Abstract
The purpose of making efficient and flexible manufacturing systems is often related to the possibility to analyze the system considering at the same time a wide number of parameters and their interactions. Simulation models are proved to be useful to support and drive company management in improving the performances of production and logistic systems. However, to achieve the expected results, a detailed model of the production and logistic system is needed as well as a structured error analysis to guarantee results reliability. The aim of this paper is to give some practical guide lines in order to drive the error analysis for discrete event stochastic simulation model that are widely used to study production and logistic system.
Chapter PDF
Similar content being viewed by others
References
Bather, J.A.: A continuous time inventory model. J. Appl. Prob. 3, 538–549 (1966)
Box, G.E.P., Hunter, W.E., Hunter, J.S.: Statistics for experimenters. John Wiley & Sons (1978)
Brown, R.G.: Smoothing, Forecasting and Prediction of Discrete Time Series. Prentice-Hall, Englewood Cliffs (1963)
Chung, A.C.: Simulation Modeling Handbook – A Pratical Approach. Industrial and Manufacturing Engineering Series, Series Editor. CRC Press (2004)
Fishman, G.S.: Discrete Event Simulation: Modeling, Programming, and Analysis. Springer (2001)
Ghiani, G., Laporte, G., Musmanno, R.: Introduction to logistics systems planning and control. John Wiley & Sons Ltd., West Sussex (2004)
Harris, F.W.: Howmany parts to make at once. Factory, The Magazine of Management 10(2), 135–136 (1913); 152, reprinted in Operations Research 38(6) (November-December 1990)
Kelton, W.D.: Random initialization methods in simulation. IIE Transactions 21(4), 355–367 (1989)
Kelton, W.D., Law, A.M.: A new approach for dealing with the startup problem in discrete event simulation. Naval Research Logistics Quarterly 30, 641–658 (1983)
Law, A.M., Kelton, W.D.: Simulation Modeling and Analysis, 3rd edn. McGraw-Hill (2000)
Mosca, R., Giribone, P.: Teoria degli esperimenti e simulazione. Quaderni di gestione degli impianti industriali, Università di Genova (1985)
Mosca, R., Giribone, P., Schenone, M.: Integrated management of a bishuttle FMS using discrete/stochastic simulator. Computer-Integrated Manufacturing Systems 5(2) (1992)
Sandıkc, B., Sabuncuoglu, I.: Analysis of the behavior of the transient period in non-terminating simulations. European Journal of Operational Research 173, 252–267 (2006)
Schruben, L.W.: Detecting initialization bias in simulation output. Operations Research 30, 569–590 (1982)
Schruben, L.W., Singh, H., Tierney, L.: Optimal test for initialization bias in simulation output. Operations Research 31, 1167–1178 (1983)
Tersine, R.J.: Principles of inventory and materials management. Elsevier Science Publishing Co.,Inc., North-Holland (1988)
Vassilacopoulus, G.: Testing for initialization bias in simulation output. Simulation 52, 151–153 (1989)
Welch, P.D. A graphical approach to the initial transient problem in steady-state simulations. In: Proceedings of the 10th IMACS World Congress on Systems, Simulation, and Scientific Computation, Montreal, pp. 219–221 (1982)
White Jr., K.P.: An effective truncation heuristic for bias reduction in simulation output. Simulation 69(6), 323–334 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 IFIP International Federation for Information Processing
About this paper
Cite this paper
Davoli, G., Nielsen, P., Pattarozzi, G., Melloni, R. (2013). Practical Considerations about Error Analysis for Discrete Event Simulations Model. In: Emmanouilidis, C., Taisch, M., Kiritsis, D. (eds) Advances in Production Management Systems. Competitive Manufacturing for Innovative Products and Services. APMS 2012. IFIP Advances in Information and Communication Technology, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40361-3_90
Download citation
DOI: https://doi.org/10.1007/978-3-642-40361-3_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40360-6
Online ISBN: 978-3-642-40361-3
eBook Packages: Computer ScienceComputer Science (R0)