scholarly journals A comparison of annealing techniques for academic course scheduling

1998 ◽  
pp. 92-112 ◽  
Author(s):  
M. A. Saleh Elmohamed ◽  
Paul Coddington ◽  
Geoffrey Fox
Keyword(s):  
Course Scheduling

Penerapan Algoritma ACO untuk Penjadwalan Kuliah Pengganti pada Perguruan Tinggi (Studi Kasus: Program Studi Informatika, Universitas Multimedia Nusantara)

2019 ◽  
Vol 9 (2) ◽  
pp. 79-85
Author(s):  
Indah Noviasari ◽  
Andre Rusli ◽  
Seng Hansun
Keyword(s):  
Ant Colony Optimization ◽  
Educational Institution ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Perceived Ease Of Use ◽  
Ant Colony Optimization Algorithm ◽  
Scheduling System ◽  
Course Scheduling

Students and scheduling are both essential parts in a higher educational institution. However, after schedules are arranged and students has agreed to them, there are some occasions that can occur beyond the control of the university or lecturer which require the courses to be cancelled and arranged for replacement course schedules. At Universitas Multimedia Nusantara, an agreement between lecturers and students manually every time to establish a replacement course. The agreement consists of a replacement date and time that will be registered to the division of BAAK UMN which then enter the new schedule to the system. In this study, Ant Colony Optimization algorithm is implemented for scheduling replacement courses to make it easier and less time consuming. The Ant Colony Optimization (ACO) algorithm is chosen because it is proven to be effective when implemented to many scheduling problems. Result shows that ACO could enhance the scheduling system in Universitas Multimedia Nusantara, which specifically tested on the Department of Informatics replacement course scheduling system. Furthermore, the newly built system has also been tested by several lecturers of Informatics UMN with a good level of perceived usefulness and perceived ease of use. Keywords—scheduling system, replacement course, Universitas Multimedia Nusantara, Ant Colony Optimization

Cooling/Heating Load Management in Educational Buildings through Course Scheduling

2021 ◽  
pp. 102405
Author(s):  
Amirhossein Fathi ◽  
Mohammad Salehi ◽  
Mohsen Mohammadi ◽  
Yasmin Rahimof ◽  
Parisa Hajaligol
Keyword(s):  
Load Management ◽  
Course Scheduling ◽  
Educational Buildings ◽  
Heating Load

Data driven course scheduling to ensure timely graduation

2021 ◽  
pp. 1-26
Author(s):  
Ming Chen ◽  
Xuan Huang ◽  
Hongyu Chen ◽  
Xuemei Su ◽  
Jasmine Yur-Austin
Keyword(s):  
Data Driven ◽  
Course Scheduling

A Constraint-Based Dental School Timetabling System

AI Magazine ◽  
2014 ◽  
Vol 35 (1) ◽  
pp. 53
Author(s):  
Hadrien Cambazard ◽  
Barry O'Sullivan ◽  
Helmut Simonis
Keyword(s):  
University Hospital ◽  
Dental School ◽  
Automated System ◽  
Mixed Integer ◽  
Analysis Tool ◽  
Course Scheduling ◽  
Java Application ◽  
School Timetabling ◽  
Programming Solution ◽  
University Course

We describe a constraint-based timetabling system that was developed for the dental school based at Cork University Hospital in Ireland. This sy stem has been deployed since 2010. Dental school timetabling differs from other university course scheduling in that certain clinic sessions can be used by multiple courses at the same time, provided a limit on room capacity is satisfied. Starting from a constraint programming solution using a web interface, we have moved to a mixed integer programming-based solver to deal with multiple objective functions, along with a dedicated Java application, which provides a rich user interface. Solutions for the years 2010, 2011 and 2012 have been used in the dental school, replacing a manual timetabling process, which could no longer cope with increasing student numbers and resulting resource bottlenecks. The use of the automated system allowed the dental school to increase the number of students enrolled to the maximum possible given the available resources. It also provides the school with a valuable “what-if” analysis tool.

scholarly journals Computer Experiment Management in Virtual Environment

2021 ◽  
Vol 2066 (1) ◽  
pp. 012081
Author(s):  
Jianqing Deng
Keyword(s):  
Virtual Environment ◽  
Management System ◽  
Ant Colony Algorithm ◽  
Computer Experiments ◽  
Computer Experiment ◽  
Utilization Rate ◽  
Course Scheduling ◽  
Experiment Management ◽  
Function Modules ◽  
Computer Operation

Abstract 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%.

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

2021 ◽  
Vol 2066 (1) ◽  
pp. 012079
Author(s):  
Yanbei Duan ◽  
Wenjie Lu
Keyword(s):  
Hybrid Algorithm ◽  
Ant Colony Algorithm ◽  
Operation Time ◽  
Original System ◽  
Colleges And Universities ◽  
Ant Colony ◽  
Scheduling System ◽  
Course Scheduling ◽  
System A ◽  
New System

Abstract 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.

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