ACM Home Page
Please provide us with feedback. Feedback
Simulation and analysis of a circuit board manufacturing facility
Full text PdfPdf (905 KB)
Source Winter Simulation Conference archive
Proceedings of the 18th conference on Winter simulation table of contents
Washington, D.C., United States
Pages: 686 - 693  
Year of Publication: 1986
ISBN:0-911801-11-1
Authors
Steven F. Shevell  Corporate Operations Division, Gandalf Data Limited, 100 Colonnade Road North, Nepean, Ontario, Canada K2E 7M4
John A. Buzacott  Department of Management Sciences, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
Michael J. Magazine  Department of Management Sciences, University of Waterloo, Waterloo, Ontario, CANADA N2L 3GI
Sponsor
SIGSIM: ACM Special Interest Group on Simulation and Modeling
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 1,   Downloads (12 Months): 4,   Citation Count: 3
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/318242.318510
What is a DOI?

ABSTRACT

A simulation model is used to analyze the effects of various factors on the performance of a complex manufacturing system. The system under study is a large circuit board manufacturing facility. There, circuit boards are assembled and tested on a wide variety of automated machines and manual workstations. The simulation model, written in the SLAM II language, is highly detailed in the manner in which processes are modelled. This becomes especially important in modelling circuit board testing where boards which fail are repaired and recirculated through the test stations. Detailed modelling also allows for numerous process routings among the different product types to be permitted. The model possesses a demonstrated accuracy in its portrayal of the real-world situation. To make the most economical use of the model in the investigation of factor influence on system performance, experiments were conducted according to the principles of statistical experiment design. A 32-trial Hadamard design was employed to test the effects of such variables as lot size, order release schedules and quality on system performance. Performance measures included mean percent of work behind schedule, process flow time and in-process inventory levels. Significant results from these experiments are presented along with a set of guidelines, with respect to the factors investigated, which yielded favorable system performance results.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Baker, K.R. and Kanet, J.J. (1984). Improved decision rules in a c;ombined system for minimizing job tardir~ess, irnternat:ional Journal of Production Research 22:6,917-92}.
 
2
Buzacott, J.A. and Shanthikumar, J.G. (1985). On approximate queuelng mo4els of dynamic jcb shops. Management Science 31:7, 870-887.
 
3
Gershwin, S.B., Akella, R. and Choong, Y.F. (1985). Short-term production scheduling of an automated manufacturing facility. IBM Journal of Research and Development 29:4, 392-400.
 
4
Holoway, C.A. and Nelson, R.T. (1974). Job shop scheduling with due dates and variable processing times. Management Science 20:9, 1264-1272.
 
5
 
6
Schlessinger, S. et al. (19v9). Terminology for model credibility. Simulation 32:3, i0 3-104.
 
7
Schruben, L.W. (1980) . Establishing the credibility of simulations. Simulation 34:3, I01-i05.
 
8
Shevell, S.F. (1985). SimuLation and Analysis of a Circuit Board Manufacturing Facility. Unpublished M.A.Sc. Thesis, Department of Management Sciences, University of %Taterloo, Waterloo, Ontario, Canada.


Collaborative Colleagues:
Steven F. Shevell: colleagues
John A. Buzacott: colleagues
Michael J. Magazine: colleagues