Examination timetable is a non-trivial task for administrators of the academic institutions repeated every semester. In terms of optimization, examination timetabling is a combinatorial optimization problem concerned with assigning a set of exams to a predefined number of timeslots and rooms with accordance to a given constraints. In this paper, the extended version of hill climbing algorithm called \(\beta \)-hill climbing is utilized to tackle the examination timetabling problem. \(\beta \)-hill climbing is a new local search-based method that has two operators (\(\beta \)-operator and \({\mathcal {N}}\)-operator) to iterate towards the optimal solution. The saturation degree heuristic method is utilized in the improvement loop of \(\beta \)-hill climbing to ensure the solution feasibility. For experimental evaluation, Carter dataset is used comprising 12 instances selected from several real-world universities. Eight convergence scenarios are designed to sensitively analyze the behavior of the proposed algorithm. For comparative evaluations, the results produced by \(\beta \)-hill climbing are comparatively comparable with previous methods that utilized the same Carter instances.

对于每学期重复一次的学术机构管理人员而言，考试时间表是一项艰巨的任务。在优化方面，考试时间表是一个组合优化问题，涉及根据给定的约束将一组检查分配给预定数量的时间段和房间。本文采用称为\（\ beta \） -hill爬山的扩展版本来解决考试时间表问题。\（\ beta \）- hill爬山是一种新的基于本地搜索的方法，它具有两个运算符（\（\ beta \）- operator和\（{{mathcal {N}} \）- operator）以迭代到最优值解。饱和度启发式方法在改进的循环中被利用。\（\ beta \） -爬山以确保解决方案的可行性。为了进行实验评估，使用了Carter数据集，其中包括从数个现实世界大学中选择的12个实例。设计了八个收敛方案来敏感地分析所提出算法的行为。为了进行比较评估，\（\ beta \）爬山产生的结果与使用相同Carter实例的先前方法具有可比性。