Solving the Optimal Selection of Wellness Tourist Attractions and Destinations in the GMS Using the AMIS Algorithm

Abstract

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.

Multi-Objective Optimization of Gateway Location Selection in Long-Range Wide Area Networks: A Tradeoff Analysis between System Costs and Bitrate Maximization

“…Within our methodology, we incorporate four improvement boxes, namely DE-inspired move, random transit, best transit, and inter-transit, as suggested by reputable sources [39][40][41][42]. These improvement boxes are executed using Equations ( 12)- (15).…”

“…During each iteration, X ijt represents the value of element j in track i, where r and n denote different elements from the set of tracks (1 to E) that are not equal to i. Q ij is a randomly generated number for position j in track i, and R ijt is a randomly generated number for element j in track i during iteration t, ranging from 0 to 1. The crossover rate (CR) is set at 0.8, following recommendations from reputable sources [42]. While our M-VaNSAS work builds upon the work in [39], we enhance it by incorporating an additional black box that employs DE mutation equations within the track improvement process, thereby introducing DE properties into the algorithm.…”

An Innovative Mobile Application for Wellness Tourism Destination Competitiveness Assessment: The Research and Development Approach

“…Previous research focused on studying the demands of wellness tourism, including tourist trends, behaviors, needs, experiences, and satisfaction [3][4][5][6][7][8]. Wellness tourism research mainly focuses on studying management models of tourist attractions and the development of routes or goods to support wellness tourists [9,10], as well as how to improve the quality of wellness tourism activities at business and community levels in a specific area [11][12][13][14][15]. Scant research has been published on creating and developing wellness tourism destination competitiveness holistically, even though wellness tourism generates significant revenue for the country.…”