A New Analysis Technique of Hybrid Production Lines - Volume 4 Number 5 (Oct. 2014) - IJMLC
  • Aug 09, 2018 News! Vol. 6, No. 4-No. 7, No. 3 has been indexed by EI(Inspec)!   [Click]
  • Aug 09, 2018 News!Good News! All papers from Volume 8, Number 3 have been indexed by Scopus!   [Click]
  • May 23, 2018 News![CFP] 2018 the annual meeting of IJMLC Editorial Board, ACMLC 2018, will be held in Ho Chi Minh, Vietnam, December 7-9, 2018   [Click]
Search
General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol. 4(5): 445-449 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.452

A New Analysis Technique of Hybrid Production Lines

Jun Liu, Qinying Fan, and Junping Kong
Abstract—A new system aggregation technique was proposed to analyze hybrid production lines. Different from the traditional techniques, new lines consisting of “equivalent” machines were set up and the parameters of machines were renewed repeatedly rather than fixed in the process of whole aggregation. Meanwhile, forward aggregation and backward aggregation alternately proceeded until steady system parameters of production lines were gotten. The comparison analysis between the technique and the traditional aggregation technique was done by numerical experiments. The advantages and applying circumstance of the new aggregation technique were also specified by numerical results.

Index Terms—Hybrid production line, System performance, Aggregation technique.

Jun Liu is with Lanzhou University of Technology, 730050, Lanzhou, China (e-mail: lzhjliu@126.com).
Qinying Fan and Junping Kong are with Lanzhou University of Technology, 730050, Lanzhou, China (e-mail: 958174577@qq.com).

[PDF]

Cite: Jun Liu, Qinying Fan, and Junping Kong, "A New Analysis Technique of Hybrid Production Lines," International Journal of Machine Learning and Computing vol. 4, no. 5, pp. 445-449, 2014.

Copyright © 2008-2018. International Journal of Machine Learning and Computing. All rights reserved.
E-mail: ijmlc@ejournal.net