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A Branch and Bound Algorithm for the List Selection Problem in Direct Mail Advertising

Author

Listed:
  • F. Robert Dwyer

    (University of Cincinnati)

  • James R. Evans

    (University of Cincinnati)

Abstract

This paper describes a branch and bound approach for optimizing a media selection problem, namely, to choose the best set of mailing lists to maximize audience reach. Prompted by a national retailer's interest in more effective and efficient direct mail catalogue distribution, the algorithm exploits current heuristic approaches which improve computational efficiency. A numerical example and computational experience using actual data are discussed, along with extensions to other practical situations.

Suggested Citation

  • F. Robert Dwyer & James R. Evans, 1981. "A Branch and Bound Algorithm for the List Selection Problem in Direct Mail Advertising," Management Science, INFORMS, vol. 27(6), pages 658-667, June.
    • Handle: RePEc:inm:ormnsc:v:27:y:1981:i:6:p:658-667
    DOI: 10.1287/mnsc.27.6.658
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    Citations

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    Cited by:

    1. Galvao, Roberto D. & Gonzalo Acosta Espejo, Luis & Boffey, Brian, 2000. "A comparison of Lagrangean and surrogate relaxations for the maximal covering location problem," European Journal of Operational Research, Elsevier, vol. 124(2), pages 377-389, July.
    2. Brian T. Downs & Jeffrey D. Camm, 1996. "An exact algorithm for the maximal covering problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(3), pages 435-461, April.
    3. Alan T. Murray, 2016. "Maximal Coverage Location Problem," International Regional Science Review, , vol. 39(1), pages 5-27, January.
    4. T Bhaskar & R Sundararajan & P G Krishnan, 2009. "A fuzzy mathematical programming approach for cross-sell optimization in retail banking," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 717-727, May.
    5. R Farzipoor Saen, 2011. "Media selection in the presence of flexible factors and imprecise data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1695-1703, September.
    6. Bitran, Gabriel R. & Mondschein, Susana V., 1993. "Mailing catalogs : an optimization approach," Working papers 3545-93., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    7. Ramasubramanian Sundararajan & Tarun Bhaskar & Abhinanda Sarkar & Sridhar Dasaratha & Debasis Bal & Jayanth K. Marasanapalle & Beata Zmudzka & Karolina Bak, 2011. "Marketing Optimization in Retail Banking," Interfaces, INFORMS, vol. 41(5), pages 485-505, October.
    8. Chen, Liang & Chen, Sheng-Jie & Chen, Wei-Kun & Dai, Yu-Hong & Quan, Tao & Chen, Juan, 2023. "Efficient presolving methods for solving maximal covering and partial set covering location problems," European Journal of Operational Research, Elsevier, vol. 311(1), pages 73-87.
    9. Jeffrey D. Camm & Susan K. Norman & Stephen Polasky & Andrew R. Solow, 2002. "Nature Reserve Site Selection to Maximize Expected Species Covered," Operations Research, INFORMS, vol. 50(6), pages 946-955, December.
    10. Jeffrey D. Camm & Jeremy Christman & A. Narayanan, 2022. "Total Unduplicated Reach and Frequency Optimization at Procter & Gamble," Interfaces, INFORMS, vol. 52(2), pages 149-157, March.
    11. James J. Cochran & Martin S. Levy & Jeffrey D. Camm, 2010. "Bayesian coverage optimization models," Journal of Combinatorial Optimization, Springer, vol. 19(2), pages 158-173, February.
    12. Daoqin Tong & Alan T. Murray, 2009. "Maximising coverage of spatial demand for service," Papers in Regional Science, Wiley Blackwell, vol. 88(1), pages 85-97, March.

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