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2020 | OriginalPaper | Chapter

A New Hybrid Firefly – Genetic Algorithm for the Optimal Product Line Design Problem

Authors : Konstantinos Zervoudakis, Stelios Tsafarakis, Sovatzidi Paraskevi-Panagiota

Published in: Learning and Intelligent Optimization

Publisher: Springer International Publishing

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Abstract

The optimal product line design is one of the most critical decisions for a firm to stay competitive, since it is related to the sustainability and profitability of a company. It is classified as an NP-hard problem since no algorithm can certify in polynomial time that the optimum it identifies is the overall optimum of the problem. The focus of this research is to propose a new hybrid optimization method (FAGA) combining Firefly algorithm (FA) and Genetic algorithm (GA). The proposed hybrid method is applied to the product line design problem and its performance is compared to those of previous approaches, like genetic algorithm (GA) and simulated annealing (SA), by using both actual and artificial consumer-related data preferences for specific products. The comparison results demonstrate that the proposed hybrid method is superior to both genetic algorithm and simulated annealing in terms of accuracy, efficiency and convergence speed.

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previous chapter Sonar Inspired Optimization in Energy Problems Related to Load and Emission Dispatch
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Literature
1.
Kotler, P., Armstrong, G.: Principles of Marketing. Pearson/Prentice Hall, Upper Saddle River (2012)
2.
Luce, R.D., Tukey, J.W.: Simultaneous conjoint measurement: a new type of fundamental measurement. J. Math. Psychol. 1, 1–27 (1964)CrossRef
3.
Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Prentice Hall, Upper Saddle River (1982)MATH
4.
Kohli, R., Sukumar, R.: Heuristics for product-line design using conjoint analysis. Manage. Sci. 36, 1464–1478 (1990)CrossRef
5.
Nair, S.K., Thakur, L.S., Wen, K.-W.: Near optimal solutions for product line design and selection: beam search heuristics. Manage. Sci. 41, 767–785 (1995)CrossRef
6.
Balakrishnan, P.V., Gupta, R., Jacob, V.S.: Development of hybrid genetic algorithms for product line designs. IEEE Trans. Syst. Man, Cybern. Part B Cybern. 34, 468–483 (2004)CrossRef
7.
Camm, J.D., Cochran, J.J., Curry, D.J., Kannan, S.: Conjoint optimization: an exact branch-and-bound algorithm for the share-of-choice problem. Manage. Sci. 52, 435–447 (2006)CrossRef
8.
Belloni, A., Freund, R., Selove, M., Simester, D.: Optimizing product line designs: efficient methods and comparisons. Manage. Sci. 54, 1544–1552 (2008)CrossRef
9.
Tsafarakis, S., Marinakis, Y., Matsatsinis, N.: Particle swarm optimization for optimal product line design. Int. J. Res. Mark. 28, 13–22 (2011)CrossRef
10.
Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Bristol (2008)
11.
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)MATH
12.
Elkhechafi, M., Hachimi, H., Elkettani, Y.: A new hybrid firefly with genetic algorithm for global optimization. Int. J. Manag. Appl. Sci. 3, 47–51 (2017)MATH
13.
Masouleh, M.F., Kazemi, M.A.A., Alborzi, M., Eshlaghy, A.T.: Engineering, technology & applied science research. ETASR (2016)
14.
Rahmani, A., MirHassani, S.A.: A hybrid Firefly-Genetic Algorithm for the capacitated facility location problem. Inf. Sci. (Ny) 283, 70–78 (2014)MathSciNetCrossRef
15.
16.
Kohli, R., Krishnamurti, R.: Optimal product design using conjoint analysis: computational complexity and algorithms. Eur. J. Oper. Res. 40, 186–195 (1989)MathSciNetCrossRef
17.
Toubia, O., Simester, D.I., Hauser, J.R., Dahan, E.: Fast polyhedral adaptive conjoint estimation. Mark. Sci. 22, 273–303 (2003)CrossRef
18.
Fisher, M.L.: The Lagrangian relaxation method for solving integer programming problems. Manage. Sci. 27, 1–18 (1981)MathSciNetCrossRef
Metadata
Title
A New Hybrid Firefly – Genetic Algorithm for the Optimal Product Line Design Problem
Authors
Konstantinos Zervoudakis
Stelios Tsafarakis
Sovatzidi Paraskevi-Panagiota
Copyright Year
2020
Book
Learning and Intelligent Optimization

Print ISBN: 978-3-030-38628-3

Electronic ISBN: 978-3-030-38629-0

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-38629-0_23

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