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Optimizing Music Station Playlists on Broadcast Radio

Author

Listed:
  • José Antonio Carbajal

    (Princeton Consultants, Inc., Princeton, New Jersey 08540)

  • Juan Ma

    (iHeartMedia, Inc., San Antonio, Texas 78258)

  • Nannan Chen

    (Hungryroot, Inc., New York, New York 10010)

  • Mario Aboytes-Ojeda

    (Verde Studio Engineering, San Antonio, Texas 78212)

Abstract

We developed a mathematical optimization–based engine that generates 24/7 music playlists for radio stations subject to strategic scheduling goals and key business rules. Utilizing song metadata, such as tempo and mood; latest song research results produced by machine learning models; and radio listenership data, the engine produces music playlists that optimize strength and diversity simultaneously. The engine has been successfully deployed to production and has shown its power in efficiently and effectively creating customized music playlists for various radio stations.

Suggested Citation

  • José Antonio Carbajal & Juan Ma & Nannan Chen & Mario Aboytes-Ojeda, 2025. "Optimizing Music Station Playlists on Broadcast Radio," Interfaces, INFORMS, vol. 55(5), pages 399-411, September.
    • Handle: RePEc:inm:orinte:v:55:y:2025:i:5:p:399-411
    DOI: 10.1287/inte.2025.0248
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