Identifying the important factors in simulation models with many factors

Simulation models may have many parameters and input variables (together called factors), while only a few factors are really important (parsimony principle). For such models this paper presents an effective and efficient screening technique to identify and estimate those important factors. The technique extends the classical binary search technique to situations with more than a single important factor. The technique uses a low-order polynomial approximation to the input/output behavior of the simulation model. This approximation may account for interactions among factors. The technique is demonstrated by applying it to a complicated ecological simulation that models the increase of temperatures worldwide.Simulation Models;econometrics

Finding the Important Factors in Large Discrete-Event Simulation: Sequential Bifurcation and its Applications

This contribution discusses experiments with many factors: the case study includes a simulation model with 92 factors.The experiments are guided by sequential bifurcation.This method is most efficient and effective if the true input/output behavior of the simulation model can be approximated through a first-order polynomial possibly augmented with two-factor interactions.The method is explained and illustrated through three related discrete-event simulation models.These models represent three supply chain configurations, studied for an Ericsson factory in Sweden.After simulating 21 scenarios (factor combinations) each replicated five times to account for noise a shortlist with the 11 most important factors is identified for the biggest of the three simulation models.simulation;bifurcation;supply;Sweden

Statistical Testing of Optimality Conditions in Multiresponse Simulation-based Optimization (Revision of 2005-81)

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 test- ing whether a specific input combination (proposed by some optimization heuristic) satisfies the Karush-Kuhn-Tucker (KKT) first-order optimality conditions. The pa- per focuses on "expensive" simulations, which have small sample sizes. The paper applies the classic t test to check whether the specific input combination is feasi- ble, and whether any constraints are binding; it applies bootstrapping (resampling) to test the estimated gradients in the KKT conditions. The new methodology is applied to three examples, which gives encouraging empirical results.Stopping rule;metaheuristics;response surface methodology;design of experiments

Measurement scales and resolution IV designs: A note (Version 3)

Experimental Design;mathematische statistiek

Statistical Testing of Optimality Conditions in Multiresponse Simulation-Based Optimization (Replaced by Discussion Paper 2007-45)

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 sample sizes.The paper estimates the gradients (in the KKT conditions) through low-order polynomials, fitted locally.These polynomials are estimated using Ordinary Least Squares (OLS), which also enables estimation of the variability of the estimated gradients.Using these OLS results, the paper applies the bootstrap (resampling) method to test the KKT conditions.Furthermore, it applies the classic Student t test to check whether the simulation outputs are feasible, and whether any constraints are binding.The paper applies the new procedure to both a synthetic example and an inventory simulation; the empirical results are encouraging.stopping rule;metaheuristics;RSM;design of experiments