Robust Design: Seeking the Best of All Possible Worlds

Abstract

We describe a framework for analyzing simulation output in order to find solutions that will work well after implementation. We show how the use of a loss function that incorporates both system mean and system variability can be used to efficiently and effectively carry out system optimization and improvement efforts. For models whose behavior depends on quantitative factors, we illustrate how robust design can be accomplished by using simple experimental designs in conjunction with response-surface metamodels. The results can yield new insights into system behavior, and may lead to recommended system configurations that differ substantially from those selected by analysis solely on the basis of mean response. We assume a knowledge base at the level of Chapter 12 of Simulation Modeling and Analysis (Law and Kelton 2000) but will review essential elements and distribute illustrative examples at the session or discussion at the conference.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2000
Accession Number
ADA491968

Entities

People

  • Susan M. Sanchez

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Applied Mathematics
  • Complex Systems
  • Data Science
  • Engineering
  • Engineers
  • Experimental Design
  • Frequency Domain
  • Industrial Plants
  • Information Science
  • Manufacturing
  • Models
  • New York
  • Operations Research
  • Optimization
  • Prototypes
  • Statistics
  • Systems Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference