scholarly journals University Course Timetabling using Bayesian based Optimization Algorithm

2018 ◽  
Vol 6 (2) ◽  
pp. 14
Author(s):  
Alinaswe Siame ◽  
Douglas Kunda
Keyword(s):  
Mathematical Optimization ◽  
Soft Constraints ◽  
Simulation Research ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Hard Constraints ◽  
Time Simulation ◽  
Feasible Solutions ◽  
Hard And Soft Constraints ◽  
University Course

<p>The timetabling problem has traditionally been treated as a mathematical optimization, heuristic, or human-machine interactive problem. The timetabling problem comprises hard and soft constraints. Hard constraints must be satisfied in order to generate feasible solutions. Soft constraints are sometimes referred to as preferences that can be contravened if necessary. In this research, we present is as both a mathematical and a human-machine problem that requires acceptable and controlled human input, then the algorithm gives options available without conflicting the hard constraints. In short, this research allows the human agents to address the soft-constraints as the algorithm works on the hard constraints, as well as the algorithm being able to learn the soft constraints over time. Simulation research was used to investigate the timetabling problem. Our proposed model employs the use a naïve Bayesian Algorithm, to learn preferred days and timings by lecturers and use them to resolve the soft constraints.  </p>

scholarly journals Sequential Integer Programming for Solving Curriculum-Based University Course Timetabling Problem

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1455-1462 ◽  
Keyword(s):  
Integer Programming ◽  
Mathematical Formulation ◽  
Soft Constraints ◽  
Course Timetabling ◽  
Second Stage ◽  
University Course Timetabling ◽  
Hard Constraints ◽  
Course Timetabling Problem ◽  
Two Stages ◽  
University Course

This research propose two stages sequential integer programming (IP) approach for solving curriculum-based university course timetabling problems (CB-UTT) in University Malaysia Sabah, Labuan international campus (UMSLIC). Like other timetabling problems, CB-UTT in UMSLIC has its own rules and features. The problem involves several hard constraints which need to be fully satisfied and soft constraints which satisfaction are very highly desirable. In this research mathematical formulation and two stages sequential IP search methodology based on UMSLIC is proposed. The IP search methodology is tested over two real-world instances, semester 1, session 2016/2017 and semester 2, session 2016/2017. The objective of this research is to generate high quality feasible CB-UTT which satisfies all peoples affected by the timetable. The results show that, the IP formulation proposed in this research is able to produce feasible solution in the first stage, and further improve by 10.99% and 8.92% respectively by solving soft constraints in the second stage without violating any hard constraints solved in the first stage. This IP approach is applicable towards the CB-UTT in UMSLIC

Combined Multi-Agent Method to Control Inter-Department Common Events Collision for University Courses Timetabling

2017 ◽  
Vol 29 (1) ◽  
pp. 110-126 ◽  
Author(s):  
Jalil Nourmohammadi-Khiarak ◽  
Yashar Zamani-Harghalani ◽  
Mohammad-Reza Feizi-Derakhshi
Keyword(s):  
Imperialist Competitive Algorithm ◽  
Course Timetabling ◽  
University Course Timetabling ◽  
University Courses ◽  
Hard Constraints ◽  
Proposed Model ◽  
Multi Agent ◽  
Hard And Soft Constraints ◽  
University Course ◽  
The University

Abstract University course timetabling is the scheduling of courses at different time slots in a university. The two important issues in this process are (i) the allocation of all events (professors, courses, and students) to resources (time slots daily/weekly and theory/practical classes) in a semester, and (ii) maximizing the satisfaction of common events (professors, courses, and students) among multiple departments. Accumulating evidences in university course timetabling problems suggest dividing the problem into several sub-problems. This study attempted to investigate the appropriateness of using the genetic algorithm (GA) and the imperialist competitive algorithm (ICA). The proposed technique consists of two steps: (i) using the proposed manipulated GA for solving the timetabling problem of each department, and (ii) eliminating the interference of common events among multiple departments and satisfying the hard and soft constraints by using ICA. Finally, a report on the efficiency of the methodology used in this study was obtained from the University of Tabriz in Iran and University of Udine in Italy. In this paper, the results are revealed in two ways: (i) reduction in the problems due to shrinking of the database and solving of the problems in parallel and (ii) solving the different parts of the problem by using various criterion results, increasing the common events satisfaction in that sub-problem. Eventually, the proposed model provided successful satisfaction of the hard constraints in <700 iterations with GA and elimination of interference in 40 iterations with ICA in most of the cases.

scholarly journals Constructing population of initial university timetable: design and analysis

2019 ◽  
Vol 15 (2) ◽  
pp. 1109 ◽  
Author(s):  
Juliana Wahid ◽  
Syariza Abdul-Rahman ◽  
Aniza Mohamed Din ◽  
Naimah Mohd-Hussin
Keyword(s):  
Statistical Tests ◽  
Population Based ◽  
Sequential Order ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Hard Constraints ◽  
Course Timetabling Problem ◽  
University Course

The construction of population of initial timetable is an essential stage in population-based metaheuristic approach for solving curriculum-based university course timetabling problem because it may impact the quality of the final timetable. This paper presents population of initial timetable construction approach in curriculum based course timetabling problem by using the graph heuristics to determine the sequential order of courses/lectures to be assigned in the timetable. The graph heuristics were implemented as single and combination of two heuristics. The courses in curriculum-based university course timetabling problem that was organized based on the heuristics setting will be repeatedly assigned to valid empty slots while fulfilling all the hard constraints. If a course is unable to be assigned to whichever slots because of no more valid empty slots, it will be inserted into the unscheduled courses/lectures list. The unscheduled courses/lectures list will be assigned later to the timetable using several procedures executed in a sequence. The approaches were tested on the ITC2007 instances and the results were analyzed with some statistical tests to determine the best setting of heuristics in the construction approach.  The result shows that the construction approach with combination of largest degree followed by saturation degree heuristic, generate the maximum number of population of initial timetables. The result from this study can be used in the improvement stage of metaheuristic algorithm that uses population-based approach.

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.

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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