Genetic Algorithms for Product Design: How Well do They Really Work?

2003 ◽  
Vol 45 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Winfried Steiner ◽  
Harald Hruschka
Keyword(s):  
Genetic Algorithms ◽  
Product Design ◽  
New Product ◽  
Product Line ◽  
Greedy Heuristic ◽  
Genetic Operators ◽  
Design Decisions ◽  
Economic Criterion ◽  
Product Line Design ◽  
Line Design

Recently, Balakrishnan and Jacob (1996) have proposed the use of Genetic Algorithms (GA) to solve the problem of identifying an optimal single new product using conjoint data. Here we extend and evaluate the GA approach with regard to the more general problem of product line design. We consider profit contribution as a firm's economic criterion to evaluate product design decisions and illustrate how the genetic operators work to find the product line with maximum profit contribution. In a Monte Carlo simulation, we assess the performance of the GA methodology in comparison to Green and Krieger's (1985) greedy heuristic.

scholarly journals A bibliometric analysis of the product line design problem

2021 ◽  
Vol 21 (1) ◽  
pp. 11-33
Author(s):  
Alma Montserrat Romero-Serrano ◽  
Omar Ahumada-Valenzuela ◽  
Juan Carlos Leyva-Lopez ◽  
Marlenne Gisela Velazquez-Cazares
Keyword(s):  
Genetic Algorithms ◽  
Product Design ◽  
Bibliometric Analysis ◽  
Design Problem ◽  
Optimal Solution ◽  
Search Space ◽  
Research Field ◽  
Product Line ◽  
Productive Author ◽  
Line Design

In the product design problem, firms aim to find suitable configurations of product attributes with the objective of increasing their participation in the marketplace. This problem belongs to the field of quantitative marketing and is considered a NP-Hard problem, due to its wide search space for an optimal solution. Among the related literature, there are different methodologies to address this problem, gaining ground those that apply metaheuristics, with an emphasis in Genetic Algorithms. The main aim of this work is to present an overview of the most significant contributions in this area using a bibliometric analysis approach. The paper uses Scopus database and Web of Science Core Collection, in order to obtain leading and the most influential articles, conferences papers, journals, authors, institutions and countries. The results highlight Kwong, C.K. as the most productive author while Nagamachi M. is the most influential author. Furthermore, China is the leading country in this research field. The use of Genetic Algorithms in the solutions of the Product Design Problem is a growing area of study with important development of methodologies and approaches.  JEL Codes: C00, C02 Received: 07/10/2020.  Accepted: 20/02/2021.  Published: 01/06/2021. 

Clonal Selection Algorithms for Optimal Product Line Design: A Comparative Study

2021 ◽  
Author(s):  
Michail Pantourakis ◽  
Stelios Tsafarakis ◽  
Konstantinos Zervoudakis ◽  
Efthymios Altsitsiadis ◽  
Andreas Andronikidis ◽  
...  
Keyword(s):  
Comparative Study ◽  
Clonal Selection ◽  
Product Line ◽  
Product Line Design ◽  
Clonal Selection Algorithms ◽  
Selection Algorithms ◽  
Line Design
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