The term 'simulation optimization' has become widespread in both academical and practical studies. One of the many reasons for this interest in simulation optimization is that for problems that arise in practical applications, explicit mathematical formulations may be too restrictive; that is where simulation is relevant. Therefore, for many practical cases one cannot obtain an analytical solution through methods that require explicit mathematical formulations. Indeed, simulation optimization has led to the numerical solution of large-scale, real-world decision-making problems. Response Surface Methodology (RSM) is a black box simulation optimization method with its origin in the 1950's. Being a black box method, RSM is simple and broadly applicable to both stochastic and deterministic systems. In this study, the aim is to solve methodological problems in RSM such as scale dependence of search direction, selection of step size, dealing with multiple responses, and stopping rule of the whole procedure. The intended audience is comprised of researchers and practitioners in the simulation area.
The term 'simulation optimization' has become widespread in both academical and practical studies. One of the many reasons for this interest in simulation optimization is that for problems that arise in practical applications, explicit mathematical formulations may be too restrictive; that is where simulation is relevant. Therefore, for many practical cases one cannot obtain an analytical solution through methods that require explicit mathematical formulations. Indeed, simulation optimization has led to the numerical solution of large-scale, real-world decision-making problems. Response Surface Methodology (RSM) is a black box simulation optimization method with its origin in the 1950's. Being a black box method, RSM is simple and broadly applicable to both stochastic and deterministic systems. In this study, the aim is to solve methodological problems in RSM such as scale dependence of search direction, selection of step size, dealing with multiple responses, and stopping rule of the whole procedure. The intended audience is comprised of researchers and practitioners in the simulation area.
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This book presents the complex subject of multidisciplinary design optimization in the most comprehensible and easy to understand language. This discipline is a field of engineering that uses...
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are...
This book includes selected peer-reviewed papers presented at the International Conference on Modeling, Simulation and Optimization, organized by National Institute of Technology, Silchar, Assam,...
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