ARTIFICIAL INTELLIGENCE-BASED SCHEDULING

Authors

  • Andrii Kobyliuk National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine
  • Artem Volokyta National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine

Keywords:

artificial intelligence, neural network, scheduling, task, enetic algorithm

Abstract

The  paper considers the algorithm for scheduling jobs in a computer system using artificial intelligence. The two-tier convolutional neural network chose the best heuristic algorithm. For training the NN is used genetic algorithm. The model is trained on the sets of random generated jobs.

References

J. N. D., Gupta, E. A., Tunc (1998). Minimizing Tardy Jobs in a Two-stage Hybrid Flowshop, International Journal of Production Research 36 (pp. 2397 – 2417).

Błażewicz, Jacek, Ecker, K. H., Pesch, E., Schmidt, G., Weglarz, J. (2001). Scheduling computer and manufacturing processes (2 ed.).

L. I., Burke, J. P., Ignizio (1992). Neural Networks and Operations Research: An Overview, Computers and Operations Research 19 (pp. 179 -189).

Schmidhuber, Jürgen (2015). "Deep Learning". Scholarpedia (pp. 85–117).

Jatinder, N. D., Gupta, Randall, S., Sexton, Enar, A., Tunc (2000, January). Selecting Scheduling Heuristics Using Neural Networks (pp. 2-22).

D., Tuzun, M. A., Magent, L. I., Burke (1997). Selection of Vehicle Routing Heuristics Using Heuristic Networks, International Transactions on Operations Research 4 (pp. 211-221).

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Published

2023-06-08

Issue

Section

Machine learning, Big Data