{"title":"Scheduling TV advertisements via genetic algorithm","authors":"Kateryna Czerniachowska","doi":"10.1504/EJIE.2019.097926","DOIUrl":null,"url":null,"abstract":"Television advertising is vital to the television industry and is one of the most popular ways for advertisers to increase sales. This paper discusses the problem of scheduling TV advertisements according to each advertisers' need and budget limitations, with the objective of maximising total viewership. The proposed solution is the genetic algorithm, and its efficiency has been evaluated using list and random-list algorithms during long (one month) and short (one week) advertising campaign periods. Computational results show that this algorithm can obtain satisfactory results for real-world test problems, based on data from a marketing research company. [Received: 30 November 2017; Revised: 14 April 2018; Revised: 15 September 2018; Revised: 20 September 2018; Accepted: 22 September 2018]","PeriodicalId":314867,"journal":{"name":"European J. of Industrial Engineering","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European J. of Industrial Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/EJIE.2019.097926","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Television advertising is vital to the television industry and is one of the most popular ways for advertisers to increase sales. This paper discusses the problem of scheduling TV advertisements according to each advertisers' need and budget limitations, with the objective of maximising total viewership. The proposed solution is the genetic algorithm, and its efficiency has been evaluated using list and random-list algorithms during long (one month) and short (one week) advertising campaign periods. Computational results show that this algorithm can obtain satisfactory results for real-world test problems, based on data from a marketing research company. [Received: 30 November 2017; Revised: 14 April 2018; Revised: 15 September 2018; Revised: 20 September 2018; Accepted: 22 September 2018]