@inproceedings{e99adb33bab243f28062dd334bb7e9aa,
title = "A demonstration of machine learning for explicit functions for cycle time prediction using MES data",
abstract = "Cycle time prediction represents a challenging problem in complex manufacturing scenarios. This paper demonstrates an approach that uses genetic programming (GP) and effective process time (EPT) to predict cycle time using a discrete event simulation model of a production line, an approach that could be used in complex manufacturing systems, such as a semiconductor fab. These predictive models could be used to support control and planning of manufacturing systems. GP results in a more explicit function for cycle time prediction. The results of the proposed approach show a difference between 1-6% on the demonstrated production line.",
author = "Birkan Can and Cathal Heavey",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 Winter Simulation Conference, WSC 2016 ; Conference date: 11-12-2016 Through 14-12-2016",
year = "2016",
month = jul,
day = "2",
doi = "10.1109/WSC.2016.7822289",
language = "English",
series = "Proceedings - Winter Simulation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2500--2511",
editor = "Roeder, {Theresa M.} and Frazier, {Peter I.} and Robert Szechtman and Enlu Zhou",
booktitle = "2016 Winter Simulation Conference",
}