Multi-Objective Single Product Robust Optimization: An Integrated Design and Marketing Approach

[+] Author and Article Information
B. Besharati

Department of Mechanical Engineering, A. J. Clark School of Engineering, University of Maryland, College Park, MD 20742

L. Luo

Department of Marketing, The Marshall School of Business, University of Southern California, Los Angeles, CA 90089

S. Azarm1

Department of Mechanical Engineering, A. J. Clark School of Engineering, University of Maryland, College Park, MD 20742azarm@umd.edu

P. K. Kannan

Department of Marketing, Robert H. Smith School of Business, University of Maryland, College Park, MD 20742

In a perfect yet unachievable sense, the variation in performance is desired to be zero.

We also considered alternative models with interaction effects between attributes. We did not find any additional improvement in our model fits. Therefore, only main effects are included in our model.

We use 95% confidence level because this is the most commonly used criterion in statistics literature (34). This percentage can be adjusted based on the product manager’s preference.


Corresponding author.

J. Mech. Des 128(4), 884-892 (Dec 19, 2005) (9 pages) doi:10.1115/1.2202889 History: Received August 08, 2005; Revised December 19, 2005

We present an integrated design and marketing approach to facilitate the generation of an optimal robust set of product design alternatives to carry forward to the prototyping stage. The approach considers variability in both (i) engineering design domain, and (ii) customer preferences in marketing domain. In the design domain, the approach evaluates performance and robustness of a design alternative due to variations in its uncontrollable parameters. In the marketing domain, in addition to considering competitive product offerings, the approach considers designs that are robust in customer preferences with respect to: (1) the variations in the design domain, and (2) the inherent variations in the estimates of preferences given the fit of the preference model to the sampled data. Our overall goal is to obtain design alternatives that are multi-objectively robust and optimal, i.e., (1) are optimal for nominal values of parameters, and (2) are within a known acceptable range in their multi-objective performance, and (3) maintain feasibility even when they are subject to applications and environments that are different from nominal or standard laboratory conditions. We illustrate the highlights of our approach with the design of a corded power tool example.

Copyright © 2006 by American Society of Mechanical Engineers
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Figure 1

Overall approach

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Figure 2

Multi-objective robustness

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Figure 3

Robust optimization approach

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Figure 4

Integrated design-marketing approach

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Figure 5

Rank ordering of design alternatives in (a) design domain, and (b) marketing domain

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Figure 6

Set of nominal and robust Pareto design alternatives

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Figure 7

Final set of robust design and product alternatives: (a) engineering design domain, and (b) marketing domain



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