figshare
Browse

JSP_Dataset (Underlying Data).xlsx

Version 3 2024-08-29, 12:04
Version 2 2024-07-24, 07:07
Version 1 2023-11-09, 12:20
dataset
posted on 2024-08-29, 12:04 authored by Suangpang pannneeSuangpang pannnee, Pitchaya Jamjuntr

Smart tourism destinations face challenges in efficiently scheduling services to meet the diverse needs of tourists while ensuring sustainability and resource optimization. This study explored the use of genetic algorithms (GAs) to optimize service scheduling and improve efficiency and customer satisfaction compared to traditional methods. Thematic analysis of collected data showed that GAs offer superior efficiency, increased customer satisfaction, and potential for enhanced tourist experiences and resource optimization. GAs could also adapt to changing circumstances and reoptimize schedules in real time. Further research and development in the use of GAs for service scheduling in tourism is recommended, including exploration of different types of tourism services and incorporation of real-time data for a competitive advantage and substantial value to the industry.

Funding

Funding for this research was provided by Suan Dusit University under the Ministry of Higher Education, Science, Research and Innovation, Thailand, grant number FF66-4-006. The grant was focused on the "Innovative of gastronomy and Agrotourism tourism platform of Suphanburi using identity and culture integrated with the expertise of the university to drive the economic foundation to support next normal in post Covid-19."

History

Usage metrics

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC