A General Approach to Product Design Optimization via Conjoint Analysis

1981 ◽  
Vol 45 (3) ◽  
pp. 17-37 ◽  
Author(s):  
Paul E. Green ◽  
J. Douglas Carroll ◽  
Stephen M. Goldberg
Keyword(s):  
Objective Function ◽  
Design Optimization ◽  
Product Design ◽  
Response Surface ◽  
Conjoint Analysis ◽  
Consumer Choice ◽  
Input Data ◽  
General Procedure ◽  
Marketing Research ◽  
Product Service

This paper describes some of the features of POSSE (Product Optimization and Selected Segment Evaluation), a general procedure for optimizing product/service designs in marketing research. The approach uses input data based on conjoint analysis methods. The output of consumer choice simulators is modeled by means of response surface techniques and optimized by different sets of procedures, depending upon the nature of the objective function.

Commercial Use of Conjoint Analysis: A Survey

1982 ◽  
Vol 46 (3) ◽  
pp. 44-53 ◽  
Author(s):  
Philippe Cattin ◽  
Dick R. Wittink
Keyword(s):  
Conjoint Analysis ◽  
Marketing Research ◽  
Service Characteristics ◽  
Commercial Use ◽  
Customer Preferences ◽  
Recent Advances ◽  
Product Service ◽  
Commercial Applications ◽  
The Impact

Conjoint analysis has been used extensively in marketing research to estimate the impact of selected product (service) characteristics on customer preferences for products (services). In this paper we discuss findings obtained from a survey of commercial users of the methodology. We project that around 1,000 commercial applications have been carried out during the last decade. We discuss the manner in which the methodology is used commercially, remaining issues that deserve further exploration, and recent advances or insights obtained by researchers working in this area.

A General Approach to Product Design Optimization Via Conjoint Analysis

1981 ◽  
Vol 45 (3) ◽  
pp. 17 ◽  
Author(s):  
Paul E. Green ◽  
J. Douglas Carroll ◽  
Stephen M. Goldberg
Keyword(s):  
Design Optimization ◽  
Product Design ◽  
Conjoint Analysis

Combining Marketing and Engineering Tools for Multi-Attribute Optimization

2007 ◽  
Author(s):  
Hemanth K. Amarchinta ◽  
Ramana V. Grandhi
Keyword(s):  
Objective Function ◽  
Design Optimization ◽  
Conjoint Analysis ◽  
Cantilever Beam ◽  
Multidisciplinary Design Optimization ◽  
Consumer Preferences ◽  
Computational Cost ◽  
Multidisciplinary Design ◽  
The Past ◽  
Single Objective

Multidisciplinary design optimization has been an active topic of research in the past two decades in developing algorithms for reducing computational cost of re-analysis and also in developing efficient ways of calculating sensitivities. Most of the efforts were aimed at single objective function (attribute). Also very little work is done to include designer’s preferences inside the optimization. In this paper, conjoint analysis, a popular marketing technique to assess consumer preferences is used to involve the preferences of the designer. The optimization is driven by the designer’s preferences and a preferred design is obtained. Here, a novel way of combining tools from marketing and engineering is shown. A cantilever beam, and a composite lightweight torpedo are used as examples to demonstrate the method.

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

ANN-Based Structure Optimization with Fatigue Reliability Constrains

2012 ◽  
Vol 204-208 ◽  
pp. 3128-3131
Author(s):  
Li Rong Sha ◽  
Yue Yang
Keyword(s):  
Structural Optimization ◽  
Objective Function ◽  
Reliability Analysis ◽  
Response Surface ◽  
Minimum Weight ◽  
Structure Optimization ◽  
Fatigue Reliability ◽  
Ann Model ◽  
Surface Method ◽  
The Relationship

The ANN-based optimization for considering fatigue reliability requirements in structural optimization was proposed. The ANN-based response surface method was employed for performing fatigue reliability analysis. The fatigue reliability requirements were considered as constraints while the weight as the objective function, the ANN model was adopted to establish the relationship between the fatigue reliability and geometry dimension of the structure, the optimal results of the structure with a minimum weight was reached.

Study on Mix-Variable Collaborative Design Optimization

2012 ◽  
Vol 215-216 ◽  
pp. 592-596
Author(s):  
Li Gao ◽  
Rong Rong Wang
Keyword(s):  
Design Optimization ◽  
Product Design ◽  
Optimization Algorithm ◽  
Collaborative Design ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Collaborative Optimization ◽  
Continuous Variables ◽  
Complex Product ◽  
Divide And Rule

In order to deal with complex product design optimization problems with both discrete and continuous variables, mix-variable collaborative design optimization algorithm is put forward based on collaborative optimization, which is an efficient way to solve mix-variable design optimization problems. On the rule of “divide and rule”, the algorithm decouples the problem into some relatively simple subsystems. Then by using collaborative mechanism, the optimal solution is obtained. Finally, the result of a case shows the feasibility and effectiveness of the new algorithm.

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