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

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Author Info

  • 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)

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    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.

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    File URL: http://dx.doi.org/10.1287/mnsc.41.5.767
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 41 (1995)
    Issue (Month): 5 (May)
    Pages: 767-785

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    Handle: RePEc:inm:ormnsc:v:41:y:1995:i:5:p:767-785

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    Related research

    Keywords: product line design; product line selection; conjoint analysis; product profile; heuristics; beam search;

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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. 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.
    3. Nils Boysen & Armin Scholl, 2008. "A general solution framework for component commonality problems," Jena Research Papers in Business and Economics - Working and Discussion Papers 12/2008, Friedrich-Schiller-University Jena, School of Economics and Business Administration.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

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