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SPOT: Scheduling Programs Optimally for Television

Author

Listed:
  • Srinivas K. Reddy

    (Terry College of Business, Brooks Hall, University of Georgia, Athens, Georgia 30602)

  • Jay E. Aronson

    (Terry College of Business, Brooks Hall, University of Georgia, Athens, Georgia 30602)

  • Antonie Stam

    (Terry College of Business, Brooks Hall, University of Georgia, Athens, Georgia 30602)

Abstract

This paper introduces SPOT (Scheduling Programs Optimally for Television), an analytical model for optimal prime-time TV program scheduling. Due in part to the advent of new cable TV channels, the competition for viewer ratings has intensified substantially in recent years, and the revenues of the major networks have not kept pace with the costs of the programs. As profit margins decrease, the networks seek to improve their viewer ratings with innovative scheduling strategies. Our SPOT models for scheduling network programs combine predicted ratings for different combinations of prime-time schedules with a novel, mixed-integer, generalized network-based flow, mathematical programming model, which when solved provides an optimal schedule. In addition to historical performance, subjective inputs from actual network managers were used as input to the network flow optimization model. The optimization model is flexible. It can utilize the managers' input and maximize profit (instead of ratings) by considering not only the revenue potential but also the costs of the shows. Moreover, SPOT can describe the scheduling problem over any time period (e.g., day, week, month, season), and designate certain shows to, and restrict them from, given time slots. The methodology of SPOT is illustrated using data for the first quarter of 1990, obtained from a cable network. The optimization model produces solutions that would have generated an increase of approximately 2% in overall profitability, representing over $6 million annually for the cable network. SPOT not only produces more profitable TV schedules for this network, but also provides valuable general insights into the development of mixed programming strategies for improving future schedules.

Suggested Citation

  • Srinivas K. Reddy & Jay E. Aronson & Antonie Stam, 1998. "SPOT: Scheduling Programs Optimally for Television," Management Science, INFORMS, vol. 44(1), pages 83-102, January.
    • Handle: RePEc:inm:ormnsc:v:44:y:1998:i:1:p:83-102
    DOI: 10.1287/mnsc.44.1.83
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    References listed on IDEAS

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    2. Eliashberg, Jehoshua & Hegie, Quintus & Ho, Jason & Huisman, Dennis & Miller, Steven J. & Swami, Sanjeev & Weinberg, Charles B. & Wierenga, Berend, 2009. "Demand-driven scheduling of movies in a multiplex," International Journal of Research in Marketing, Elsevier, vol. 26(2), pages 75-88.
    3. Alexander Dhoest & Nele Simons, 2016. "Still ‘Watching’ TV? The Consumption of TV Fiction by Engaged Audiences," Media and Communication, Cogitatio Press, vol. 4(3), pages 176-184.
    4. 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.
    5. 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.
    6. Srinivas Bollapragada & Marc Garbiras, 2004. "Scheduling Commercials on Broadcast Television," Operations Research, INFORMS, vol. 52(3), pages 337-345, June.
    7. I. Robert Chiang & Jhih‐Hua Jhang‐Li, 2020. "Competition through Exclusivity in Digital Content Distribution," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1270-1286, May.
    8. Srinivas Bollapragada & Michael R. Bussieck & Suman Mallik, 2004. "Scheduling Commercial Videotapes in Broadcast Television," Operations Research, INFORMS, vol. 52(5), pages 679-689, October.
    9. Alexander Dhoest & Nele Simons, 2016. "Still ‘Watching’ TV? The Consumption of TV Fiction by Engaged Audiences," Media and Communication, Cogitatio Press, vol. 4(3), pages 176-184.
    10. Elsie Sterbin Gottlieb, 2002. "Solving generalized transportation problems via pure transportation problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 49(7), pages 666-685, October.
    11. Bragge, Johanna, 2001. "Premediation analysis of the energy taxation dispute in Finland," European Journal of Operational Research, Elsevier, vol. 132(1), pages 1-16, July.
    12. Sanjeev Swami & Martin L. Puterman & Charles B. Weinberg, 2001. "Play It Again, Sam? Optimal Replacement Policies for a Motion Picture Exhibitor," Manufacturing & Service Operations Management, INFORMS, vol. 3(4), pages 369-386, July.
    13. Denny Meyer & Rob J. Hyndman, 2005. "Rating Forecasts for Television Programs," Monash Econometrics and Business Statistics Working Papers 1/05, Monash University, Department of Econometrics and Business Statistics.
    14. Danaher, Peter J. & Dagger, Tracey S. & Smith, Michael S., 2011. "Forecasting television ratings," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1215-1240, October.
    15. Gaurav Sabnis & Rajdeep Grewal, 2015. "Cable News Wars on the Internet: Competition and User-Generated Content," Information Systems Research, INFORMS, vol. 26(2), pages 301-319, June.
    16. Bollapragada, Srinivas & Bussieck, Michael & Mallik, Suman, 2002. "Scheduling Commercial Videotapes in Broadcast Television," Working Papers 02-0127, University of Illinois at Urbana-Champaign, College of Business.
    17. 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.
    18. 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.
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    20. 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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