Abstract
The Internet shopping optimization problem (IShOP) is an NP-hard combinatorial problem, which minimizes the total cost of shopping a list of products available in a set of shops on the Internet, considering the product price plus the shipping costs. With the advent of electronic commerce and the incredible popularity of Internet transactions, IShOP has become a problem of great relevance today in modern society with several variations of the practical problem application. This chapter reviews the different approaches applied to solve the problem. We review the used models, the solution methods, and the instances used to analyze the performance algorithms. Finally, we identify the main current and future research trends.
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Acknowledgements
Authors thanks to CONACYT for supporting the projects from (a) Cátedras CONACYT Program with Number 3058. (b) CONACYT Project with Number A1-S-11012 from Convocatoria de Investigación Científica Básica 2017–2018 and CONACYT Project with Number 312397 from Programa de Apoyo para Actividades Científicas, Tecnológicas y de Innovación (PAACTI), a efecto de participar en la Convocatoria 2020-1 Apoyo para Proyectos de Investigación Científica, Desarrollo Tecnológico e Innovación en Salud ante la Contingencia por COVID-19. (c) M.A. García Morales would like to thank CONACYT for the support number 658787. Also H. Fraire thank to Tecnológico Nacional de México for the support to the project 10362.21-P.
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Morales, M.Á.G., Huacuja, H.J.F., Solís, J.F., Reyes, L.C., Santillán, C.G.G. (2023). A Survey of Models and Solution Methods for the Internet Shopping Optimization Problem. In: Castillo, O., Melin, P. (eds) Fuzzy Logic and Neural Networks for Hybrid Intelligent System Design. Studies in Computational Intelligence, vol 1061. Springer, Cham. https://doi.org/10.1007/978-3-031-22042-5_6
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