在流行病期间,模拟化与基于图表的搜索对封闭区域的社会距离问题进行了模拟化
1Department of Industrial Engineering, School of Engineering, Architecture, and Design, Bartin University, Bartin, Turkey.
PloS one
|February 11, 2025
概括
这项研究引入了一个新的问题,即最大多样性社会距离问题 (MDPs),以优化学生的座位安排以最大限度地保持距离. 一个模拟回火算法 (SA-MDPs) 有效地为教育社会距离找到最佳或近最佳的解决方案.
科学领域:
- 运营研究 运营研究
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 在疫情期间,包括教育环境中的社交距离在内的预防性政策至关重要.
- 优化学生的座位以最大限度地保持身体距离是一个复杂的挑战.
研究的目的:
- 引入并解决最大多样性社会距离问题 (MDPs).
- 开发一个高效的算法,最大限度地提高学生之间的距离,同时遵守社会距离的限制.
主要方法:
- 制定了类似于最大多样性问题 (MDP) 的问题.
- 开发了一个模拟回火算法框架 (SA-MDPs) 以获得最佳或接近最佳的解决方案.
- 实现了一个贪的基于随机的算法,用于初始可行的解决方案和基于图形理论的邻里搜索.
主要成果:
- 与商业解决方案相比,SA-MDPs算法始终发现最佳或优越的解决方案.
- 该算法在各种课堂大小的合理计算时间内取得了这些结果.
- 邻里搜索有效地根据社会距离确定了主导的,主导的和非主导的座位.
结论:
- 拟议的SA-MDPs是解决最大多样性社会距离问题的有效方法.
- 这种方法提供了一个计算效率高的方法来优化学生的座位,以提高教育环境中的安全性.
- 该算法为实施社交距离措施的机构提供了实用解决方案.
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