Abstract
In today’s business environment, cycle time is a critical performance measure for manufacturing, and accurate estimation of cycle time–throughput (CT–TH) relationships plays an important role in successful production planning. To efficiently generate such comprehensive performance profiles, we propose a metamodeling approach, which integrates discrete-event simulation, adaptive statistical methods, and analytical queueing analysis. The resulting metamodels are mathematical equations quantifying the CT–TH relationships, and they have the “what-if” capability of tractable queueing models as well as the high fidelity of detailed simulation. In this chapter, methods for metamodeling CT–TH profiles are described for both single and multiproduct environments assuming that the manufacturing system is operated in steady state. As a future direction, the investigation of transient CT–TH behavior is also briefly discussed.
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References
Agrawal A, Minis I, Nagi R (2000) Cycle time reduction by improved MRP-based production planning. Int J Prod Res 28:4823–4841.
Asmundsson J, Rardin RL, Uzsoy R (2006) Tractable nonlinear production planning models for semiconductor wafer fabrication facilities. IEEE Trans Semicond Manufact 19:95–111.
Atherton RW, Dayhoff JE (1986) Signature analysis: simulation of inventory, cycle time and throughput trade-offs in wafer fabrication. IEEE Trans Components Hybrids Manufact Technol 9(4):498–507.
Barton RR (1998) Simulation metamodels. In: Medeiros DJ, Watson EF, Carson JS, Manivannan MS (eds) Proceedings of the 1998 winter simulation conference. IEEE, Piscataway, New Jersey, pp 167–174.
Barton RR, Meckesheimer M (2006) Metamodel-based simulation optimization. In: Henderson SG, Nelson BL (eds) Handbook in OR & MS 13. Elsevier, North Holland, pp 535–574.
Bekki JM, Fwoler JW, Mackulak GT, Nelson BL (2010) Indirect cycle time quantile estimation using the Cornish-Fisher expansion. IIE Trans 42:31–44.
Bekki JM, Fowler JW, Mackulak GT, Kulahci M (2009) Estimation of cycle time quantiles in manufacturing environments employing non-FIFO dispatching policies. J Simul 3:69–83.
Bekki J, Mackulak GT, Fowler JW (2006) Indirect cycle-time percentile Estimation for non-FIFO dispatching policies. In: Perrone LF, Wieland FP, Liu J, Lawson BG (eds) Proceedings of the 2006 winter simulation conference, pp 1825–1835.
Bitran GR, Tirupati D (1989) Tradeoff curves, targeting and balancing in manufacturing queueing networks. Oper Res 37(4):547–564.
Brown S, Chance F, Fowler JW et al. (1997) A centralized approach to factory simulation. Future Fab Int 1(3):83–86.
Chen EJ, Kelton WD (2001) Quantile and histogram estimation. In: Peters BA, Smith JS, Medeiros DJ, Rohrer MW (eds) Proceedings of the 2001 winter simulation conference, pp 451–459.
Cheng RCH, Kleijnen JPC (1999) Improved design of queueing simulation experiments with highly heteroscedastic responses. Oper Res 47:762–777.
Chryssolouris G, Lee M, Pierce J et al. (1990) Use of neural networks for the design of manufacturing systems. Manufact Rev 3:187–194.
Chung S-H, Lai C-M (2006) Job releasing and throughput planning for wafer fabrication under demand fluctuating make-to-stock environment. Int J Adv Manufact Technol 31:316–327.
Cochran JK, Chen H-N (2002) Generating daily production plans for complex manufacturing facilities using multi-objective genetic algorithms. Int J Prod Res 40:4147–4167.
Connors DP, Feigin GE, Yao DD (1996) Queueing network model for semiconductor manufacturing. IEEE Trans Semicond Manufact 9:412–427.
Conway RW, Johnson BM, Maxwell WL (1959) Some problems of digital systems simulation. ManageSci 6:92–110.
Cornish EA, Fisher RA (1937) Moments and cumulants in the specification of distributions. Revue de l’Institut International de Statistique 5:307–320.
Delp D, Si J, Fowler J (2006) The development of the complete x-factor contribution measurement for improving cycle time and cycle time variability. IEEE Trans Semicond Manufact 19:352–362.
Delp D, Si J, Hwang Y, Pei B et al. (2005) Availability adjusted x-factor. Int J Prod Res 43: 3933–3953.
