, innovation, data mining, decision support systems as well as education and learning. Consequently search for new and perfection …

supply side extensive Input-Output Tables can be mentioned and on the demand side the optimization by Multi-Criteria Decision …. The simulation used was limited in its applications. Clearly if this simulation has no practical consequences, it still …

measures. Combining a Monte Carlo simulation with a statistical model of player skill and random variation in scoring, we …

integrand are given by some stochastic differential equation. We also propose numerical simulation of stochastic differential … integrals terms at the initial time of the simulation along with the solution of the stochastic integrals which is found in … terms of Hermite polynomials and variance of the integrals. We apply the method of iterated integrals to simulation of …

adoption of a Gaussian Pseudo Random Number Generator turned out to be useful as it creates crises days while simulation …

This chapter surveys two methods for the optimization of real-world systems that are modelled through simulation. These … experiment; this design fixes the input combinations of the simulation model. These regression models uses a sequence of local …-estimated through sequential designs. "Robust" optimization may use RSM or Kriging, and accounts for uncertainty in simulation inputs …