三种进化算法的比较,用于主题公园与多类型设施的团体安排
Yi-Chih Hsieh1, Peng-Sheng You2
1Department of Industrial Management, National Formosa University, Yunlin, Taiwan.
Science progress
|October 15, 2024
概括
这项研究为学生团体引入了一个新的主题公园问题,优化设施访问以减少等待时间. 开发了算法来管理群体偏好,并改善学生和经营者的整体主题公园体验.
科学领域:
- 运营研究 运营研究
- 计算智能是一种计算智能.
- 旅游管理 旅游管理
背景情况:
- 主题公园面临着长排队和高峰季节游客满意度下降的挑战,特别是对于学校团体来说.
- 学生团体同时进入和退出要求加剧了受欢迎景点的等待时间.
- 目前的系统无法充分满足不同设施偏好的学生群体的特定需求.
研究的目的:
- 为学生团体量身定制,提出一个新的主题公园问题与多种设施 (TPP-MTF).
- 开发一个优化的系统来分配组到设施,最大限度地减少等待时间和最大限度地满足.
- 通过有效的资源配置,为主题公园和学生团体创造一个双赢的场景.
主要方法:
- 开发了一种用于随机排列整数序列的新解码方法.
- 解码方法被整合到基于免疫的算法,遗传算法和粒子群优化中.
- 这些算法被应用到台湾主题公园的现实实例研究中.
主要成果:
- 提出的算法有效地解决了TPP-MTF问题,显示了小组等待时间的显著减少.
- 对比分析显示了基于免疫,遗传和粒子群优化算法的有效性.
- 开发的系统有助于提前销售门票,并对主题公园的游客数量进行估计.
结论:
- TPP-MTF模型和拟议的算法为主题公园管理大型学生团体提供了可行的解决方案.
- 优化日程安排提高了游客满意度和公园运营效率.
- 这种方法为改善主题公园管理和游客体验提供了一个框架,特别是在教育旅游方面.
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