A Branch-and-Price Approach to the Share-of-Choice Product Line Design Problem

References

• Balakrishnan P. V., Gupta R., Jacob V. Development of hybrid genetic algorithms for product line designs. IEEE Trans. Systems, Man, Cybernetics B: Cybernetics (2004) 34(1):468–483Crossref, Google Scholar
• Belloni A., Freund R., Selove M., Simester D. Optimizing product line designs: Efficient methods and comparisons. Management Sci. (2008) 54(9):1544–1552Link, Google Scholar
• Camm J. D., Cochran J. J., Curry D. J., Kannan S. Conjoint optimization: An exact algorithm for the share-of-choice problem. Management Sci. (2006) 52(3):435–447Link, Google Scholar
• Church R. L., ReVelle C. The maximal covering location problem. Papers Regional Sci. Assoc. (1974) 23:101–118Crossref, Google Scholar
• Cortes C., Vapnik V. Support vector networks. Machine Learn. (1995) 20:273–297Google Scholar
• Degraeve Z., Schrage L. Optimal integer solutions to industrial cutting stock problems. INFORMS J. Comput. (1999) 11(4):406–419Link, Google Scholar
• Dobson G., Kalish S. Positioning and pricing a product line. Marketing Sci. (1988) 7(2):107–125Link, Google Scholar
• Dobson G., Kalish S. Heuristics for pricing and positioning a product line using conjoint and cost data. Management Sci. (1993) 39(2):160–175Link, Google Scholar
• Dolan R. J., Simon H.Power Pricing (1996) (Free Press, New York) Google Scholar
• Evgeniou T., Pontil M., Toubia O. A convex optimization approach to modeling consumer heterogeneity in conjoint estimation. Marketing Sci. (2007) 26(6):805–818Link, Google Scholar
• Green P. E., Krieger A. M. Models and heuristics for product line selection. Marketing Sci. (1985) 4(1):1–19Link, Google Scholar
• Green P. E., Krieger A. M., Wind Y. J. Thirty years of conjoint analysis: Reflections and prospects. Interfaces (2001) 31(3):56–73Link, Google Scholar
• Hauser J. R., Tellis G. J., Griffin A. Research on innovation: A review and agenda for Marketing Science. Marketing Sci. (2006) 25(6):687–717Link, Google Scholar
• Jiao J., Zhang Y., Wang Y. Product portfolio planning with customer-engineering interaction. IEEE Trans. (2005) 37:801–814Crossref, Google Scholar
• Jiao J., Zhang Y., Wang Y. A heuristic genetic algorithm for product portfolio planning. Comput. Oper. Res. (2007) 34:1777–1799Crossref, Google Scholar
• Kohli R., Krishnamurti R. Optimal product design using conjoint analysis: Computational complexity and algorithm. Eur. J. Oper. Res. (1989) 40(2):186–195Crossref, Google Scholar
• Kohli R., Sukumar R. Heuristics for product-line design using conjoint analysis. Management Sci. (1990) 36(12):1464–1478Link, Google Scholar
• Kuhfeld W. F. Conjoint analysis. (2003) . Technical paper, SAS Institute, Cary, NC. Accessed April 29, 2007, http://support.sas.com/techsup/tnote/tnote_stat.htmlGoogle Scholar
• Lenk P., DeSarbo W., Green P. E., Young M. Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Sci. (1996) 15(2):173–191Link, Google Scholar
• Louviere J. J., Woodworth G. Design and analysis of simulated consumer choice or allocation experiments: An approach based on aggregate data. J. Marketing Res. (1983) 20:350–367Crossref, Google Scholar
• Luce R. D., Tukey J. W. Simultaneous conjoint measurement: A new type of fundamental measurement. J. Math. Psych. (1964) 1:1–27Crossref, Google Scholar
• McBride R. D., Zufryden F. S. An integer programming approach to the optimal product line selection problem. Marketing Sci. (1988) 7(2):126–140Link, Google Scholar
• McFadden D., Zarembka P. Conditional logit analysis of qualitative choice behavior. Frontiers in Econometrics (1974) (Academic Press, New York) 105–142Google Scholar
• Michalek J. J., Feinberg F. M., Papalambros P. Y. Linking marketing and engineering product design decisions via analytical target cascading. J. Product Innovation Managagement (2005) 22(1):42–62Crossref, Google Scholar
• Michalek J. J., Feinberg F. M., Adizuzel F., Ebbes P., Papalambros P. Y. Realizable product line design optimization: Coordinating marketing and engineering models via analytical target cascading. (2007) . Working paper, Department of Mechanical Engineering, University of Michigan, Ann ArborGoogle Scholar
• Nair S. K., Thakur L., Wen K. W. Near optimal solutions for product line design and selection: Beam search heuristic. Management Sci. (1995) 41(5):767–785Link, Google Scholar
• Nemhauser G. L., Wolsey L. A.Integer and Combinatorial Optimization (1988) (John Wiley & Sons, New York) Crossref, Google Scholar
• Rossi P. E., Allenby G. M. Bayesian statistics and marketing. Marketing Sci. (2003) 22(3):304–328Link, Google Scholar
• Rossi P. E., Allenby G. M., McCulloch R.Bayesian Statistics and Marketing (2005) (John Wiley & Sons, New York) Crossref, Google Scholar
• Shi L., Olafsson S., Chen Q. An optimization framework for product design. Management Sci. (2001) 47(12):1681–1692Link, Google Scholar
• Toubia O., Hauser J. R., Simester D. I. Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. (2004) 41(February):116–131Crossref, Google Scholar
• Toubia O., Simester D. I., Hauser J. R., Dahan E. Fast polyhedral adaptive conjoint estimation. Marketing Sci. (2003) 22(3):273–303Link, Google Scholar
• Train K. E.Discrete Choice Methods with Simulation (2003) (Cambridge University Press, Cambridge, UK) Crossref, Google Scholar
• Wittink D. R., Cattin P. Commercial use of conjoint analysis: An update. J. Marketing (1989) 53:91–96Crossref, Google Scholar
• Wittink D. R., Vriens M., Burhenne W. Commercial use of conjoint analysis in Europe: Results and critical reflections. Internat. J. Res. Marketing (1994) 11:41–52Crossref, Google Scholar
• Zufryden F. S., Shocker A. D. A conjoint measurement-based approach for optimal new product design and market segmentation. Analytical Approaches to Product and Marketing Planning (1977) (Marketing Science Institute, Cambridge, MA) 100–114Google Scholar
• Zufryden F. S. Product line optimization by integer programming. Proc. Annual Meeting of ORSA/TIMS (1982) San DiegoGoogle Scholar