Scheduling Advertising on Cable Television

• 成果类型: Article
• 署名作者: Souyris, Sebastian; Seshadri, Sridhar; Subramanian, Sriram
• 署名单位: Rensselaer Polytechnic Institute; University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.2430
• 发表日期: 2023
• 关键词: Optimization

摘要

Advertisement scheduling is a daily essential operational process in the television business. Efficient distribution of viewers among advertisers allows the television network to satisfy contracts and increase ad sale revenues. Ad scheduling is a challenging multiperiod, mixed-integer programming problem in which the network must create schedules to meet advertisers' campaign goals and maximize ad revenues. Each campaign must meet a specific target group of viewers and a unique set of constraints. Moreover, the number of viewers is uncertain. To solve this problem, we develop and implement a practical approach that combines mathematical programming and machine learning to create daily schedules. These schedules are of high quality according to standard business metrics and the small integer programming gap. Leading networks in the United States and India using our methods experience a 3%-5% revenue increase.