Using simulation and neural networks to develop a scheduling advisor
UNSPECIFIED (2001) Using simulation and neural networks to develop a scheduling advisor. In: Winter Simulation Conference (WSC 01), 2001, ARLINGTON, VA.Full text not available from this repository.
The research using artificial intelligence and computer simulation introduces a new approach for solving the job shop-scheduling problem. The new approach is based on the development of a neural network-scheduling advisor, which is trained using optimal scheduling decisions. The data set, which is used to train the neural network, is obtained from simulation experiments with small-scale job shop scheduling problems. The paper formulates the problem and after a review of the current solution methods it describes the steps of a new methodology for developing the neural network-scheduling advisor and collecting the data required for its training. The paper concludes by mentioning the expected findings that can be used to evaluate the degree of success of the new methodology.
|Item Type:||Conference Item (UNSPECIFIED)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software|
|Journal or Publication Title:||WSC'01: PROCEEDINGS OF THE 2001 WINTER SIMULATION CONFERENCE, VOLS 1 AND 2|
|Editor:||Peters, BA and Smith, JS and Medeiros, DJ and Rohrer, MW|
|Number of Pages:||5|
|Page Range:||pp. 954-958|
|Title of Event:||Winter Simulation Conference (WSC 01)|
|Location of Event:||ARLINGTON, VA|
|Date(s) of Event:||2001|
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