selecting an appropriate manufacturing machine is a very important and complex problem for firms, which usually have to deal with both qualitative and quantitative criteria. taking into account the differences among machines in terms of cost, speed, quality and after sale services, type and number of machines are important parameters to consider. therefore, the goal of this paper is to propose ...

An improvement or redesign of a process often starts by modifying the model supporting the process. Analysis techniques, like simulation, can be used to evaluate alternatives. However, even a small number of design choices may lead to an explosion of models that need to be explored to find the optimal models for said process. If the exploration depends on simulation, it often becomes infeasible...

in this study, a prediction model based on support vector machines (svm) improved by introducing a volume weighted penalty function to the model was introduced to increase the accuracy of forecasting short term trends on the stock market to develop the optimal trading strategy. along with vw-svm classifier, a hybrid feature selection method was used that consisted of f-score as the filter part ...

abstract: despite the importance of predictive variable in prediction, in most of the research in the field of auditors’ opinion the purpose was rendering the suitable models. meanwhile, less attention was paid to the selection of optimal predictive variable and appropriate models of these selection. therefore, in most of these research the predictive variables were chosen randomly and accordin...

The behavior of selection methods used in evolutionary algorithms that operate in uncertain environments is investigated in the framework of parametric two{armed bandit problems. Asymptotically optimal selection strategies are based on the sequential probability ratio test which i s p r o ved to perform up to four times better than analogous strategies based on the optimal xed size sample test....

A decision-theoretic framework is described for sequential classification when the parameter space is a finite partially ordered set. An example of an optimal strategy is then presented. This example establishes that an asymptotically optimal class of experiment selection rules is not necessarily optimal in the given decision-theoretic setting.

Optimal control is an important field of study both in theory and in applications. Based on uncertainty theory, an expected value model of uncertain optimal control problem was studied by Zhu. In this paper, an optimistic value model for uncertain optimal control problem is investigated. Applying Bellman’s principle of optimality, the principle of optimality for the model is presented. And then...

A general theorem on asymptotically optimal sequential selection of experiments is presented and applied to a Bayesian classification problem when the parameter space is a finite partially ordered set. The main results include establishing conditions under which the posterior probability of the true state converges to one almost surely, and determining optimal rates of convergence. Properties o...

This study was born in the context of new challenges imposed by the recast of the EU Energy Performance of Buildings Directive. The aim of this work is to develop strategies to identify and investigate the relationship between decisional variables within a nZEB design concept, providing a useful method to deal with a huge number of simulations corresponding to a large number of building configu...

We consider a dynamical system with finitely many equilibria and perturbed by small noise, in addition to being controlled by an ‘expensive’ control. We study the invariant distribution of the controlled process as the variance of the noise becomes vanishingly small. It is shown that depending on the relative magnitudes of the noise variance and the ‘running cost’ for control, one can identify ...