Ehteshami B, Pétrakian RG, Shabe PM (1992) Trade-offs in cycle time management: hot lots. IEEE Trans Semicond Manufact 3: 101–106.
Fowler J, Rose O (2004) Grand challenges in modeling and simulation of complex manufacturing systems. Simul Trans Soc Comput Simulat Int 80(9):469–476.
Fowler JW, Leach SE, Mackluak GT, Nelson BL (2008) Variance-based sampling for simulating cycle time-throughput curves using simulation-based estimates. J Simul 2:69–80.
Fowler JW, Mackulak GT, Ankenman BE et al. (2005) Procedures for efficient cycle time-throughput curve generation. In: Proceedings of the NSF 2005 DMII Grantees Conference, pp 1–8.
Fowler JW, Park S, Mackulak GT, Shunk DL (2001) Efficient cycle time-throughput curve generation using fixed sample size procedure. Int J Prod Res 39(12):2595–2613.
Fu MC, Glover F, April J (2005) Simulation optimization: a review, new developments, and applications. In: Kuhl ME, Steiger NM, Armstrong FB, Joines JA (eds) Proceedings of the 2005 Winter Simulation Conference. IEEE, Piscataway, New Jersey, pp 83–95.
Herrmann JW, Chincholkar MM (2001) Reducing throughput time during product design. J Manufact Syst 20:416–428.
Hopp WJ, Spearman ML (2000) Factory physics, 2nd edn. Irwin McGraw-Hill, Boston.
Hung YC, Michailidis G, Bingham DR (2003) Developing efficient simulation methodology for complex queueing networks. In: Chick S, Sanchez PJ, Ferrin D, Morrice DJ (eds) Proceedings of the 2003 Winter Simulation Conference. IEEE, Piscataway, New Jersey, pp 512–519.
Ingolfsson A, Akhmetshina E, Budge S, Li Y et al. (2007) A survey and experimental comparison of service level approximation methods for non-stationary M/M/s queueing systems. INFORMS J Comput 19(2): 201–214.
International Technology Roadmap for Semiconductors 2006 update. Available via http://www.itrs.net/Links/2006Update/FinalToPost/10_Factory_2006Update_OnlinePDF.pdf. Accessed 1 2007.
Kenney JF, Keeping ES (1954) Mathematics of statistics, part 1. D. Van Nostrand Company, Inc., Princeton, New Jersey.
Kleijnen JPC (1988) Experimental design and regression analysis in simulation: an FMS case study, Eur J Oper Res 33:257–261.
Kleijnen JPC (1993) Simulation and optimization in production planning: a case study, Decis Supp Syst 9:269–280.
Kleijnen JPC, Sanchez SM, Lucas TW et al. (2005) State-of-the-art review: a user’s guide to the brave new world of designing simulation experiments. INFORMS J Comput 17(3):263–289.
Kleijnen JPC, Sargent JPC (2000) A methodology for fitting and validating metamodels in simulation. Eur J Oper Res 120:14–29.
Kleijnen JPC, van Beers WCM (2004) Application-driven sequential designs for simulation experiments: Kriging metamodeling. J Oper Res Soc 55:876–883.
Kleijnen JPC, van Beers WCM (2005) Robustness of Kriging when interpolating in random simulation with heterogeneous variances: some experiments. Eur J Oper Res 165:826–834.
Ko SS, Serfozo R, Sivakumar AI (2004) Reducing cycle times in manufacturing and supply chains by input and service rate smoothing. IIE Trans 36:145–153.
Leach SE, Fowler JW, Mackulak GT et al. (2005) Asymptotic variance-based sampling for simulating cycle time-throughput curves. Working paper ASUIE-ORPS-2005–003, Arizona State University.
Liao DY, Wang CN (2004) Neural-network based delivery time estimates for prioritized 300-mm automatic material handling operations. IEEE Trans Semicond Manufact 17:324–332.
Mackulak GT, Fowler JW, Park S et al. (2005) A three phase simulation methodology for generating accurate and precise cycle time-throughput curves. Int J Simul Process Model 1:36–47.
McNeill J, Mackulak G, Fowler J (2003) Indirect estimation of cycle time percentiles from discrete event simulation models using the Cornish-Fisher expansion. In: Chick S, Sánchez PJ, Ferrin D, Morrice DJ (eds) Proceedings of the 2003 winter simulation conference. IEEE, Piscataway, New Jersey, pp 1377–1382.
