Asymmetric cycle time bounding in semiconductor manufacturing: an efficient and effective back-propagation-network-based method
Toly Chen ()
Additional contact information
Toly Chen: Feng Chia University
Operational Research, 2016, vol. 16, issue 3, No 4, 445-468
Abstract:
Abstract Estimating the cycle time of every job in a semiconductor manufacturing factory is essential. However, because cycle times are uncertain, determining the cycle time range is also crucial. Manufactures use the lower bound of a cycle time to promise an attractive due date to customers and the upper bound to indicate the latest possible time that fabrication will be completed. Thus, an interval estimate of cycle time is preferable to a point estimate. In several previous studies, a symmetric interval estimate has been derived from a probabilistic perspective. However, the managerial implications of the upper and lower bounds differ, and an asymmetric interval estimate is more useful. Therefore, this paper proposes a back-propagation-network-based approach to estimating the job cycle time and determining the cycle time range. A real case from a semiconductor manufacturing factory was used to illustrate the proposed method. According to the results, the estimates of the job cycle time obtained using the proposed method were precise and accurate. The established upper and lower bounds (especially the lower bound) were much tighter than those used in existing methods.
Keywords: Cycle time; Range semiconductor manufacturing; Asymmetric; Back propagation network (BPN) (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://link.springer.com/10.1007/s12351-015-0208-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:operea:v:16:y:2016:i:3:d:10.1007_s12351-015-0208-7
Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351
DOI: 10.1007/s12351-015-0208-7
Access Statistics for this article
Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis
More articles in Operational Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().