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Analyzing Effectiveness of Warm-up Detection Methods

Yıl 2017, Cilt: 32 Sayı: 4, 201 - 210, 15.12.2017
https://doi.org/10.21605/cukurovaummfd.383428

Öz

In discrete event system simulation, it is necessary
to purify the system from effects of its initial conditions for unbiased
estimation of performance outputs. Particularly in non-terminating models, the
period during which the system reaches the steady state, that is, the warm-up
period statistics, should not be included in the calculations to remove the
biased effects on the performance output values. In problems which simulation
and optimization methods are used together, the update of simulation parameters
before each run results in variation of warm-up periods.  In order to decrease this variation, the
online warm-up determination methods are used. In this study, Welch’s Graphical
method and two online warm-up detection methods: Exponential Variation Rate
Rule and Euclidean Distance methods are applied for M/M/1 queueing systems
simulation. The effectiveness of these methods based on convergence to the
analytic solutions and CPU times are compared.

Kaynakça

  • 1. Hoad, K., Robinson, S., Davies, R., 2008. Automating Warm-Up Length Estimation. Proceedings of Winter Simulation Conference, 532-540.
  • 2. Oh, H., Park, K., 2006. An Effective Heuristic To Detect Warm-Up Period in Simulation Output. Proceedings of The Winter Simulation Conference, 182-188.
  • 3. Lee, Y.H., Kyung, K-H., Jung, C-S., 1997. On-Line Determination of Steady State in Simulation Outputs, Computers& Industrial Engineering, 33, 805-808.
  • 4. White, K.P., Jr., Cobb, M.J., Spratt, S., 2000. Comparison of Five Steady-State Truncation Heuristics for Simulation. Proceedings of the Winter Simulation Conference, 755-760.
  • 5. Robinson, S., 2007. A Statistical Process Control Approach to Selecting A Warm-Up Period for A Discrete-Event Simulation. European Journal of Operational Research, 176, 332–346.
  • 6. Gordon, G., 1969. System Simulation. New Jersey: Prentice- Hall.
  • 7. Banks, J., Carson, J.S., Nelson, B.L., Nicol D.M., 2001. Discrete-Event System Simulation, 3rd Ed. Prentice Hall, Upper Saddle River, NJ.
  • 8. Wilson, J.R., Pritsker, A.A.B., 1978a. A Survey of Research on the Simulation Startup Problem, 31:55-58.
  • 9. Gafarian, A.V., Ancker, C.J., Morisaku, T., 1978. Evaluation of Commonly used Rules for Detecting ‘Steady State’ in Computer Simulation. Naval Research Logistics Quarterly, 25: 511-529.
  • 10. Nelson, B.L., 1992. Initial-Condition Bias. In Handbook of Industrial Engineering, 2nd Ed., Ed., G. Salvendy. Newyork: John Wiley.
  • 11. Roth, E., Josephy, N., 1993. A Relaxation Time Heuristic for Exponential-Erlang Queueing Systems. Computers & Operations Research 20(3): 293-301.
  • 12. Roth, E., 1994. The Relaxation Time Heuristic for the Initial Transient Problem in M/M/K Queueing Systems. European Journal of Operational Research. 72: 376-386.
  • 13. Fishman, G.S., 2001. Discrete-Event Simulation, Modeling, Programming, and Analysis. New York: Springer- Verlag.
  • 14. Bause, F., Eickhoff, M., 2003. Truncation Point Estimation using Multiple Replications in Parallel. In Proceedings of the 2003 Winter Simulation Conference, 414-421.
  • 15. Sandıkçı, B., Sabuncuoğlu, İ., 2006. Analysis of the Behavior of the Transient Period in Non-Terminating Simulations. European Journal of Operational Research, 173;252–267.
  • 16. Law, A.M., 1983. Statistical Analysis of Simulation Output Data. Operations Research 31: 983-1029.
  • 17. Pawlikowski, K., 1990, Steady-State Simulation of Queueing Processes: A Survey of Problems and Solutions. Computing Surveys, 22: 123-170.
  • 18. Alexopoulos, C., Seilai, A.F., 1998. Output Data Analysis, Handbook of Simulation, 225-272. New York: Wiley.
  • 19. Law, A.M., Kelton, W.D., 2000. Simulation Modeling and Analysis, 3rd Ed. New York: Mcgraw-Hill.
  • 20. Linton, J.R., Harmonosky, C.M., 2002. A Comparison of Selective Initialization Bias Elimination Methods. In Proceedings of the Winter Simulation Conference, 1951-1957.
  • 21. Mahajan P.S., Ingalls R.G., 2004, Evaluation of Methods used to Detect Warm-Up Period In Steady State Simulation. Proceedings of Winter Simulation Conference.
  • 22. Conway, R.W., 1963. Some Tactical Problems in Digital Simulation. Management Science 10(1): 47-61.
  • 23. Fishman, G.S., 1973. Concepts and Methods in Discrete Event Digital Simulation. New York: Wiley.
  • 24. Wilson, J.R., Pritsker, A.A.B., 1978b. Evaluation of Startup Policies in Simulation Experiments. Simulation 31(3): 79-89.
  • 25. Bratley, P., B. Fox, Schrage, L., 1987. A Guide to Simulation, 2nd Ed. New York: Springer-Verlag.
  • 26. Yücesan, E., 1993, Randomization Tests for Initialization Bias in Simulation Output. Naval Research Logistics, 40: 643-663.
  • 27. White, K.P., Jr. 1997. An Effective Truncation Heuristic for Bias Reduction in Simulation Output. Simulation, 69(6): 323-334.
  • 28. Fishman, G.S., 1971. Estimating Sample Size in Computing Simulation Experiments Management Science 18: 21-38.
  • 29. Spratt, S.C., 1998. Heuristics for the Startup Problem. M.S.Thesis, Department of Systems Engineering, University of Virginia.
  • 30. Cash, C.R., Dippold, D.G., Long, J.M., Nelson, B.L., Pollard, W.P., 1992. Evaluation of Tests for Initial Conditions Bias. In Proceedings of the 1992 Winter Simulation Conference,
  • 31. Goldsman, D., Schruben, L.W. Swain, J.J., 1994. Tests for Transient Means in Simulated Time Series. Naval Research Logistics, 41:171-187.
  • 32. Schruben, L.W., 1982. Detecting Initialization Bias in Simulation Output. Operations Research, 30(3):151-153.
  • 33. Kimbler, D.L., Knight, B.D., 1987. A Survey of Current Methods for the Elimination of Initialisation Bias in Digital Simulation. Annual Simulation Symposium 20: 133-142.
  • 34. Kelton, W.D., Law, A.M., 1983. A New Approach for Dealing with The Startup Problem in Discrete Event Simulation. Naval Research Logistics Quarterly. 30:641-658.
  • 35. Gallagher, M.A., Bauer Jnr, K.W., Maybeck, P. S., 1996. Initial Data Truncation for Univariate Output of Discrete-Event Simulations using the Kalman Filter. Management Science 42(4): 559-575.
  • 36. Jackway, P.T., Desilva, B.M., 1992, A Methodology for Initialisation Bias Reduction in Computer Simulation Output. Asia-Pacific Journal of Operational Research, 9: 87-100.
  • 37. Lee, Y.H., Kim, Y.B., Park, K.J., 1997. Single Run Optimization using Reverse Simulation Method. Proceedings of the WSC, 183-193.

