APLIKASI ALGORITMA GENETIKA DALAM PENJADWALAN MATA KULIAH

Making a class schedule becomes problem and takes a long time because of several obstacles such as the lack of lecture rooms, the lack of teaching staff, and the high of courses available in one semester. This study aims to apply genetic algorithms in making class schedules to facilitate the process of making class schedules. The method used is the waterfall method with the stages of the Software Development Life Cycle. The results of the application of genetics application show that the process of making course schedules can overcome the constraints of 1) space and time clashes, 2) lecturer conflicts, 3) Friday prayer times clashing, 4) there is a time when the lecturer wants for certain reasons, and 5) practicum in the laboratory room. By passing these constraints, the application of genetic algorithms in course scheduling is categorized as effective. Based on the results of running on 51 lecturers (51 chromosomes), the average running time 30 times in a row is 25.86 minutes so that the use of genetic algorithm applications in scheduling courses is efficient.

Course Scheduling Under Sudden Scarcity: Applications to Pandemic Planning

Problem definition: Physical distancing requirements during the COVID-19 pandemic have dramatically reduced the effective capacity of university campuses. Under these conditions, we examine how to make the most of newly scarce resources in the related problems of curriculum planning and course timetabling. Academic/practical relevance: We propose a unified model for university course scheduling problems under a two-stage framework and draw parallels between component problems while showing how to accommodate individual specifics. During the pandemic, our models were critical to measuring the impact of several innovative proposals, including expanding the academic calendar, teaching across multiple rooms, and rotating student attendance through the week and school year. Methodology: We use integer optimization combined with enrollment data from thousands of past students. Our models scale to thousands of individual students enrolled in hundreds of courses. Results: We projected that if Massachusetts Institute of Technology moved from its usual two-semester calendar to a three-semester calendar, with each student attending two semesters in person, more than 90% of student course demand could be satisfied on campus without increasing faculty workloads. For the Sloan School of Management, we produced a new schedule that was implemented in fall 2020. The schedule allowed half of Sloan courses to include an in-person component while adhering to safety guidelines. Despite a fourfold reduction in classroom capacity, it afforded two thirds of Sloan students the opportunity for in-person learning in at least half their courses. Managerial implications: Integer optimization can enable decision making at a large scale in a domain that is usually managed manually by university administrators. Our models, although inspired by the pandemic, are generic and could apply to any scheduling problem under severe capacity constraints.

Computer Experiment Management in Virtual Environment

With the development of the era of big data and the continuous updating of computer technology, the traditional teaching is not satisfied with the current diversified educational development concept, especially for the computer operation class, computer operation has become the mainstream of the current computer experiment class.With the increasing number of computer experiments and the extensive use of computers in open laboratories, how to manage computer experiments reasonably has become an urgent problem. The function of the computer is powerful, and students have different habits of using the computer, and the entertainment function of the computer is also loved by many students. Therefore, in the process of experiment, students often play games and watch movies, which is not easy to manage. In order to solve these problems, this paper studies the computer experiment management system under the virtual environment to help teachers manage the experiment process. This paper studies the function modules of the computer experiment management system, explains the key elements of the analysis of the implementation of the experiment management system, and explains the construction scheme of the virtual experiment teaching environment, and expounds the ant colony algorithm used in the design and management of the course scheduling system. This paper also analyzes the operation and load of computer experiment management through simulation experiment research, and tests and analyzes to prove the rationality of the management system. The experimental research shows that in the daily computer experiment management system, the most commonly used is the experimental teaching, and the conventional office, accounting for 27.35 and 26.33 respectively. The highest utilization rate of the computer experiment system is the daily teaching, accounting for 53.6%.

Automatic Course Scheduling System in Universities Based on Hybrid Genetic-Ant Colony Algorithm

Scheduling is the daily work of the Ministry of Education in Colleges and universities. In the past ten years, the scale of our colleges and universities has expanded rapidly, but the teaching resources are relatively limited. Many schools are facing the problem of insufficient classroom resources and teachers resources. The current way of organizing courses is increasingly difficult to make full use of existing resources to solve the changing needs and inefficiencies, which need to be improved urgently. This paper applies the hybrid Genetic-Ant algorithm to the automatic course scheduling system in Colleges and universities, and uses the cross-function to design and build the automatic course scheduling system in Colleges and universities. And select a college’s course scheduling system from this city for research, and use the Genetic-Ant hybrid algorithm to improve the original system to form a new system, called the original system A, and called the improved new system B, to compare the operation time and system suitability of the two systems. The results show that the fitness of system B is better than that of system A. When the scheduling unit is 100, the fitness of system A is 181, and system B is 203. When the scheduling unit is 400, the fitness of system B is 14 higher than that of system A. When the scheduling unit is 800, the fitness of system B is 64 higher than that of system A. Thus, the hybrid algorithm of genetic ant colony can improve the rationality of the curriculum.

Design of Automatic Course Arrangement System for Electronic Engineering Teaching Based on Monte Carlo Genetic Algorithm

In order to overcome the problems of convergence and low satisfaction in the traditional course scheduling system, a new Electronic Engineering Teaching Automatic Course Scheduling System based on the Monte Carlo genetic algorithm is proposed in this paper. The overall structure and hardware structure of the course scheduling system are designed. The hardware includes system management, course scheduling information input, course scheduling management, and course schedule query. In the software part, the Monte Carlo genetic algorithm is used to optimize the course scheduling optimization process, and a course scheduling scheme more in line with the needs of students and teachers is obtained. The experimental results show that the Monte Carlo genetic algorithm has higher convergence and higher user satisfaction compared with the traditional genetic algorithm. Therefore, it shows that the performance of the course scheduling system has been effectively improved.