Life-cycle cost estimates for large scale, long term, future military capabilities are difficult to make and subject to complexities. Usually they are generated from anecdotal experience. However, experience may not be a sound basis, so modelling and simulation are employed to define conditions that lead to early system failure in measures such as availability levels or the capability's life-of-type. Such models typically have common characteristics, including decay or degradation, queueing delays, availability of resources, and maintenance processes. Our generic model is a queue server, discrete event simulation that emulates macroscopic maintenance processes using time based parameters and statistical distributions. Previously we reported that the simulated system shows evidence of bifurcation-like behaviour in life-of-type estimates. This suggested that uncertainties in microscopic variables (such as inter-arrival times) cause instabilities in high level strategic performance indicators, making the prediction of such indicators difficult and bringing into question the use of mean based estimation methods for inventory provisioning. Our objective is to define the conditions which lead to system failure. We use a series of numerical simulation experiments to investigate and define such conditions. Outcomes show that system performance is sensitive to the types of input distribution used and that decay processes are strongly associated with complex behaviour even when most of the interacting factors of the real system have been removed from the simulation. References Bender A., Pincombe A. and Sherman G. D., Effects of decay uncertainty in the prediction of life-cycle costing for large scale military capability projects 18th World IMACS / MODSIM Congress, Cairns, Australia 13--17 July 2009, http://mssanz.org.au/modsim09. Feichtinger G., Hommes C. H. and Herold W., Chaos in a Simple Deterministic Queueing System, ZOR - Mathematical Methods of Operations Research, 40, 1994, 109--119. doi:10.1007/BF01414032 Gavrilov L. A., Gavrilova, N. S., The reliability theory of ageing and longevity. Journal of Theoretical Biology, 213(4), 2001, 527--545. doi:10.1006/jtbi.2001.2430 Kleijnen, J. P. C., S. M. Sanchez, T. W. Lucas, and T. M. Cioppa, State-of-the-art review: a userís guide to the brave new world of designing simulation experiments. INFORMS Journal on Computing, 17, no. 3, 2005, 263--289. doi:10.1287/ijoc.1050.0136 Upadhya S. K. and Srinivasan N. K., Availability of Weapon Systems with Logistic Delays: A Simulation Approach, International Journal of Quality and Reliability Management 20:7, 2004, 836--846. doi:10.1108/02656710310491249 Verhulst, P. F., Recherches mathematiques sur la loi d'accroissement de la population., Nouv. mem. de l'Academie Royale des Sci. et Belles-Lettres de Bruxelles 18, 1845, 1--41.
@article{2604,
title = {Determining some of the triggers for early life cycle failure in decay affected logistic queueing simulation},
journal = {ANZIAM Journal},
volume = {51},
year = {2010},
doi = {10.21914/anziamj.v51i0.2604},
language = {EN},
url = {http://dml.mathdoc.fr/item/2604}
}
Sherman, Gregory; Pincombe, Adrian; Bender, Axel. Determining some of the triggers for early life cycle failure in decay affected logistic queueing simulation. ANZIAM Journal, Tome 51 (2010) . doi : 10.21914/anziamj.v51i0.2604. http://gdmltest.u-ga.fr/item/2604/