This paper presents a tool for warehouse processes optimization. It allows checking of the effectiveness of any warehouse layout with a free designed storage location assignment. Warehouse real-time simulator tests the quality of projected layout and item's location assignment using optimal...

This article uses a sequentialized experimental design to select simulation input combinations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the input/output data of the simulation model (computer code)....

This paper studies simulation-based optimization with multiple outputs. It assumes that the simulation model has one random objective function and must satisfy given constraints on the other random outputs. It presents a statistical procedure for testing whether a specific input combination...

This paper derives a novel procedure for testing the Karush-Kuhn-Tucker (KKT) first-order optimality conditions in models with multiple random responses.Such models arise in simulation-based optimization with multivariate outputs. This paper focuses on expensive simulations, which have small...

This article uses a sequentialized experimental design to select simulation input com- binations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the input/output data of the simulation model (computer...

Visualization is not only an important part of experimental data analysis process but also an efficient tool for transfer of acquired knowledge in multiple areas including, but not restricted to, ICT, general scientific discussion, innovation, data mining, decision support systems as well as...

Analytical descriptions of tournament selection efficiency properties are elusive for realistic tournament structures. For example, with more than four competitors, there are very few robust analytical tournament selection efficiency measures. Combining a Monte Carlo simulation with a...

In this article we suggest a new method for solutions of stochastic integrals where the dynamics of the variables in integrand are given by some stochastic differential equation. We also propose numerical simulation of stochastic differential equations which is based on iterated integrals method...

This chapter surveys two methods for the optimization of real-world systems that are modelled through simulation. These methods use either linear regression metamodels, or Kriging (Gaussian processes). The metamodel type guides the design of the experiment; this design fixes the input...

This tutorial reviews the design and analysis of simulation experiments. These experiments may have various goals: validation, prediction, sensitivity analysis, optimization (possibly robust), and risk or uncertainty analysis. These goals may be realized through metamodels. Two types of...