Improving the Movie Showtime Scheduling Problem by Integrated Artificial Intelligence Techniques

Conference proceedings article

Authors/Editors

JUMPOL POLVICHAI

Strategic Research Themes

Machine Learning (Big Data Analytics)

Publication Details

Author list: Lawitsanon, Paknarat; Hanthanunchai, Kamonporn; Chanachanchai, Nattanan; Mahanin, Sitthisak;
Polvichai, Jumpol;

Publication year: 2022

ISBN: 9781665485104

ISSN: 16742370

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136197851&doi=10.1109%2fJCSSE54890.2022.9836305&partnerID=40&md5=b983ee6e4a2599707cddddab7df741ab

Languages: English-Great Britain (EN-GB)

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Abstract

This paper describes a development of an artificial intelligence system for efficiently scheduling movie showtimes. The strategy was to get the maximum amount of visitors by applying any appropriate artificial intelligence techniques to the problem of showtime schedule. The system consists of three key parts which are the movie showtime scheduling system, the predictive model of the total amount of visitors of each movie on selected days, and the web application. In this paper, five different branches of movie theaters were selected for examining the system. The total movie slots were calculated by the models and utilized to be used in the scheduling process following the criteria defined from exploratory data analysis (EDA). According to experiments, the final integrated system was evaluated with many appropriate test scenarios. The average number of visitors by the artificial intelligence system was greater than the average visitors normally reported by the movie theater company. Consequently, the developed system was showing ability to help the company increase the income and also decrease the staff's burden tasks. In addition, any mistakes caused by human errors were expected to alleviate as well. © 2022 IEEE.

Keywords

Movie Scheduling, Scheduling Problems