回顾和分析现有的排课方法

G. Kazbekova, А. Nurtaza
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引用次数: 0

摘要

在组织教育机构教育过程的质量和有效地利用科学和教学潜力方面,最重要的步骤之一是制定高质量的课程表。一个定性设计的课程表应该保证学生群体和教学人员的均匀负荷。大学课程规划问题(UCTP)是大学课程规划的具体问题,同时使用学生、教师和教室等资源。这些问题被认为是非多项式时间(NP)和组合优化(COP)问题,这意味着它们可以通过优化算法来求解以获得所需的表。高校已经采用了多种方法来解决表格问题,其中大多数都采用了优化方法。本文讨论了解决大学课表问题的六种方法:顺序方法、聚类方法、基于约束的方法、元启发式方法、广义搜索方法、混合进化算法、多准则方法、基于案例的推理技术、超启发式方法、自适应方法。本文的目的是对解决高校调度问题的优化方法进行全面综述。此外,在解决这类问题时,给出了公认的元启发式优化算法。此外,还开发了通过指定经常使用的控制数据集来分析和测量解决方案性能的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Review and analysis of existing methods of scheduling classes
One of the most important steps in organizing the quality of the educational process in educational institutions and the effectiveness of the use of scientific and pedagogical potential is the task of forming a high-quality lesson schedule. A qualitatively designed lesson schedule should ensure the uniform loading of student groups and teaching staff. The problem of university course planning (UCTP) is a specific problem of course planning at a university, with simultaneous use of resources such as students, faculty, and classrooms. These problems are considered non-polynomial time (NP) and Combinatorial Optimization (COP) problems, which means that they can be solved by optimization algorithms to obtain the desired table. Several methods have been used in universities to solve table problems, and most of them use optimization methods. This article discusses six methods for solving problems with the schedule of classes in universities: Sequential methods, сluster methods, сonstraint-based methods, meta-heuristic methods, generalized search, hybrid evolutionary algorithms, multi-criteria approaches, case-based reasoning techniques, hyper-heuristics, adaptive approaches. The purpose of this article is a comprehensive review of optimization approaches to solving the problems of scheduling in universities. In addition, when solving such problems, the accepted algorithms for Meta-heuristic optimization are shown. In addition, methods have been developed to analyze and measure the performance of a solution by specifying frequently used control data sets.
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