Isınma Periyodu Belirleme Yöntemlerinin Etkinliklerinin Analizi

Yıl 2017, Cilt: 32 Sayı: 4, 201 - 210, 15.12.2017
https://doi.org/10.21605/cukurovaummfd.383428

Öz

Kesikli
olay simülasyonunda, performans çıktı değerlerinin yansız tahmini için sistemi
başlangıç durumu etkilerinden arındırmak gerekmektedir. Özellikle sonlanmayan
modellerde, sistemin durağan duruma ulaşana kadar geçirdiği süre yani ısınma
periyodu istatistikleri, performans çıktı değerlerinin üzerindeki yanlı etkilerinin
ortadan kaldırılması için hesaplamalara dâhil edilmemelidir. Simülasyon ve
optimizasyon yöntemlerinin birlikte kullanıldığı problemlerde ise simülasyon
parametrelerinin her koşum öncesi güncellenmesi ısınma periyodunda da
değişimlere sebep olmaktadır. Bu değişimi azaltmak için, anlık ısınma periyodu
belirleme yöntemleri kullanılmaktadır. Bu çalışmada, Welch Grafik yöntemi ve
anlık ısınma periyodu belirleme yöntemlerinden Üstel Değişim Oranı Kuralı ve
Öklid Uzaklığı yöntemleri M/M/1 kuyruk modeli için uygulanmıştır. Yöntemler
etkinlikleri analitik sonuçlara yakınsama başarımları ve CPU zamanları bazında
etkinlikleri karşılaştırılmıştır.

