Conjoint Optimization: An Exact Branch-and-Bound Algorithm for the Share-of-Choice Problem

Published Online:https://doi.org/10.1287/mnsc.1050.0461

References

  • Allenby G. M., Rossi P. Marketing models of consumer heterogeneity. J. Econometrics (1999) 89:57–78CrossrefGoogle Scholar
  • Allenby G. M., Arora N., Ginter J. L. Incorporating prior knowledge into the analysis of conjoint studies. J. Marketing Res. (1995) 32(May):152–162CrossrefGoogle Scholar
  • Allenby G. M., Arora N., Ginter J. L. On the heterogeneity of demand. J. Marketing Res. (1998) 35:384–389CrossrefGoogle Scholar
  • Allenby G. M., Arora N., Diener C., Kim J., Lotti M., Markowitz P. Distinguishing likelihoods, loss functions and heterogeneity in the evaluation of marketing models. Canadian J. Marketing Res. (2002) 20(1):44–45Google Scholar
  • Balakrishnan P. V., Jacob V. S. Genetic algorithms for product design. Management Sci. (1996) 42(8):1105–1117LinkGoogle Scholar
  • Bradlow E. T. Current issues and a “wish list” for conjoint analysis. (2003) 1–10Working paper, The Wharton School, University of Pennsylvania, Philadelphia, PAGoogle Scholar
  • Church R. L., ReVelle C. The maximal covering location problem. Papers Regional Sci. Assoc. (1974) 23:101–118CrossrefGoogle Scholar
  • Cochran J. J. Statistical characteristics of coverage optimization based on sample data. (1997) . Doctoral dissertation, University of Cincinnati, Cincinnati, OHGoogle Scholar
  • Curry D. J. Hurdle models for conjoint optimization: Exposition and extensions. (2004) . Working paper, University of Cincinnati, Cincinnati, OHGoogle Scholar
  • Dahan E., Srinivasan V. The predictive power of Internet-based product concept testing using visual depiction and animation. J. Product Innovation Management (2000) 17(March):99–109CrossrefGoogle Scholar
  • Dahan E., Hauser J. R., Simester D., Toubia O. Application and test of Web-based adaptive polyhedral conjoint analysis. (2002) . Paper 146, Sloan School of Management, MIT, Cambridge, MAGoogle Scholar
  • Downs B. T., Camm J. D. An exact algorithm for the maximal covering problem. Naval Res. Logist. (1996) 43:435–461CrossrefGoogle Scholar
  • Fourer R., Gay D. M., Kernighan B. W.AMPL, A Modeling Language for Mathematical Programming (2002) 2nd ed.(Boyd and Fraser Publishing Company, Danvers, MA) Google Scholar
  • Gilbride T. J., Allenby G. M. A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Sci. (2004) 23(3):391–406LinkGoogle Scholar
  • Green P. E., Krieger A. M. Choice rules and sensitivity analysis in conjoint simulators. J. Acad. Marketing Sci. (1988) 16(1):114–127CrossrefGoogle Scholar
  • Green P. E., Krieger A. M. Recent contributions to optimal product positioning and buyer segmentation. Eur. J. Oper. Res. (1989) 41:127–141CrossrefGoogle Scholar
  • Green P. E., Krieger A. M. Conjoint analysis with product-positioning applications. Handbooks in Operations Research & Management Science (1993) Volume 5(North-Holland, New York)467–515Google Scholar
  • Green P. E., Carroll J. D., Goldberg S. M. A general approach to product design optimization via conjoint analysis. J. Marketing (1981) 45:17–37CrossrefGoogle Scholar
  • Green P. E., Krieger A. M., Wind Y. Thirty years of conjoint analysis: Reflections and prospects. Interfaces (2001) 31(3):S56–S73LinkGoogle Scholar
  • Green P. E., Krieger A. M., Wind J., Wind J., Green P. E. Buyer choice simulators, optimizers, and dynamic models. Market Research and Modeling: Progress and Prospects, A Tribute to Paul E. Green (2003) (Kluwer Academic Publishers, Norwell, MA) Google Scholar
