Sequential bifurcation for observations with random errors

Simulation;Bifurcation;analyse

A formal description of discrete event dynamic systems including perturbation analysis

Simulation;operations research

Factor screening by sequential bifurcation

Simulation;Bifurcation;analyse

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

Full Screening Another View at One-Factor-at-a-Time Designs

Textbooks on Design Of Experiments invariably start by explaining why one-factor-at-a -time (OAT) is an inferior method. Here we will show that in a model with all interactions a variant of OAT is extremely efficient, provided that we only have non-negative parameters and that there are only a few large parameters. In the extreme, this means that there is one positive parameter. In that case, for m variables, that is, for 2m-1 parameters, the procedure only needs m+2 observations to find. In other examined cases, the proposed method is also very fast.

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