A bibliometric analysis of the product line design problem

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. 

• Fecha de publicación: 2021
• Institución: Universidad Pedagógica y Tecnológica de Colombia
• Repositorio: RiUPTC: Repositorio Institucional UPTC
• Idioma: eng
• OAI Identifier: oai:repositorio.uptc.edu.co:001/12496
• Acceso en línea: https://revistas.uptc.edu.co/index.php/inquietud_empresarial/article/view/11411; https://repositorio.uptc.edu.co/handle/001/12496
• Palabra clave: Bibliometric; product line design problem; product line development; new product line; product line design; genetic algorithm; bibliométrico, problema de diseño de línea de producto, algoritmos genéticos.; Bibliometrico; problema de diseño de línea de producto; desarrollo de línea de prodcuto; nueva línea de producto; diseño de línea de producto; algoritmo genetico
• License: Derechos de autor 2021 Alma Montserrat Romero Serrano