Using Dynamic Search Mandatory Genetic Algorithm to Solve the University Course Timetabling Problem Considering Walking Distance

2021 ◽  
pp. 34-45
Author(s):  
Yuzhen Hu ◽  
Sirui Wang ◽  
Zhiming Duan
Keyword(s):  
Genetic Algorithm ◽  
Walking Distance ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Dynamic Search ◽  
Course Timetabling Problem ◽  
University Course ◽  
The University

scholarly journals Solving University Course Timetabling Problem Using Multi-Depth Genetic Algorithm

2020 ◽  
Vol 77 ◽  
pp. 01001
Author(s):  
Alfian Akbar Gozali ◽  
Shigeru Fujimura
Keyword(s):  
Genetic Algorithm ◽  
Individual Student ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Search Spaces ◽  
University Course Timetabling ◽  
Course Timetabling Problem ◽  
University Course ◽  
The University ◽  
Additional Constraints

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, and departments. The constraints could be hard (encouraged to be satisfied) or soft (better to be fulfilled). This problem becomes complicated for universities which have an immense number of students and lecturers. Moreover, several universities are implementing student sectioning which is a problem of assigning students to classes of a subject while respecting individual student requests along with additional constraints. Such implementation enables students to choose a set of preference classes first then the system will create a timetable depend on their preferences. Subsequently, student sectioning significantly increases the problem complexity. As a result, the number of search spaces grows hugely multiplied by the expansion of students, other variables, and involvement of their constraints. However, current and generic solvers failed to meet scalability requirement for student sectioning UCTP. In this paper, we introduce the Multi-Depth Genetic Algorithm (MDGA) to solve student sectioning UCTP. MDGA uses the multiple stages of GA computation including multi-level mutation and multi-depth constraint consideration. Our research shows that MDGA could produce a feasible timetable for student sectioning problem and get better results than previous works and current UCTP solver. Furthermore, our experiment also shows that MDGA could compete with other UCTP solvers albeit not the best one for the ITC-2007 benchmark dataset.

Genetic Algorithm With Three-Dimensional Population Dominance Strategy for University Course Timetabling Problem

2021 ◽  
Vol 13 (2) ◽  
pp. 56-69
Author(s):  
Zhifeng Zhang ◽  
Junxia Ma ◽  
Xiao Cui
Keyword(s):  
Genetic Algorithm ◽  
Three Dimensional ◽  
Adaptive Mutation ◽  
Initial Population ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Course Timetabling Problem ◽  
University Course ◽  
The University

In recent years, with the growing expansion of the recruitment scale and the further reform in teaching, how to use the limited teacher resources and the limited classroom resources to schedule a reasonable university course timetable has gotten great interest. In this paper, the authors firstly hashed over the university course timetabling problem, and then they presented the related mathematical model and constructed the relevant solution framework. Subsequently, in view of characteristics of the university course timetabling problem, they introduced genetic algorithm to solve the university course timetabling problem and proposed many improvement strategies which include the three-dimensional coding strategy, the fitness function design strategy, the initial population generation strategy, the population dominance strategy, the adaptive crossover probability strategy, and the adaptive mutation probability strategy to optimize genetic algorithm. Simulation results show that the proposed genetic algorithm can solve the university course timetabling problem effectively.

scholarly journals A hybrid metaheuristic approach to the university course timetabling problem

2010 ◽  
Vol 18 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Salwani Abdullah ◽  
Hamza Turabieh ◽  
Barry McCollum ◽  
Paul McMullan
Keyword(s):  
Course Timetabling ◽  
Timetabling Problem ◽  
Hybrid Metaheuristic ◽  
University Course Timetabling ◽  
Course Timetabling Problem ◽  
University Course ◽  
The University

scholarly journals The application of Coarse-Grained Parallel Genetic Algorithm with Hadoop in University Intelligent Course-Timetabling System

2015 ◽  
Vol 10 (8) ◽  
pp. 11
Author(s):  
Liping Wu
Keyword(s):  
Genetic Algorithm ◽  
Educational Administration ◽  
Coarse Grained ◽  
Map Reduce ◽  
Parallel Genetic Algorithm ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Course Timetabling Problem ◽  
University Course

The university course-timetabling problem is a NP-C problem. The traditional method of arranging course is inefficient, causes a high conflict rate of teacher resource or classroom resource, and is poor satisfaction in students. So it does not meet the requirements of modern university educational administration management. However, parallel genetic algorithm (PGA) not only have the advantages of the traditional genetic algorithm(GA), but also take full advantage of the computing power of parallel computing. It can improve the quality and speed of solving effectively, and have a broad application prospect in solving the problem of university course-timetabling problem. In this paper, based on the cloud computing platform of Hadoop, an improved method of fusing coarse-grained parallel genetic algorithm (CGPGA) and Map/Reduce programming model is deeply researched, and which is used to solve the problem of university intelligent courses arrangement. The simulation experiment results show that, compared with the traditional genetic algorithm, the coarse-grained parallel genetic algorithm not only improves the efficiency of the course arrangement and the success rate of the course, but also reduces the conflict rate of the course. At the same time, this research makes full use of the high parallelism of Map/Reduce to improve the efficiency of the algorithm, and also solves the problem of university scheduling problem more effectively.

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