An Evolutionary Algorithm for Optimal Fuel-Type Configurations in Car Lines
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An Evolutionary Algorithm for Optimal Fuel-Type Configurations in Car Lines

Authors: Charalampos Saridakis, Stelios Tsafarakis

Abstract:

Although environmental concern is on the rise across Europe, current market data indicate that adoption rates of environmentally friendly vehicles remain extremely low. Against this background, the aim of this paper is to a) assess preferences of European consumers for clean-fuel cars and their characteristics and b) design car lines that optimize the combination of fuel types among models in the line-up. In this direction, the authors introduce a new evolutionary mechanism and implement it to stated-preference data derived from a large-scale choice-based conjoint experiment that measures consumer preferences for various factors affecting clean-fuel vehicle (CFV) adoption. The proposed two-step methodology provides interesting insights into how new and existing fuel-types can be combined in a car line that maximizes customer satisfaction.

Keywords: Clean-fuel vehicles, product line design, conjoint analysis, choice experiment, differential evolution.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1131559

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[1] Balakrishnan, P., Gupta, R., & Jacob, V. (2004). Development of hybrid genetic algorithms for product line designs. IEEE Transactions on Systems, Man, and Cybernetics, 34, 468-483.
[2] Belloni, A., Freund, R., Selove, M., & Simester, D. (2008). Optimizing Product Line Designs: Efficient Methods and Comparisons. Management Science, 54, 1544-1552.
[3] Dagsvik, J. K., & Liu, G. (2009). A Framework for Analyzing Rank-Ordered Data with Application to Automobile Demand. Transportation Research Part A, 43, 1–12.
[4] Engelbrecht, A. P. (2007). Computational Intelligence: An Introduction. Wiley.
[5] European Commission (2012). Reducing CO2 emissions from passenger cars, 30 July.
[6] Hidrue, M. K., Parsons, G. R., Kempton, W., & Gardner, M. P. (2011). Willingness to Pay for Electric Vehicles and Their Attributes. Resource and Energy Economics, 33, 686–705.
[7] Kohli, R., & Sukumar, R. (1990). Heuristics for product line design using conjoint analysis. Management Science, 36, 1464-1478.
[8] Nair, S. K., Thakur, L. S., & Wen, K. (1995). Near optimal solutions for product line design and selection: Beam Search heuristics. Management Science, 41, 767-785.
[9] Olson, E. L. (2013). It’s not easy being green: the effects of attribute tradeoffs on green product preference and choice. Journal of the Academy of Marketing Science, 41, 171-184.
[10] Potoglou, D., & Kanaroglou, P. S. (2007). Household demand and willingness to pay for clean vehicles. Transportation Research Part D, 12, 264-274.
[11] Storn, R. & Price, K. (1997). Differential evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.
[12] Train, K. E. (2008). EM Algorithms for Nonparametric Estimation of Mixing Distributions. Journal of Choice Modelling, 1, 40–69.
[13] Tsafarakis, S., Marinakis, Y., & Matsatsinis, N. (2011). Particle swarm optimization for optimal product line design. International Journal of Research in Marketing, 28, 13 – 22.