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Near Optimal Solutions for Product Line Design and Selection: Beam Search Heuristics

Author

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  • Suresh K. Nair

    (Department of Operations and Information Management, U41-1M, University of Connecticut, Storrs, Connecticut 06269-0241)

  • Lakshman S. Thakur

    (Department of Operations and Information Management, U41-1M, University of Connecticut, Storrs, Connecticut 06269-0241)

  • Kuang-Wei Wen

    (Department of Operations and Information Management, U41-1M, University of Connecticut, Storrs, Connecticut 06269-0241)

Abstract

Many practical product line design problems have large numbers of attributes and levels. In this case, if most attribute level combinations define feasible products, constructing product lines directly from part-worths data is necessary. For three typical formulations of this important problem, Kohli and Sukumar (Kohli, R., R. Sukumar. 1990. Heuristics for product-line design using conjoint analysis. Management Sci. 36 1464--1478.) present state-of-the-art heuristics to find good solutions. In this paper, we develop improved heuristics based on a beam search approach for solving these problems. In our computations for 435 simulated problems, significant improvements occur in five important performance measures used. Our heuristic solutions are closer to the optimal, have smaller standard deviation over replicates, take less computation time, obtain optimal solutions more often and identify a number of "good" product lines explicitly. Computation times for these problems are no more than 22 seconds on a PC, small enough for adequate sensitivity analysis. We also apply the heuristics to a real data set and clarify computational steps by giving a detailed example.

Suggested Citation

  • Suresh K. Nair & Lakshman S. Thakur & Kuang-Wei Wen, 1995. "Near Optimal Solutions for Product Line Design and Selection: Beam Search Heuristics," Management Science, INFORMS, vol. 41(5), pages 767-785, May.
    • Handle: RePEc:inm:ormnsc:v:41:y:1995:i:5:p:767-785
    DOI: 10.1287/mnsc.41.5.767
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    Cited by:

    1. Winfried Steiner & Harald Hruschka, 2002. "A Probabilistic One-Step Approach to the Optimal Product Line Design Problem Using Conjoint and Cost Data," Review of Marketing Science Working Papers 1-4-1003, Berkeley Electronic Press.
    2. Nair, Suresh K. & Tarasewich, Peter, 2003. "A model and solution method for multi-period sales promotion design," European Journal of Operational Research, Elsevier, vol. 150(3), pages 672-687, November.
    3. Alexouda, Georgia & Paparrizos, Konstantinos, 2001. "A genetic algorithm approach to the product line design problem using the seller's return criterion: An extensive comparative computational study," European Journal of Operational Research, Elsevier, vol. 134(1), pages 165-178, October.
    4. Winfried J. Steiner & Harald Hruschka, 2002. "Produktliniengestaltung mit Genetischen Algorithmen," Schmalenbach Journal of Business Research, Springer, vol. 54(7), pages 575-601, November.
    5. Xinfang (Jocelyn) Wang & Jeffrey D. Camm & David J. Curry, 2009. "A Branch-and-Price Approach to the Share-of-Choice Product Line Design Problem," Management Science, INFORMS, vol. 55(10), pages 1718-1728, October.
    6. Kyle D. Chen & Warren H. Hausman, 2000. "Technical Note: Mathematical Properties of the Optimal Product Line Selection Problem Using Choice-Based Conjoint Analysis," Management Science, INFORMS, vol. 46(2), pages 327-332, February.
    7. Sarangi, Subrat & Chakraborty, Abhishek & Triantis, Konstantinos P., 2021. "Multimarket competition effects on product line decisions – A multi-objective decision model in fast moving consumer goods industry," Journal of Business Research, Elsevier, vol. 133(C), pages 388-398.
    8. V. Krishnan & Rahul Singh & Devanath Tirupati, 1999. "A Model-Based Approach for Planning and Developing a Family of Technology-Based Products," Manufacturing & Service Operations Management, INFORMS, vol. 1(2), pages 132-156.
    9. Tallys H. Yunes & Dominic Napolitano & Alan Scheller-Wolf & Sridhar Tayur, 2007. "Building Efficient Product Portfolios at John Deere and Company," Operations Research, INFORMS, vol. 55(4), pages 615-629, August.
    10. G. E. Fruchter & A. Fligler & R. S. Winer, 2006. "Optimal Product Line Design: Genetic Algorithm Approach to Mitigate Cannibalization," Journal of Optimization Theory and Applications, Springer, vol. 131(2), pages 227-244, November.
    11. Alexandre Belloni & Robert Freund & Matthew Selove & Duncan Simester, 2008. "Optimizing Product Line Designs: Efficient Methods and Comparisons," Management Science, INFORMS, vol. 54(9), pages 1544-1552, September.
    12. Wang, Xinfang (Jocelyn) & Curry, David J., 2012. "A robust approach to the share-of-choice product design problem," Omega, Elsevier, vol. 40(6), pages 818-826.
    13. Xin Jia Jiang & Yanhua Xu & Chenhao Zhou & Ek Peng Chew & Loo Hay Lee, 2018. "Frame Trolley Dispatching Algorithm for the Frame Bridge Based Automated Container Terminal," Transportation Science, INFORMS, vol. 52(3), pages 722-737, June.
    14. Tsafarakis, Stelios & Zervoudakis, Konstantinos & Andronikidis, Andreas & Altsitsiadis, Efthymios, 2020. "Fuzzy self-tuning differential evolution for optimal product line design," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1161-1169.
    15. Albritton, M. David & McMullen, Patrick R., 2007. "Optimal product design using a colony of virtual ants," European Journal of Operational Research, Elsevier, vol. 176(1), pages 498-520, January.
    16. Tsafarakis, Stelios & Marinakis, Yannis & Matsatsinis, Nikolaos, 2011. "Particle swarm optimization for optimal product line design," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 13-22.
    17. Büther, Marcel, 2008. "Beam search for the elastic generalized assignment problem," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 634, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    18. Day, Jamison M. & Venkataramanan, M.A., 2006. "Profitability in product line pricing and composition with manufacturing commonalities," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1782-1797, December.
    19. Stelios Tsafarakis, 2016. "Redesigning product lines in a period of economic crisis: a hybrid simulated annealing algorithm with crossover," Annals of Operations Research, Springer, vol. 247(2), pages 617-633, December.
    20. Tan Wang & Genaro Gutierrez, 2022. "Robust Product Line Design by Protecting the Downside While Minding the Upside," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 194-217, January.
    21. McMullen, P.R. & Tarasewich, Peter, 2005. "A beam search heuristic method for mixed-model scheduling with setups," International Journal of Production Economics, Elsevier, vol. 96(2), pages 273-283, May.
    22. Leyuan Shi & Sigurdur Ólafsson & Qun Chen, 2001. "An Optimization Framework for Product Design," Management Science, INFORMS, vol. 47(12), pages 1681-1692, December.
    23. Kilsun Kim & Dilip Chhajed, 2002. "Product Design with Multiple Quality-Type Attributes," Management Science, INFORMS, vol. 48(11), pages 1502-1511, November.
    24. Kamalini Ramdas & Mohanbir S. Sawhney, 2001. "A Cross-Functional Approach to Evaluating Multiple Line Extensions for Assembled Products," Management Science, INFORMS, vol. 47(1), pages 22-36, January.
    25. Tarasewich, Peter & McMullen, Patrick R., 2001. "A pruning heuristic for use with multisource product design," European Journal of Operational Research, Elsevier, vol. 128(1), pages 58-73, January.

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