Optimization of simulated systems is the goal of many methods, but most methods assume known environments. In this paper we present a methodology that does account for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by either Response Surface Methodology or Kriging metamodeling. We illustrate the resulting methodology through the well-known Economic Order Quantity (EOQ) model
This chapter surveys two methods for the optimization of real-world systems that are modelled throug...
In the real world of engineering problems, in order to reduce optimization costs in physical process...
Most methods in simulation-optimization assume known environments, whereas this research accounts fo...
Optimization of simulated systems is the goal of many methods, but most methods assume known environ...
Optimization of simulated systems is the goal of many methods, but most methods assume known environ...
Optimization of simulated systems is the goal of many methods, but most methods assume known environ...
This contribution summarizes a methodology for simulation optimization assuming some simulation inpu...
Optimization of simulated systems is tackled by many methods, but most methods assume known environm...
Optimization of simulated systems is the goal of many techniques, but most of them assume known envi...
Optimization of simulated systems is tackled by many methods, but most methods assume known environm...
AbstractMost methods in simulation-optimization assume known environments, whereas this research acc...
Most methods in simulation-optimization assume known environments, whereas this research accounts fo...
This contribution summarizes a methodology for simulation optimization assuming some simulation inp...
Optimization of simulated systems is tackled by many methods, but most methods assume known environm...
Metamodels are often used in simulation-optimization for the design and management of complex system...
This chapter surveys two methods for the optimization of real-world systems that are modelled throug...
In the real world of engineering problems, in order to reduce optimization costs in physical process...
Most methods in simulation-optimization assume known environments, whereas this research accounts fo...
Optimization of simulated systems is the goal of many methods, but most methods assume known environ...
Optimization of simulated systems is the goal of many methods, but most methods assume known environ...
Optimization of simulated systems is the goal of many methods, but most methods assume known environ...
This contribution summarizes a methodology for simulation optimization assuming some simulation inpu...
Optimization of simulated systems is tackled by many methods, but most methods assume known environm...
Optimization of simulated systems is the goal of many techniques, but most of them assume known envi...
Optimization of simulated systems is tackled by many methods, but most methods assume known environm...
AbstractMost methods in simulation-optimization assume known environments, whereas this research acc...
Most methods in simulation-optimization assume known environments, whereas this research accounts fo...
This contribution summarizes a methodology for simulation optimization assuming some simulation inp...
Optimization of simulated systems is tackled by many methods, but most methods assume known environm...
Metamodels are often used in simulation-optimization for the design and management of complex system...
This chapter surveys two methods for the optimization of real-world systems that are modelled throug...
In the real world of engineering problems, in order to reduce optimization costs in physical process...
Most methods in simulation-optimization assume known environments, whereas this research accounts fo...