Interactive Product Design Selection With an Implicit Value Function

2002 ◽  
Author(s):  
K. Maddulapalli ◽  
S. Azarm ◽  
A. Boyars
Keyword(s):  
Product Design ◽  
Decision Maker ◽  
Trial Design ◽  
Value Function ◽  
Automated Method ◽  
Design Alternatives ◽  
Interactive Product ◽  
Electric Drill ◽  
Selection For ◽  
Design Selection

We present an automated method to aid a Decision Maker (DM) in selecting the ‘most preferred’ from a set of design alternatives. The method assumes that the DM’s preferences reflect an implicit value function that is quasi-concave. The method is iterative, using three approaches in sequence to eliminate lower-value alternatives at each trial design. The method is interactive, with the DM stating preferences in the form of attribute tradeoffs at each trial design. We present an approach for finding a new trial design at each iteration. We provide an example, the design selection for a cordless electric drill, to demonstrate the method.

Interactive Product Design Selection With an Implicit Value Function

2005 ◽  
Vol 127 (3) ◽  
pp. 367-377 ◽  
Author(s):  
K. Maddulapalli ◽  
S. Azarm ◽  
A. Boyars
Keyword(s):  
Trial Design ◽  
Value Function ◽  
Probability Distributions ◽  
Specific Design ◽  
Design Alternatives ◽  
Interactive Product ◽  
Electric Drill ◽  
Selection For ◽  
Design Selection ◽  
The Value Function

We present a new method to aid a decision maker (DM) in selecting the “most preferred” from a set of design alternatives. The method is deterministic and assumes that the DM’s preferences reflect an implicit value function that is quasi-concave. The method is interactive, with the DM stating preferences in the form of attribute tradeoffs at a series of trial designs, each a specific design under consideration. The method is iterative and uses the gradient of the value function obtained from the preferences of the DM to eliminate lower value designs at each trial design. We present an approach for finding a new trial design at each iteration. We provide an example, the design selection for a cordless electric drill, to demonstrate the method. We provide results showing that (within the limit of our experimentation) our method needs only a few iterations to find the most preferred design alternative. Finally we extend our deterministic selection method to account for uncertainty in the attributes when the probability distributions governing the uncertainty are known.

Product Design Selection With Preference and Attribute Variability for an Implicit Value Function

2005 ◽  
Vol 128 (5) ◽  
pp. 1027-1037 ◽  
Author(s):  
A. K. Maddulapalli ◽  
S. Azarm
Keyword(s):  
Product Design ◽  
Value Function ◽  
Selection Process ◽  
Optimal Designs ◽  
Engineering Product ◽  
Marginal Rate ◽  
Marginal Rate Of Substitution ◽  
Design Alternatives ◽  
Attribute Levels ◽  
Design Selection

An important aspect of engineering product design selection is the inevitable presence of variability in the selection process. There are mainly two types of variability: variability in the preferences of the decision maker (DM) and variability in attribute levels of the design alternatives. We address both kinds of variability in this paper. We first present a method for selection with preference variability alone. Our method is interactive and iterative and assumes only that the preferences of the DM reflect an implicit value function that is differentiable, quasi-concave and non-decreasing with respect to attributes. The DM states his/her preferences with a range (due to the variability) for marginal rate of substitution (MRS) between attributes at a series of trial designs. The method uses the range of MRS preferences to eliminate “dominated designs” and then to find a set of “potentially optimal designs.” We present a payload design selection example to demonstrate and verify our method. Finally, we extend our method for selection with preference variability to the case where the attribute levels of design alternatives also have variability. We assume that the variability in attribute levels can be quantified with a range of attribute levels.

Product Design Selection With Variability in Preferences for an Implicit Value Function

2004 ◽  
Author(s):  
K. Maddulapalli ◽  
S. Azarm
Keyword(s):  
Product Design ◽  
Decision Maker ◽  
Value Function ◽  
End Users ◽  
Optimal Designs ◽  
Selection Methods ◽  
Marginal Rate ◽  
Marginal Rate Of Substitution ◽  
Design Selection ◽  
Rate Of Substitution

Many existing selection methods require that the Decision Maker (DM) state his/her preferences precisely. However, the DM may not have enough information about the needs of end users thus causing variability in the preferences. To address this problem, we present a method for selection that accounts for variability in the DM’s preferences. Our method is interactive and iterative and assumes only that the preferences of the DM reflect an implicit value function that is quasi-concave and non-decreasing with respect to attributes. Due to the variability, the DM states his/her preferences with a range for Marginal Rate of Substitution (MRS) between attributes at a series of trial designs. The method uses the range of MRS preferences to eliminate “dominated designs” and find a set of “non-eliminated designs”. We present a heuristic to reduce the set of non-eliminated designs and obtain a set of “potentially optimal designs”. The significance of potentially optimal designs is that only one of these designs will be the most preferred for any subset of the range of MRS preferences. We present a payload design selection example to demonstrate and verify that our method indeed finds the set of potentially optimal designs.

