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Scheduling Commercial Videotapes in Broadcast Television

Author

Listed:
  • Srinivas Bollapragada

    (GE Global Research Center, 1 Research Circle, Schenectady, New York 12309)

  • Michael R. Bussieck

    (GAMS Development Corporation, 1217 Potomac Street NW, Washington, DC 20007)

  • Suman Mallik

    (Department of Business Administration, University of Illinois, 350 Wohlers Hall, 1206 South Sixth Street, Champaign, Illinois 61820)

Abstract

This paper, motivated by the experiences of a major U.S.-based broadcast television network, presents algorithms and heuristics to schedule commercial videotapes. Major advertisers purchase several slots to air commercials during a given time period on a broadcast network. We study the problem of scheduling advertiser's commercials in the slots it purchased when the same commercial is to be aired multiple times. Under such a situation, the advertisers typically want the airings of a commercial to be as evenly spaced as possible. Thus, our objective is to schedule a set of commercials in a set of available slots such that multiple airings of the same commercial are as evenly spaced as possible. A natural formulation of this problem is a mixed-integer program that can be solved using third-party solvers. We also develop a branch-and-bound algorithm based on a problem-specific bounding scheme. Both approaches fail to solve larger problem instances within a reasonable time frame. We present an alternative mixed-integer program that lends itself to an efficient solution. For solving even larger problems, we present multiple heuristics.

Suggested Citation

  • Srinivas Bollapragada & Michael R. Bussieck & Suman Mallik, 2004. "Scheduling Commercial Videotapes in Broadcast Television," Operations Research, INFORMS, vol. 52(5), pages 679-689, October.
  • Handle: RePEc:inm:oropre:v:52:y:2004:i:5:p:679-689
    DOI: 10.1287/opre.1040.0119
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    References listed on IDEAS

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    Cited by:

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    3. Shi, Yang & Zhao, Ying, 2019. "Modeling Advertisers' Willingness to Pay in TV Commercial Slot Auctions," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 120-133.
    4. Giovanni Giallombardo & Giovanna Miglionico & Houyuan Jiang, 2015. "New Reformulations for the Conflict Resolution Problem in the Scheduling of Television Commercials," Working Papers 2015/03, Cambridge Judge Business School, University of Cambridge.
    5. M J Brusco, 2008. "Scheduling advertising slots for television," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(10), pages 1363-1372, October.
    6. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    7. Albert Corominas & Alberto García-Villoria & Rafael Pastor, 2013. "Metaheuristic algorithms hybridised with variable neighbourhood search for solving the response time variability problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 296-312, July.
    8. Daya Ram Gaur & Ramesh Krishnamurti & Rajeev Kohli, 2009. "Conflict Resolution in the Scheduling of Television Commercials," Operations Research, INFORMS, vol. 57(5), pages 1098-1105, October.
    9. Srinivas Bollapragada & Marc Garbiras, 2004. "Scheduling Commercials on Broadcast Television," Operations Research, INFORMS, vol. 52(3), pages 337-345, June.
    10. Corominas, Albert & Kubiak, Wieslaw & Pastor, Rafael, 2010. "Mathematical programming modeling of the Response Time Variability Problem," European Journal of Operational Research, Elsevier, vol. 200(2), pages 347-357, January.
    11. García-Villoria, Alberto & Pastor, Rafael, 2010. "Solving the response time variability problem by means of a genetic algorithm," European Journal of Operational Research, Elsevier, vol. 202(2), pages 320-327, April.
    12. Sylvia Hristakeva & Julie Holland Mortimer, 2023. "Price Dispersion and Legacy Discounts in the National Television Advertising Market," Marketing Science, INFORMS, vol. 42(6), pages 1162-1183, November.
    13. Rakesh R. Mallipeddi & Subodha Kumar & Chelliah Sriskandarajah & Yunxia Zhu, 2022. "A Framework for Analyzing Influencer Marketing in Social Networks: Selection and Scheduling of Influencers," Management Science, INFORMS, vol. 68(1), pages 75-104, January.
    14. Guohua Wan & Xiangtong Qi, 2010. "Scheduling with variable time slot costs," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(2), pages 159-171, March.
    15. Giovanni Giallombardo & Houyuan Jiang & Giovanna Miglionico, 2016. "New Formulations for the Conflict Resolution Problem in the Scheduling of Television Commercials," Operations Research, INFORMS, vol. 64(4), pages 838-848, August.
    16. José Antonio Carbajal & Peter Williams & Andreea Popescu & Wes Chaar, 2019. "Turner Blazes a Trail for Audience Targeting on Television with Operations Research and Advanced Analytics," Interfaces, INFORMS, vol. 49(1), pages 64-89, January.
    17. Bert De Reyck & Ioannis Fragkos & Yael Grushka-Cockayne & Casey Lichtendahl & Hammond Guerin & Andrew Kritzer, 2017. "Vungle Inc. Improves Monetization Using Big Data Analytics," Interfaces, INFORMS, vol. 47(5), pages 454-466, October.
    18. García-Villoria, Alberto & Salhi, Said & Corominas, Albert & Pastor, Rafael, 2011. "Hyper-heuristic approaches for the response time variability problem," European Journal of Operational Research, Elsevier, vol. 211(1), pages 160-169, May.
    19. Dana G. Popescu & Pascale Crama, 2016. "Ad Revenue Optimization in Live Broadcasting," Management Science, INFORMS, vol. 62(4), pages 1145-1164, April.
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    21. José Antonio Carbajal & Wes Chaar, 2017. "Turner Optimizes the Allocation of Audience Deficiency Units," Interfaces, INFORMS, vol. 47(6), pages 518-536, December.

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