Scaling properties of neural networks for job-shop scheduling

Simon Y. Foo, Yoshiyasu Takefuji, Harold Szu

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


This paper investigates the scaling properties of neural networks for solving job-shop scheduling problems. Specifically, the Tank-Hopfield linear programming network is modified to solve mixed integer linear programming with the addition of step-function amplifiers. Using a linear energy function, our approach avoids the traditional problems associated with most Hopfield networks using quadratic energy functions. Although our approach requires more hardware (in terms of processing elements and resistive interconnects) than a recent approach by Zhou et al. [2], 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)79-91
Number of pages13
Issue number1
Publication statusPublished - 1995 May
Externally publishedYes


  • Mixed integer linear programing
  • Optimization
  • Scheduling
  • Tank-Hopfield neural networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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