Browsing by Author "Mamay K."

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    OPTIMIZING EXAM SCHEDULES: A LITERATURE SURVEY
    (СДУ хабаршысы - 2023, 2023) Mamay K.
    Abstract. Exam scheduling is a challenging problem that involves finding an optimal schedule for a set of exams subject to various constraints, such as room capacities, time windows, and conflicting exams. This problem has been extensively studied in operations research and computer science and various methods have been proposed over the years. In this paper, we present a comprehensive literature review of the existing methods for exam scheduling optimization. We summarize the key contributions, strengths, and weaknesses of each approach and compare and contrast the various methods based on various performance measures such as solution quality, computational time, and scalability. Additionally, we discuss the real-world applications of the exam scheduling problem and the impact of different methods on practical solutions. Finally, we provide insights into the current state of the art in exam scheduling and suggest future directions for research. Our survey provides a useful resource for researchers, practitioners, and decision-makers who are interested in exam scheduling optimization.
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    Solving the exam scheduling problems with Genetic Algorithms
    (2023) Mamay K.
    The exam scheduling problem is a complex task faced by educational institutions worldwide. Efficiently allocating exams within limited time slots while considering various constraints, such as student preferences, room capacities, and faculty availability, poses a significant challenge. This dissertation aims to address the exam scheduling problem by leveraging the power of Genetic Algorithms (GAs). Genetic Algorithms are robust search and optimization techniques inspired by the process of natural selection. By employing evolutionary principles, GAs have proven to be effective in finding optimal or near-optimal solutions for a wide range of combinatorial optimization problems. In this study, Ipresent a new application of genetic algorithms to solve the exam scheduling problem, to devise a solution that can be applied to Kazakhstan universities, and to evaluate the performance of GA compared to other existing algorithms commonly used in this field.