Estimating expected completion times with probabilistic job routing
Pages 1804 - 1810
Abstract
A common problem in production environments is the need to estimate the remaining time in system for work-in-progress jobs. Simulation can be used to obtain the estimates. However, when the future path of a job is uncertain (due to stochastic events such as rework), using simulation to estimate the remaining cycle time of a job at step k can be imprecise; traditional confidence intervals on the estimated remaining cycle times may be too large to be of practical significance. We propose a response surface methodology-based approach to estimating conditional confidence intervals on the remaining cycle times as jobs progress through the system and more information is obtained on them. This method will provide more useful and accurate estimates of remaining cycle times at various stages of the process flow. Further, we outline two different simulation approaches for estimating the response surfaces used to generate the confidence intervals.
References
[1]
Backus, P., M. Janakiram, S. Mowzoon, G. C. Runger, and A. Bhargava. 2006. Factory Cycle-Time Prediction with a Data-Mining Approach. IEEE Transactions on Semiconductor Manufacturing. 19(2): 252--8.
[2]
Box, G. E. P. and K. B. Wilson. 1951. On the Experimental Attainment of Optimum Conditions. Journal of the Royal Statistical Society B.13: 1--45.
[3]
Buzacott, J. A. and J. G. Shanthikumar. 1993. Stochastic Models of Manufacturing Systems. W. J. Farbrycky and J. H. Mize. Prentice Hall International Series in Industrial and Systems Engineering. Prentice-Hall, Inc. Upper Saddle River, NJ.
[4]
Cheng, R. C. H. 2005. Bootstrapping Simultaneous Confidence Bands. Proceedings of the 2005 Winter Simulation Conference, eds. M. E. Kuhl, N. M. Steiger, F. B. Armstrong and J. A. Joines. IEEE. 240--7.
[5]
Cheng, R. C. H. and J. P. C. Kleijnen. 1999. Improved Design of Queueing Simulation Experiments with Highly Heteroscedastic Responses. Operations Research. 47(5): 762--77.
[6]
Fleming, P. J. and B. Simon. 1991. Interpolation Approximations of Sojourn Time Distributions. Operations Research. 39(2): 251--60.
[7]
Govind, N. and D. Fronckowiak. 2003. Resident-Entity Based Simulation of Batch Chamber Tools in 300mm Semiconductor Manufacturing. Proceedings of the 2003 Winter Simulation Conference, eds. S. E. Chick, P. J. Sanchez, D. Ferrin and D. J. Morrice. IEEE. 1398--405.
[8]
Hasan, N. C. and M. L. Spearman. 1995. Determining Job Completion Time Distributions in Stochastic Production Environments. Proceedings of the 1995 Winter Simulation Conference, eds. C. Alexopoulos, K. Kang, R. Lilegdon and D. Goldsman. 837--45.
[9]
Hung, Y.-C., G. Michailidis, and D. R. Bingham. 2003. Developing Efficient Simulation Methodology for Complex Queueing Networks. Proceedings of the 2003 Winter Simulation Conference, eds. S. E. Chick, P. J. Sanchez, D. Ferrin and D. J. Morrice. IEEE. 512--9.
[10]
Johnson, R. T., S. E. Leach, J. W. Fowler, and G. T. Mackulak. 2004. Variance-Based Sampling for Cycle Time - Throughput Confidence Intervals. Proceedings of the 2004 Winter Simulation Conference, eds. R. G. Ingalls, M. D. Rossetti, J. S. Smith and B. A. Peters. 716--20.
[11]
Liao, D.-Y. and C.-N. Wang. 2004. Neural-Network-Based Delivery Time Estimates for Prioritized 300-Mm Automatic Material Handling Operations. IEEE Transactions on Semiconductor Manufacturing. 17(3): 324--32.
[12]
Little, J. D. 1961. A Proof of the Queueing Formula: L = Lw. Operations Research. 9: 383--7.
[13]
Myers, R. H. and W. H. J. Carter. 1973. Response Surface Techniques for Dual Response Systems. Technometrics. 15: 301--17.
