Sensitivity analysis for product design selection with an implicit value function

2007 ◽  
Vol 180 (3) ◽  
pp. 1245-1259 ◽  
Author(s):  
A.K. Maddulapalli ◽  
S. Azarm ◽  
A. Boyars
Keyword(s):  
Sensitivity Analysis ◽  
Product Design ◽  
Value Function ◽  
Design Selection

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.

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.

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.

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.

Customer-Driven Product Design Selection Using Web Based User-Generated Content

2011 ◽  
Author(s):  
L. Wang ◽  
B. D. Youn ◽  
S. Azarm ◽  
P. K. Kannan
Keyword(s):  
Product Design ◽  
User Generated Content ◽  
Web Based ◽  
Customer Data ◽  
Survey Techniques ◽  
Customer Preferences ◽  
Public Media ◽  
Systematic Effort ◽  
Design Selection ◽  
Customer Survey

Acquisition of the customer data for product design selection using conventional customer survey techniques can be a time-consuming and costly undertaking. The aim of this paper is to overcome this limitation by using web based User-Generated Content (UGC) as an alternative to the conventional customer survey techniques. UGC refers to various public media contents created by web users including contents in online customer reviews, blogs, and social networking interactions. So far, there has not been any systematic effort in using UGC in design selection for a customer durable product. Using UGC in product design selection is not an easy task because UGC can be freely expressed and written by customers with little constraints, structure and bounds. As a result, UGC can contain a lot of noise, variability in content and even bias induced by the customers. In order to make use of UGC, this paper develops a systematic methodology for eliciting product attributes from UGC, constructing customer preference models and using these models in design selection. To demonstrate the proposed method, design selection of a smartphone using UGC is considered as an example. It is shown in the example that the proposed method can provide a reasonable estimation of customer preferences while being useful for product design selection.

Quantifying the Importance of Solar Soft Costs: A New Method to Apply Sensitivity Analysis to a Value Function

2020 ◽  
Vol 142 (12) ◽  
Author(s):  
Sita M. Syal ◽  
Erin F. MacDonald
Keyword(s):  
Sensitivity Analysis ◽  
Cost Reduction ◽  
Research Funding ◽  
Value Function ◽  
Cost Model ◽  
Department Of Energy ◽  
Potential Cost ◽  
Industry Growth ◽  
Cost Of Energy ◽  
Solar Research

Abstract This paper presents a new approach to build a decision model for government funding agencies, such as the US Department of Energy (DOE) solar office, to evaluate solar research funding strategies. High solar project costs—including technology costs, such as modules, and soft costs, such as permitting—currently hinder many installations; project cost reduction could lead to a lower project levelized cost of energy (LCOE) and, in turn, higher installation rates. Government research funding is a crucial driver to solar industry growth and potential cost reduction; however, DOE solar funding has not historically aligned with the industry priorities for LCOE reduction. Solar technology has received significantly higher research funding from the DOE compared to soft costs. Increased research funding to soft cost programs could spur needed innovation and accelerate cost reduction for the industry. To this end, we build a cost model to calculate the LCOE of a utility-scale solar development using technology and soft costs and conduct a sensitivity analysis to quantify how the inputs influence the LCOE. Using these results, we develop a multi-attribute value function and evaluate six funding strategies as possible alternatives. We find the strategy based on current DOE allocations results in the lowest calculated value and the strategy that prioritizes soft cost results in the highest calculated value, suggesting alternative ways for the DOE solar office to prioritize research funding and potentially spur future cost reduction.

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