  • Hauser J. R., Tellis G. J., Griffin A. Research on innovation: A review and agenda for marketing science. (2004) . Working paper, Sloan School of Management, MIT, Cambridge, MAGoogle Scholar
  • Huber J., Orme B. K., Miller R., Gustafsson A., Herrmann A., Huber F. Dealing with product similarity in conjoint simulation. Conjoint Measurement (2001) 2nd ed(Springer-Verlag, Berlin-Heidelberg, Germany) 479–496CrossrefGoogle Scholar
  • ILOG, Inc.ILOG CPLEX 8.0 User’s Manual (2003) (Mountain View, CA)Google Scholar
  • Johnson R. Accuracy of utility estimation in ACA. (1987) . Working paper, Sawtooth Software, Sequim, WAGoogle Scholar
  • Kohli R., Krishnamurti R. A heuristic approach to product design. Management Sci. (1987) 33:1523–1533LinkGoogle Scholar
  • Kohli R., Krishnamurti R. Optimal product design using conjoint analysis: Computational complexity and algorithms. Eur. J. Oper. Res. (1989) 40:186–195CrossrefGoogle Scholar
  • Kohli R., Sukumar R. Heuristics for product line design using conjoint analysis. Management Sci. (1990) 36(12):1464–1478LinkGoogle Scholar
  • Krieger A. M., Green P. E., Wind J. Adventures in conjoint analysis: A practitioner’s guide to trade-off modeling and applications. (2004) . Working paper, The Wharton School, University of Pennsylvania, Philadelphia, PAGoogle Scholar
  • Kuhfeld W., Tobias R. D., Garratt M. Efficient experimental design with marketing research applications. J. Marketing Res. (1994) 31(November):545–557CrossrefGoogle 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):173–191LinkGoogle Scholar
  • McBride R. D., Zufryden F. S. An integer programming approach to the optimal product line selection problem. Marketing Sci. (1988) 7(2):126–140LinkGoogle Scholar
  • Orme B. K., Huber J. Improving the value of conjoint simulations. Marketing Res. (2000) 12(4):12–20Google Scholar
  • Rossi P. E., Allenby G. M. Bayesian statistics and marketing. Marketing Sci. (2003) 22(3):304–328LinkGoogle Scholar
  • Sawtooth Software Advanced simulation module for product optimization V1. (2003) . Technical paper, Sawtooth Software, Inc., Sequim, WA, 1–26Google Scholar
  • Shi L., Olafsson S. Nested partitions method for global optimization. Oper. Res. (2000) 48:390–407LinkGoogle Scholar
  • Shi L., Olafsson Q., Chen S. An optimization framework for product design. Management Sci. (2001) 47(12):1681–1692LinkGoogle Scholar
  • Toubia O., Simester D. I., Hauser J. R., Dahan E. Fast polyhedral adaptive conjoint estimation. Marketing Sci. (2003) 22(Summer):273–303LinkGoogle Scholar
  • Toubia O., Hauser J. R., Simester D. I. Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. (2004) 41(February):116–131CrossrefGoogle Scholar
  • Train K. E.Discrete Choice Methods with Simulation (2003) (Cambridge University Press, Cambridge, UK) CrossrefGoogle Scholar
  • Vavra T. G., Green P. E., Krieger A. M. Evaluating EZ-Pass: Using conjoint analysis to assess consumer response to a new tollway technology. Marketing Res. (1999) 11(2):5–13Google Scholar
  • Wind J., Green P. E., Shifflet D., Scarbrough M. Courtyard by Marriott: Designing a hotel facility with consumer-based marketing models. Interfaces (1989) 19:25–47LinkGoogle Scholar
  • Wittink D. R., Cattin P. Commercial use of conjoint analysis: An update. J. Marketing (1989) 53:91–96CrossrefGoogle Scholar
  • Wittink D. R., Vriens M., Burhenne W. Commercial use of conjoint analysis in Europe: Results and critical reflections. Internat. J. Res. Marketing (1994) 11:41–52CrossrefGoogle 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 Market Planning (1977) (Marketing Science Institute, Cambridge, MA) 100–114Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.