A Customer-Based Expected Utility Metric for Product Design Selection

2002 ◽  
Author(s):  
B. Besharati ◽  
S. Azarm ◽  
A. Farhang-Mehr
Keyword(s):  
Product Design ◽  
Design Process ◽  
Expected Utility ◽  
Attribute Level ◽  
Purchase Decisions ◽  
Design Alternative ◽  
Design Alternatives ◽  
Product Design Process ◽  
Attribute Levels ◽  
Design Selection

The ability to select a design alternative, from a set of feasible alternatives, that is likely to meet customers’ and designer’s preferences and also account for uncertainties is vital to the success of a product design process. This paper presents a new metric, a Customer-based Expected Utility (CEU) metric, for product design selection that accounts for a range of attribute levels (i.e., the customer range) within which customers make purchase decisions. The metric also accounts for designer’s preferences and uncertainty in achieving a desired attribute level (or a combination of attribute levels). The application of the CEU metric is demonstrated by rank-ordering a set of design alternatives for a cordless power tool. Using this metric, design alternatives that fall outside the customer range will yield a relatively low CEU value, while among those that fall in the customer range, the alternatives with a higher value of the designer’s utility yield a higher value of the CEU metric.

Compatibility Analysis of Product Design for Recyclability and Reuse

1994 ◽  
Author(s):  
Patrick Di Marco ◽  
Charles F. Eubanks ◽  
Kos Ishii
Keyword(s):  
Life Cycle ◽  
Cost Function ◽  
Product Design ◽  
Material Selection ◽  
Computer Tool ◽  
Compatibility Analysis ◽  
Life Cycle Design ◽  
Environmentally Responsible ◽  
Qualitative Knowledge ◽  
Selection For

Abstract This paper describes a method for evaluating the compatibility of a product design with respect to end-of-life product retirement issues, particularly recyclability. Designers can affect the ease of recycling in two major areas: 1) ease of disassembly, and 2) material selection for compatibility with recycling methods. The proposed method, called “clumping,” involves specification of the level of disassembly and the compatibility analysis of each remaining clump with the design’s post-life intent; i.e., reuse, remanufacturing, recycling, or disposal. The method uses qualitative knowledge to assign a normalized measure of compatibility to each clump. An empirical cost function maps the measure to an estimated cost to reprocess the product. The method is an integral part of our life-cycle design computer tool that effectively guides engineers to an environmentally responsible product design. A refrigerator in-door ice dispenser serves as an illustrative example.

Interactive Product Design

2011 ◽  
pp. 45-84 ◽  
Author(s):  
Xavier Fischer ◽  
Georges Fadel ◽  
Yann Ledoux
Keyword(s):  
Product Design ◽  
Interactive Product

scholarly journals The Method for Product Design Selection with Incomplete Linguistic Weight Information Based on Quality Function Deployment in a Fuzzy Environment

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Ming Li
Keyword(s):  
Product Design ◽  
Quality Function Deployment ◽  
Design Development ◽  
Quality Function ◽  
Advantages And Disadvantages ◽  
Fuzzy Environment ◽  
Design Requirements ◽  
Design Selection ◽  
The Given ◽  
The Relationship

The selection of a design for the given product is a critical problem in product design development. Focuses of the designers and customers on the design are not identical. In order to bridge the gap and provide a more relaxing way to select the design, a new method based on quality function deployment (QFD) is proposed. In such a method, customers are required to give their linguistic preferences on the design with respect to the customer requirements (CRs). In the rating of the weight of CRs, they are allowed to provide incomplete linguistic weight information and the objective optimization model is proposed to derive the exact linguistic weight information. Designers are required to rate the correlation between design requirements (DRs) and the relationship between the CRs and DRs to construct the house of quality. Opinions given by the customers are translated into the opinions with respect to the DRs based on the QFD. Afterwards, the priorities of the designs and design requirements are determined. The assessment results not only show the contribution of each design requirement to the customer satisfaction but also show the advantages and disadvantages of each design from the designers’ perspective clearly and directly. An example is provided to validate the applicability of the proposed method.

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