Scheduling for minimizing total actual flow time by neural networks

DOI10.1080/00207549208942908zbMath0756.90048OpenAlexW2080033990MaRDI QIDQ4005626

Akio Yamamoto, Hiroshi Ohta, Ikuo Arizono

Publication date: 27 September 1992

Published in: International Journal of Production Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207549208942908

zbMATH Keywords

artificial neural network systemsGaussian machinetotal actual flow time

Mathematics Subject Classification ID

Stochastic network models in operations research (90B15) Deterministic scheduling theory in operations research (90B35) Combinatorial optimization (90C27) Neural networks for/in biological studies, artificial life and related topics (92B20) Computational methods for problems pertaining to operations research and mathematical programming (90-08)

Related Items (8)

A GA-based NN approach for makespan estimation ⋮ Metaheuristics: A bibliography ⋮ Sequencing jobs on a single machine: A neural network approach ⋮ Applications of neural networks in manufacturing: a state-of-the-art survey ⋮ A neural network model for on-line control of flexible manufacturing systems ⋮ A neural network model for scheduling problems ⋮ An intelligent controller for manufacturing cells ⋮ Makespan estimation in batch process industries: A comparison between regression analysis and neural networks

Cites Work

• A logical calculus of the ideas immanent in nervous activity

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