Isınma Periyodu Belirleme Yöntemlerinin Etkinliklerinin Analizi

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

Analyzing Effectiveness of Warm Up Detection Methods

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

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  • 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. OnLine 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, 3 rd 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 NonTerminating 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: SpringerVerlag. 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.
Çukurova Üniversitesi Mühendislik-Mimarlik Fakültesi Dergisi-Cover
  • ISSN: 1019-1011
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 1986
  • Yayıncı: ÇUKUROVA ÜNİVERSİTESİ MÜHENDİSLİK FAKÜLTESİ