perform extensive calculations, unlike those described by Zhou et al.

Original languageEnglish
Pages (from-to)321-327
Number of pages7
JournalEngineering Applications of Artificial Intelligence
Volume7
Issue number3
DOIs
Publication statusPublished - 1994
Externally publishedYes

Fingerprint

Linear programming
Neurons
Neural networks
Hardware
Processing
Job shop scheduling

Keywords

  • Hopfield neural networks
  • Job-shop scheduling
  • mixed integer-linear programming

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

Cite this

Job-shop scheduling based on modified tank-hopfield linear programming networks. / Foo, Simon Y.; Takefuji, Yoshiyasu; Szu, Harold.

In: Engineering Applications of Artificial Intelligence, Vol. 7, No. 3, 1994, p. 321-327.

Research output: Contribution to journalArticle

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