A performance analysis of dispatch rules for semiconductor assembly & test operations
Shihui Jia,
Douglas J. Morrice and
Jonathan F. Bard
Journal of Simulation, 2019, vol. 13, issue 3, 163-180
Abstract:
The purpose of this paper is to statistically compare six dispatch rules for semiconductor assembly & test operations with respect to two hierarchical objectives: minimise the weighted sum of key device (chip) shortages and maximise the weighted throughput of lots processed. Three factors affecting system performances are studied: the different rules, whether the machines are set up at time zero, and whether the maximum number of changeovers is restricted over the planning horizon. System performance is determined with both a discrete event AutoSched AP simulation model and a greedy randomised adaptive search procedure (GRASP) for 30 real and randomly generated data sets. A full factorial design with common random numbers was conducted to concurrently investigate the interaction among the three factors. The statistical analysis showed that the two rules designed to process as many hot lots (i.e., key device lots) as possible performed the best in minimising the weighted shortage. We also found that initial setups and limiting the number of setups over a 2- to 3-day planning horizon can significantly weaken the rules’ performance but in some cases the interaction of these factors mitigated their negative impacts. Due to the general nature of the models, the results should be applicable to most reentrant flow shops. The GRASP is now being used by the sponsoring company and the most promising dispatch rules are being studied in an experimental environment.
Date: 2019
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/17477778.2018.1453255 (text/html)
Access to full text is restricted to subscribers.
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:taf:tjsmxx:v:13:y:2019:i:3:p:163-180
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjsm20
DOI: 10.1080/17477778.2018.1453255
Access Statistics for this article
Journal of Simulation is currently edited by Christine Currie
More articles in Journal of Simulation from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().