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The enormous potential of digital computation to manage new complex systems is impeded by exponential increases in complexity.

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The model's dimensionality increases from hundreds to thousands of variables, therefore, it is necessary have sub-models constructed by diverse technical teams to be integrated into the total computer simulation model. This site presents access to the recent advances in computer simulation for decision making that is of interest to researchers and graduate students across a of academic domains.

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Enter a word or phrase in the dialogue box, e. Frolova, and V. PowerSim Co. Abramson D. Abspoel S, L. Etman, J. Vervoort, R. Schoofs, and J. Rooda, Simulation based optimization of stochastic systems with integer de variables by sequential multipoint linear approximation, Structural and Multidisciplinary Optimization22, Agnetis A. Operational Research97, Ahmed S. Ahmed M. Alkhamis, and M. Hasan, Optimizing discrete stochastic systems using simulated annealing and simulation, Computers and Industrial Engineering32, Alkhamis, D.

Ahn J-H. Kim, Action-timing problem with sequential Bayesian belief revision process, Eur. Operational Research, Single Orsa gentleman seeks a fwb K. Akmedjanov F. Chelyshev, Robust stability investigation using frequency domain technique, Reliable Computing2 supplement, Alberto, I. Mallor, and P. Mateo, Optimization with simulation and multiobjective analysis in industrial decision-making: A case study, Journal of Operational Research, Aleksandrov V.

Sysoyeve, and V. Shemeneva, Stochastic optimization, Eng. Alessandri A. Parisini, Nonlinear modelling of complex large-scale plants using neural networks and stochastic approximation, IEEE Transactions on Systems, Man, and Cybernetics: A27, Alexopoulos Ch. Seila, A conservative method for selecting the best simulated system, Operations Research Letters19, Alkhamis T. Al-Mharmah H. Calvin, Optimal random non-adaptive algorithm for optimization of Brownian motion, Journal of Global Optimization8, Al-Qaq W. Devetsikiotis, and J. Townsen, Stochastic gradient optimization of importance sampling for the efficient simulation of digital communication systems, IEEE Transactions on Communications43, Alrefaei M.

Andradottir, A modification of the stochastic ruler method for discrete stochastic optimization, European Journal of Operational Research, Al-Sultan K. Heyman, and T. Ott, On the choice of alternative measures in importance sampling with Markov chains, Operations Research33, Andramonov M. Rubinov, and B. Glover, Cutting angle methods in global optimization, Applied Mathematics Letters12, Andres T.

Apeland S. Aven, Risk based maintainance optimaization: Foundational issues, Reliability Engineering and System Safety67, Araki Y. Inoue, Comparison of the extremal search method by human being and machine, System and Control20, Archetti F. Gaivoronski, and A. Sciomachen A. Arinze B. Burton, A simulation model for industrial marketing, Omega20 3, Armstrong J. Black, D. Laxton, and D. Rose, A robust method for simulating forward-looking models, Journal of Economic Dynamics and Control22, Arsham H.

Rubinstein, The efficiency and heavy traffic properties of the score function method in sensitivity analysis of queueing models, Advances in Applied Probability24, Asmussen S. Rubinstein, Response surface estimation and sensitivity analysis via the efficient change of measure, Comm. Models9, Wang, Regenerative rare events simulation via likelihood ratios, Journal of Applied Probability31, Atienza O.

Hong, Computer simulation: An effective tool for teaching statistical optimization procedures, Quality Engineering10 3, Aytug H. Dogan, and G. Bezmez, Determining the of kanbans: A simulation metamodelling approach, Simulation67, Bhattacharyya, and G. Koehler, Genetic learning through simulation: An investigation in shop floor scheduling, Annals of Operations Research78, Azadivar F.

Talavage, Optimization of stochastic simulation models, Mathematics and Computers in Simulation22, Tompkins, Simulation optimization with qualitative variables and structural model changes: A genetic algorithm approach, European Journal Of Operational Research, Schwefel, An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation1, ,

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