McNeill J, Nelson BL, Fowler JW et al. (2005) Cycle-time percentile estimation in systems employing dispatching rules. In: Kuhl ME, Steiger NM, Armstrong FB, Joines JA (eds) Proceedings of the 2005 winter simulation. IEEE, Piscataway, New Jersey, pp 751–755.
Mollaghasemi M, LeCroy K, Georgiopoulos M (1998) Application of neural networks and simulation modeling in manufacturing system design. Interfaces 28: 100–114.
Nelson BL (2004) 50th anniversary article: stochastic simulation research in management science. Manage Sci 50:855–868.
Nemoto K, Akcali E, Uzsoy R (2000) Quantifying the benefits of cycle time reduction in semiconductor wafer fabrication. IEEE Trans Electron Pack Manufact 23:39–47.
Papadopoulos HT, Heavey C (1996) Queueing theory in manufacturing systems analysis and design: a classification of models for production and transfer lines. Eur J Oper Res 92:1–27.
Papadopolous HT, Heavey C Browne J (1993) Queueing theory in manufacturing systems analysis and design, 1st edn. Springer, New York.
Park S, Fowler JW, Mackulak GT et al. (2002) D-optimal sequential experiments for generating a simulation-based cycle time-throughput curve. Oper Res 50(6):981–990.
Pukelsheim F (2006) Optimal design of experiments. SIAM, Philadelphia.
Sabuncuoǧlu I, Touhami S (2002) Simulation metamodeling with neural networks: an experimental investigation. Int J Prod Res 40(11): 2483–2505.
Santner TJ, Williams BJ, Notz WI (2003) The design and analysis of computer experiments. Springer-Verlag, NY.
Schultz C (2004) Spare parts inventory and cycle time reduction. Int J Prod Res 42:759–776.
Shang JS, Tadikamalla PR (1993) Output maximization of a CIM system: simulation and statistical approach. Int J Prod Res 31(1):19–41.
Shantikumar JG, Ding S, Zhang MT (2007) Queueing theory for semiconductor manufacturing systems: a survey and open problems. IEEE Trans Automat Sci Eng 4(4):513–522.
Sivakumar AI, Chong CS (2001) A simulation based analysis of cycle time distribution, and throughput in semiconductor backend manufacturing. Comput Ind 45:59–78.
Spence AM, Welter DJ (1987) Capacity planning of a photolithography work cell in a wafer manufacturing line. In Proceedings of the IEEE international conference on robotics and automation, Raleigh, NC, Piscataway, NJ, pp 702–708.
Sze MT, Fi P, Lee WB (2001) Modeling the component assignment problem in PCB assembly. Assembly Autom 21:55–60.
Uzsoy R, Lee CY, Martin Vega L (1992)A review of production planning and scheduling in the semiconductor industry, part I: system characteristics, performance evaluation, and production planning. IIE Trans 24(4):47–61.
van Beers WCM, Kleijnen JPC (2003) Kriging for interpolation in random simulation. J Oper Res Soc 54:255–262.
van Beers WCM, Kleijnen JPC (2004) Kriging in simulation: a survey. In: Ingalls RG, Rossetti MD, Smith JS, Peters BA (eds) Proceedings of the 2004 winter simulation conference. IEEE, Piscataway, New Jersey, pp 113–121.
Vellido A, Lisboa PJG, Vaughan J (1999) Neural networks in business: a survey of applications (1992–1998). Expert Syst Appl 17:51–70.
Yang F, Ankenman BE, Nelson BL (2007) Efficient generation of cycle time-throughput curves through simulation and metamodeling. Naval Res Logist 54:78–93.
Yücesan E, Fowler JW (2000) Simulation analysis of manufacturing and logistics systems. Kluwer Encyclopedia Prod Manufact Manage 687–697, ISBN: 978-0-7923-8630-8.
Acknowledgment
This work was partially supported by Semiconductor Research Corporation Grant Numbers 2004-OJ-1224 and 2004-OJ-1225 and by grants DMII 0140441 and DMII 0140385 from the National Science Foundation.
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Ankenman, B.E., Bekki, J.M., Fowler, J., Mackulak, G.T., Nelson, B.L., Yang, F. (2011). Simulation in Production Planning: An Overview with Emphasis on Recent Developments in Cycle Time Estimation. In: Kempf, K., Keskinocak, P., Uzsoy, R. (eds) Planning Production and Inventories in the Extended Enterprise. International Series in Operations Research & Management Science, vol 151. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6485-4_19
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