[14]
Roeder, T. M. 2004. An Information Taxonomy for Discrete Event Simulations. Ph.D. Dissertation, Department of Industrial Engineering and Operations Research, University of California, Berkeley. Berkeley, CA.
[15]
Roeder, T. M., S. A. Fischbein, M. Janakiram, and L. W. Schruben. 2002. Resource-Driven and Job-Driven Simulations. Proceedings of the 2002 International Conference on Modeling and Analysis of Semiconductor Manufacturing. 78--83.
[16]
Roeder, T. M., N. Govind, and L. W. Sehruben. 2004. A Queueing Network Approximation of Semiconductor Automated Material Handling Systems: How Much Information Do We Really Need? Proceedings of the 2004 Winter Simulation Conference, eds. R. G. Ingalls, M. D. Rossetti, J. S. Smith and B. A. Peters. 1956--61.
[17]
Sabuncuoglu, I. and A. Comlekci. 2002. Operation-Based Flowtime Estimation in a Dynamic Job Shop. Omega. 30(6): 423--42.
[18]
Schruben, D. and L. W. Schruben. 2001. Graphical Modeling Using SIGMA. 4th Edition. Custom Simulations.
[19]
Sha, D. Y. and C.-H. Liu. 2005. Using Data Mining for Due Date Assignment in a Dynamic Job Shop Environment. The International Journal of Advanced Manufacturing Technology. 25(11--12): 1164--74.
[20]
Shanthikumar, J. G. and U. Sumita. 1988. Approximations for the Time Spent in a Dynamic Job Shop with Applications to Due-Date Assignment. International Journal of Production Research. 26(8): 1329--52.
[21]
Strelen, J. C. 2004. The Accuracy of a New Confidence Interval Method. Proceedings of the 2004 Winter Simulation Conference, eds. R. G. Ingalls, M. D. Rossetti, J. S. Smith and B. A. Peters. 654--62.
[22]
Vandaele, N., L. De Boeck, and D. Callewier. 2002. An Open Queueing Network for Lead Time Analysis. IIE Transactions. 34(1): 1--9.
[23]
Whitt, W. 1983. Queueing Network Analyzer. Bell System Technical Journal. 62(9): 2779--815.
- Estimating expected completion times with probabilistic job routing
Recommendations
Minimizing Total Completion Time Subject to Job Release Dates and Preemption Penalties
Extensive research has been devoted to preemptive scheduling. However, little attention has been paid to problems where a certain time penalty must be incurred if preemption is allowed. In this paper, we consider the single-machine scheduling problem of ...
Total absolute deviation of job completion times on uniform and unrelated machines
Unlike other measures of variation of job completion times considered in scheduling literature, the measure of minimizing total absolute deviation of job completiontimes (TADC) was shown to have a polynomial time solution on a single machine. It was ...
Single machine scheduling with simple linear deterioration to minimize total absolute deviation of completion times
We consider a single machine scheduling problem with simple linear deterioration. Job processing times are assumed to be a simple linear function of a job-dependent growth rate and the job's starting time. We seek an optimal schedule, so as to minimize ...
Comments
Information & Contributors
Information
Published In
December 2006
2429 pages
ISBN:1424405017
- Editors:
- L. Felipe Perrone,
- Barry G. Lawson,
- Jason Liu,
- Frederick P. Wieland,
- General Chair:
- David Nicol,
- Program Chair:
- Richard Fujimoto
Sponsors
- IIE: Institute of Industrial Engineers
- ASA: American Statistical Association
- IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
- IEEE-CS\DATC: The IEEE Computer Society
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
- NIST: National Institute of Standards and Technology
- (SCS): The Society for Modeling and Simulation International
- INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation
Publisher
Winter Simulation Conference
Publication History
Published: 03 December 2006
Check for updates
Qualifiers
- Article
Conference
WSC06
Sponsor:
- IIE
- ASA
- IEICE ESS
- IEEE-CS\DATC
- SIGSIM
- NIST
- (SCS)
- INFORMS-CS
Acceptance Rates
WSC '06 Paper Acceptance Rate 177 of 252 submissions, 70%;
Overall Acceptance Rate 3,413 of 5,075 submissions, 67%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 106Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 07 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in