Kaynakça

  • 1. Hoad, K., Robinson, S., Davies, R., 2008. Automating Warm-Up Length Estimation. Proceedings of Winter Simulation Conference, 532-540.
  • 2. Oh, H., Park, K., 2006. An Effective Heuristic To Detect Warm-Up Period in Simulation Output. Proceedings of The Winter Simulation Conference, 182-188.
  • 3. Lee, Y.H., Kyung, K-H., Jung, C-S., 1997. On-Line Determination of Steady State in Simulation Outputs, Computers& Industrial Engineering, 33, 805-808.
  • 4. White, K.P., Jr., Cobb, M.J., Spratt, S., 2000. Comparison of Five Steady-State Truncation Heuristics for Simulation. Proceedings of the Winter Simulation Conference, 755-760.
  • 5. Robinson, S., 2007. A Statistical Process Control Approach to Selecting A Warm-Up Period for A Discrete-Event Simulation. European Journal of Operational Research, 176, 332–346.
  • 6. Gordon, G., 1969. System Simulation. New Jersey: Prentice- Hall.
  • 7. Banks, J., Carson, J.S., Nelson, B.L., Nicol D.M., 2001. Discrete-Event System Simulation, 3rd Ed. Prentice Hall, Upper Saddle River, NJ.
  • 8. Wilson, J.R., Pritsker, A.A.B., 1978a. A Survey of Research on the Simulation Startup Problem, 31:55-58.
  • 9. Gafarian, A.V., Ancker, C.J., Morisaku, T., 1978. Evaluation of Commonly used Rules for Detecting ‘Steady State’ in Computer Simulation. Naval Research Logistics Quarterly, 25: 511-529.
  • 10. Nelson, B.L., 1992. Initial-Condition Bias. In Handbook of Industrial Engineering, 2nd Ed., Ed., G. Salvendy. Newyork: John Wiley.
  • 11. Roth, E., Josephy, N., 1993. A Relaxation Time Heuristic for Exponential-Erlang Queueing Systems. Computers & Operations Research 20(3): 293-301.
  • 12. Roth, E., 1994. The Relaxation Time Heuristic for the Initial Transient Problem in M/M/K Queueing Systems. European Journal of Operational Research. 72: 376-386.
  • 13. Fishman, G.S., 2001. Discrete-Event Simulation, Modeling, Programming, and Analysis. New York: Springer- Verlag.
  • 14. Bause, F., Eickhoff, M., 2003. Truncation Point Estimation using Multiple Replications in Parallel. In Proceedings of the 2003 Winter Simulation Conference, 414-421.
  • 15. Sandıkçı, B., Sabuncuoğlu, İ., 2006. Analysis of the Behavior of the Transient Period in Non-Terminating Simulations. European Journal of Operational Research, 173;252–267.
  • 16. Law, A.M., 1983. Statistical Analysis of Simulation Output Data. Operations Research 31: 983-1029.
  • 17. Pawlikowski, K., 1990, Steady-State Simulation of Queueing Processes: A Survey of Problems and Solutions. Computing Surveys, 22: 123-170.
  • 18. Alexopoulos, C., Seilai, A.F., 1998. Output Data Analysis, Handbook of Simulation, 225-272. New York: Wiley.
  • 19. Law, A.M., Kelton, W.D., 2000. Simulation Modeling and Analysis, 3rd Ed. New York: Mcgraw-Hill.
  • 20. Linton, J.R., Harmonosky, C.M., 2002. A Comparison of Selective Initialization Bias Elimination Methods. In Proceedings of the Winter Simulation Conference, 1951-1957.
  • 21. Mahajan P.S., Ingalls R.G., 2004, Evaluation of Methods used to Detect Warm-Up Period In Steady State Simulation. Proceedings of Winter Simulation Conference.
  • 22. Conway, R.W., 1963. Some Tactical Problems in Digital Simulation. Management Science 10(1): 47-61.
  • 23. Fishman, G.S., 1973. Concepts and Methods in Discrete Event Digital Simulation. New York: Wiley.
  • 24. Wilson, J.R., Pritsker, A.A.B., 1978b. Evaluation of Startup Policies in Simulation Experiments. Simulation 31(3): 79-89.
  • 25. Bratley, P., B. Fox, Schrage, L., 1987. A Guide to Simulation, 2nd Ed. New York: Springer-Verlag.
  • 26. Yücesan, E., 1993, Randomization Tests for Initialization Bias in Simulation Output. Naval Research Logistics, 40: 643-663.
  • 27. White, K.P., Jr. 1997. An Effective Truncation Heuristic for Bias Reduction in Simulation Output. Simulation, 69(6): 323-334.
  • 28. Fishman, G.S., 1971. Estimating Sample Size in Computing Simulation Experiments Management Science 18: 21-38.
  • 29. Spratt, S.C., 1998. Heuristics for the Startup Problem. M.S.Thesis, Department of Systems Engineering, University of Virginia.
  • 30. Cash, C.R., Dippold, D.G., Long, J.M., Nelson, B.L., Pollard, W.P., 1992. Evaluation of Tests for Initial Conditions Bias. In Proceedings of the 1992 Winter Simulation Conference,
  • 31. Goldsman, D., Schruben, L.W. Swain, J.J., 1994. Tests for Transient Means in Simulated Time Series. Naval Research Logistics, 41:171-187.
  • 32. Schruben, L.W., 1982. Detecting Initialization Bias in Simulation Output. Operations Research, 30(3):151-153.
  • 33. Kimbler, D.L., Knight, B.D., 1987. A Survey of Current Methods for the Elimination of Initialisation Bias in Digital Simulation. Annual Simulation Symposium 20: 133-142.
  • 34. Kelton, W.D., Law, A.M., 1983. A New Approach for Dealing with The Startup Problem in Discrete Event Simulation. Naval Research Logistics Quarterly. 30:641-658.
  • 35. Gallagher, M.A., Bauer Jnr, K.W., Maybeck, P. S., 1996. Initial Data Truncation for Univariate Output of Discrete-Event Simulations using the Kalman Filter. Management Science 42(4): 559-575.
  • 36. Jackway, P.T., Desilva, B.M., 1992, A Methodology for Initialisation Bias Reduction in Computer Simulation Output. Asia-Pacific Journal of Operational Research, 9: 87-100.
  • 37. Lee, Y.H., Kim, Y.B., Park, K.J., 1997. Single Run Optimization using Reverse Simulation Method. Proceedings of the WSC, 183-193.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Nuşin Uncu Bu kişi benim

