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.
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References
1.
BalakrishnanP.V. (Sundar), and JacobV.S. (1996) Genetic Algorithms for product design.Management Science, 42, pp. 1105–1117.
2.
CattinP., and WittinkD.R. (1982) Commercial use of conjoint analysis: a survey.Journal of Marketing, 46, pp. 44–53.
3.
ChenK.D., and HausmanW.H. (2000) Technical note: mathematical properties of the optimal product line selection problem using choicebased conjoint analysis.Management Science, 46, pp. 327–332.
4.
DawidH.. (1996) Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models.Berlin: Springer.
5.
DobsonG., and KalishS.. (1988) Positioning and pricing a product line.Marketing Science, 7, pp. 107–125.
6.
DobsonG., and KalishS.. (1993) Heuristics for pricing and positioning a productline using conjoint and cost data.Management Science, 39, pp. 160–175.
7.
GoldbergD.E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning.Reading, MA: Addison Wesley.
8.
GreenP.E., and KriegerA.M. (1985) Models and heuristics for product line selection.Marketing Science, 4, pp. 1–19.
9.
GreenP.E., and KriegerA.M. (1992) An application of a product positioning model to pharmaceutical products.Marketing Science, 11, pp. 117–132.
10.
GreenP.E., and RaoV.R. (1971) Conjoint measurement for quantifying judgmental data.Journal of Marketing Research, 8, pp. 355–363.
11.
GreenP.E., and SrinivasanV.. (1978) Conjoint analysis in consumer research: issues and outlook.Journal of Consumer Research, 5, pp. 103–123.
12.
GreenP.E., and SrinivasanV.. (1990) Conjoint analysis in marketing: new developments with implications for research and practice.Journal of Marketing, 54, pp. 3–19.
13.
HollandJ.H. (1975) Adaptation in Natural and Artificial Systems.Ann Arbor. MI: The University of Michigan Press.
14.
KaulA., and RaoV.R. (1995) Research for product positioning and design decisions: an integrative review.International Journal of Research in Marketing, 12, pp. 293–320.
15.
KohliR., and SukumarR.. (1990) Heuristics for productline design using conjoint analysis.Management Science, 36, pp. 1464–1478.
16.
McBrideR.D., and ZufrydenF.S. (1988) An integer programming approach to the optimal product line selection problem.Marketing Science, 7, pp. 126–140.
17.
McFaddenD.. (1974) Conditional logit analysis of qualitative choice behavior. In: ZarembkaP.. (ed.) Frontiers of Econometrics.New York: Academic Press, pp. 105–142.
NairS.K., ThakurL.S., and WenK.W. (1995) Near optimal solutions for product line design and selection: beam search heuristics.Management Science, 41, pp. 767–785.
20.
WittinkD.R., and CattinP.. (1989) Commercial use of conjoint analysis: an update.Journal of Marketing, 53, pp. 91–96.
21.
WittinkD.R., VriensM., and BurhenneW.. (1994) Commercial use of conjoint analysis in Europe: results and critical reflections.International Journal of Research in Marketing, 11, pp. 41–52.
