调度(生产过程)
收入
计算机科学
整数规划
运筹学
在线广告
广告
数学优化
业务
工程类
互联网
算法
数学
万维网
会计
作者
Sebastián Souyris,Sridhar Seshadri,S. Sriram
出处
期刊:Operations Research
[Institute for Operations Research and the Management Sciences]
日期:2023-01-17
卷期号:71 (6): 2217-2231
被引量:1
标识
DOI:10.1287/opre.2022.2430
摘要
Scheduling Advertising on Cable Television 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, S. Souyris, S. Seshadri, and S. Subramanian develop and implement a practical approach that combines mathematical programming and machine learning to create daily schedules. According to standard business metrics and the small integer programming gap, these schedules are of high quality. Using their methods, leading networks in the United States and India experience a 3% to 5% revenue increase, which translates to about $60 million annually for one prominent user.
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