Optimizing Music Station Playlists on Broadcast Radio

José Antonio Carbajal (), Juan Ma (), Nannan Chen () and Mario Aboytes-Ojeda ()
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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

Interfaces, 2025, vol. 55, issue 5, 399-411

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.

Keywords: media industry; radio broadcasting; music playlist; integer programming; multicriteria optimization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:55:y:2025:i:5:p:399-411

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