Yayımlanma Tarihi 15 Aralık 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 32 Sayı: 4

Kaynak Göster

APA Uncu, N. (2017). Isınma Periyodu Belirleme Yöntemlerinin Etkinliklerinin Analizi. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 32(4), 201-210. https://doi.org/10.21605/cukurovaummfd.383428
AMA Uncu N. Isınma Periyodu Belirleme Yöntemlerinin Etkinliklerinin Analizi. cukurovaummfd. Aralık 2017;32(4):201-210. doi:10.21605/cukurovaummfd.383428
Chicago Uncu, Nuşin. “Isınma Periyodu Belirleme Yöntemlerinin Etkinliklerinin Analizi”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 32, sy. 4 (Aralık 2017): 201-10. https://doi.org/10.21605/cukurovaummfd.383428.
EndNote Uncu N (01 Aralık 2017) Isınma Periyodu Belirleme Yöntemlerinin Etkinliklerinin Analizi. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 32 4 201–210.
IEEE N. Uncu, “Isınma Periyodu Belirleme Yöntemlerinin Etkinliklerinin Analizi”, cukurovaummfd, c. 32, sy. 4, ss. 201–210, 2017, doi: 10.21605/cukurovaummfd.383428.
ISNAD Uncu, Nuşin. “Isınma Periyodu Belirleme Yöntemlerinin Etkinliklerinin Analizi”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 32/4 (Aralık 2017), 201-210. https://doi.org/10.21605/cukurovaummfd.383428.
JAMA Uncu N. Isınma Periyodu Belirleme Yöntemlerinin Etkinliklerinin Analizi. cukurovaummfd. 2017;32:201–210.
MLA Uncu, Nuşin. “Isınma Periyodu Belirleme Yöntemlerinin Etkinliklerinin Analizi”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, c. 32, sy. 4, 2017, ss. 201-10, doi:10.21605/cukurovaummfd.383428.
Vancouver Uncu N. Isınma Periyodu Belirleme Yöntemlerinin Etkinliklerinin Analizi. cukurovaummfd. 2017;32(4):201-10.