Solving the Optimal Selection of Wellness Tourist Attractions and Destinations in the GMS Using the AMIS Algorithm
This study aims to select the ideal mixture of small and medium-sized destinations and attractions in Thailand’s Ubon Ratchathani Province in order to find potential wellness destinations and attractions. In the study region, 46 attractions and destinations were developed as the service sectors for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/10/9/165 |
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| author | Rapeepan Pitakaso Natthapong Nanthasamroeng Sairoong Dinkoksung Kantimarn Chindaprasert Worapot Sirirak Thanatkij Srichok Surajet Khonjun Sarinya Sirisan Ganokgarn Jirasirilerd Chaiya Chomchalao |
| author_facet | Rapeepan Pitakaso Natthapong Nanthasamroeng Sairoong Dinkoksung Kantimarn Chindaprasert Worapot Sirirak Thanatkij Srichok Surajet Khonjun Sarinya Sirisan Ganokgarn Jirasirilerd Chaiya Chomchalao |
| author_sort | Rapeepan Pitakaso |
| collection | DOAJ |
| description | This study aims to select the ideal mixture of small and medium-sized destinations and attractions in Thailand’s Ubon Ratchathani Province in order to find potential wellness destinations and attractions. In the study region, 46 attractions and destinations were developed as the service sectors for wellness tourism using the designed wellness framework and the quality level of the attractions and destinations available on social media. Distinct types of tourists, each with a different age and gender, comprise a single wellness tourist group. Due to them, even with identical attractions and sites, every traveler has a different preference. A difficult task for travel agencies is putting together combinations of attractions and places for each tourist group. In this paper, the mathematical formulation of the suggested problem is described, and the optimal solution is achieved using Lingo v.16. Unfortunately, the large size of test instances cannot be solved with Lingo v16. However, the large-scale problem, particularly the case study in the target area, has been solved using a metaheuristic method called AMIS. According to the computation in the final experiment, AMIS can raise the solution quality across all test instances by an average of 3.83 to 8.17 percent. Therefore, it can be concluded that AMIS outperformed all other strategies in discovering the ideal solution. AMIS, GA and DE may lead visitors to attractions that generate 29.76%, 29.58% and 32.20%, respectively, more revenue than they do now while keeping the same degree of preference when the number of visitors doubles. The attractions’ and destinations’ utilization has increased by 175.2 percent over the current situation. This suggests that small and medium-sized enterprises have a significantly higher chance of flourishing in the market. |
| first_indexed | 2024-03-10T00:22:06Z |
| format | Article |
| id | doaj.art-b78b810caac8439c846ce2a8f3bf21d1 |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-10T00:22:06Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-b78b810caac8439c846ce2a8f3bf21d12023-11-23T15:41:45ZengMDPI AGComputation2079-31972022-09-0110916510.3390/computation10090165Solving the Optimal Selection of Wellness Tourist Attractions and Destinations in the GMS Using the AMIS AlgorithmRapeepan Pitakaso0Natthapong Nanthasamroeng1Sairoong Dinkoksung2Kantimarn Chindaprasert3Worapot Sirirak4Thanatkij Srichok5Surajet Khonjun6Sarinya Sirisan7Ganokgarn Jirasirilerd8Chaiya Chomchalao9Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, ThailandArtificial Intelligence Optimization SMART Laboratory, Engineering Technology Department, Faculty of Industrial Technology, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, ThailandUbon Ratchathani Business School, Ubon Ratchathani University, Ubon Ratchathani 34190, ThailandFaculty of Tourism and Hotel Management, Mahasarakham University, Maha Sarakham 44000, ThailandDepartment of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Lanna Chiang Rai, Chiang Rai 57120, ThailandArtificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, ThailandArtificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, ThailandDepartment of Industrial Management Technology, Faculty of Liberal Arts and Sciences, Sisaket Rajabhat University, Sisaket 33000, ThailandDepartment of Industrial Management Technology, Faculty of Liberal Arts and Sciences, Sisaket Rajabhat University, Sisaket 33000, ThailandDepartment of Industrial Technology, Faculty of Industrial Technology, Nakhon Phanom University, Nakhon Phanom 48000, ThailandThis study aims to select the ideal mixture of small and medium-sized destinations and attractions in Thailand’s Ubon Ratchathani Province in order to find potential wellness destinations and attractions. In the study region, 46 attractions and destinations were developed as the service sectors for wellness tourism using the designed wellness framework and the quality level of the attractions and destinations available on social media. Distinct types of tourists, each with a different age and gender, comprise a single wellness tourist group. Due to them, even with identical attractions and sites, every traveler has a different preference. A difficult task for travel agencies is putting together combinations of attractions and places for each tourist group. In this paper, the mathematical formulation of the suggested problem is described, and the optimal solution is achieved using Lingo v.16. Unfortunately, the large size of test instances cannot be solved with Lingo v16. However, the large-scale problem, particularly the case study in the target area, has been solved using a metaheuristic method called AMIS. According to the computation in the final experiment, AMIS can raise the solution quality across all test instances by an average of 3.83 to 8.17 percent. Therefore, it can be concluded that AMIS outperformed all other strategies in discovering the ideal solution. AMIS, GA and DE may lead visitors to attractions that generate 29.76%, 29.58% and 32.20%, respectively, more revenue than they do now while keeping the same degree of preference when the number of visitors doubles. The attractions’ and destinations’ utilization has increased by 175.2 percent over the current situation. This suggests that small and medium-sized enterprises have a significantly higher chance of flourishing in the market.https://www.mdpi.com/2079-3197/10/9/165wellness tourismoptimal selection of tourist attractionsfamily wellness touristsgroup preferencesAMISmixed integer programming |
| spellingShingle | Rapeepan Pitakaso Natthapong Nanthasamroeng Sairoong Dinkoksung Kantimarn Chindaprasert Worapot Sirirak Thanatkij Srichok Surajet Khonjun Sarinya Sirisan Ganokgarn Jirasirilerd Chaiya Chomchalao Solving the Optimal Selection of Wellness Tourist Attractions and Destinations in the GMS Using the AMIS Algorithm Computation wellness tourism optimal selection of tourist attractions family wellness tourists group preferences AMIS mixed integer programming |
| title | Solving the Optimal Selection of Wellness Tourist Attractions and Destinations in the GMS Using the AMIS Algorithm |
| title_full | Solving the Optimal Selection of Wellness Tourist Attractions and Destinations in the GMS Using the AMIS Algorithm |
| title_fullStr | Solving the Optimal Selection of Wellness Tourist Attractions and Destinations in the GMS Using the AMIS Algorithm |
| title_full_unstemmed | Solving the Optimal Selection of Wellness Tourist Attractions and Destinations in the GMS Using the AMIS Algorithm |
| title_short | Solving the Optimal Selection of Wellness Tourist Attractions and Destinations in the GMS Using the AMIS Algorithm |
| title_sort | solving the optimal selection of wellness tourist attractions and destinations in the gms using the amis algorithm |
| topic | wellness tourism optimal selection of tourist attractions family wellness tourists group preferences AMIS mixed integer programming |
| url | https://www.mdpi.com/2079-3197/10/9/165 |
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