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Journal articles on the topic 'University course timetabling'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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1

Hambali, A. M., Y. A. Olasupo, and M. Dalhatu. "Automated university lecture timetable using Heuristic Approach." Nigerian Journal of Technology 39, no. 1 (2020): 1–14. http://dx.doi.org/10.4314/njt.v39i1.1.

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There are different approaches used in automating course timetabling problem in tertiary institution. This paper present a combination of genetic algorithm (GA) and simulated annealing (SA) to have a heuristic approach (HA) for solving course timetabling problem in Federal University Wukari (FUW). The heuristic approach was implemented considering the soft and hard constraints and the survival for the fittest. The period and space complexity was observed. This helps in matching the number of rooms with the number of courses.
 Keywords: Heuristic approach (HA), Genetic algorithm (GA), Cour
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Rudová, Hana, Tomáš Müller, and Keith Murray. "Complex university course timetabling." Journal of Scheduling 14, no. 2 (2010): 187–207. http://dx.doi.org/10.1007/s10951-010-0171-3.

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Mazlan, Munirah, Mokhairi Makhtar, Ahmad Firdaus Khair Ahmad Khairi, Mohamad Afendee Mohamed, and Mohd Nordin Abdul Rahman. "Ant colony optimisation for solving university course timetabling problems." International Journal of Engineering & Technology 7, no. 2.15 (2018): 139. http://dx.doi.org/10.14419/ijet.v7i2.15.11371.

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Course timetabling is one of the most important activities faced by any educational institution. Furthermore, the course timetabling process is time-consuming and tiresome as it needs to be prepared for each regular semester. This paper aims to apply the Ant Colony Optimisation (ACO) method to solve the course timetabling problem. This approach is to optimise the properties of the course requirement and minimise various conflicts for the time slot assignation. This method is based on the life of the ant colony in generating automatic timetabling according to the properties (pheromones) such as
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Haitan, O. М. "THE UNIVERSITY COURSE TIMETABLING AUTOMATION." Scientific notes of Taurida National V.I. Vernadsky University. Series: Technical Sciences 1, no. 2 (2020): 58–66. http://dx.doi.org/10.32838/2663-5941/2020.2-1/09.

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Fakhurazi, Farah Nabihah, and Nur Aidya Hanum Aizam. "AN APPLICATION OF INTEGER LINEAR PROGRAMMING TOWARDS UNIVERSITI MALAYSIA TERENGGANU COURSE SCHEDULING PROBLEM." Universiti Malaysia Terengganu Journal of Undergraduate Research 4, no. 4 (2022): 1–8. http://dx.doi.org/10.46754/umtjur.v4i4.319.

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University Course Timetabling (UCT) problem is a combinatorial optimization problem in which a set of events has to be scheduled in timeslots and located in a suitable room by considering all the constraint involved. A feasible timetable plays an important role in ensuring the tasks or events are carried out appropriately. However, producing one is not an easy task due to its large data size and variety of requirements involved. In this research, mathematical model using Mixed Integer Linear Programming is applied for the university course timetabling problem. To validate the MILP model, data
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Arratia-Martinez, Nancy Maribel, Cristina Maya-Padron, and Paulina A. Avila-Torres. "University Course Timetabling Problem with Professor Assignment." Mathematical Problems in Engineering 2021 (January 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/6617177.

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One of the decision problems in many organizations and institutions is to decide how to schedule different tasks, in particular, in higher education institutions. One of the main problems is the university course timetabling problem (UCTP): this problem consists of the allocation of events (courses, professors, and students) to a number of fixed time slots and rooms, this at the beginning of each academic period of the universities. The existent formulations include particular requirements from different educational levels and institutions, as in our case. In this paper, we focus on the univer
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Zhang, Zhifeng, Junxia Ma, and Xiao Cui. "Genetic Algorithm With Three-Dimensional Population Dominance Strategy for University Course Timetabling Problem." International Journal of Grid and High Performance Computing 13, no. 2 (2021): 56–69. http://dx.doi.org/10.4018/ijghpc.2021040104.

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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
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8

Mazlan, Munirah, Mokhairi Makhtar, Ahmad Firdaus Khair Ahmad Khairi, Mohamed Afendee Mohamed, and Mohd Nordin Abdul Rahman. "A study on optimization methods for solving course timetabling problem in university." International Journal of Engineering & Technology 7, no. 2.14 (2018): 196. http://dx.doi.org/10.14419/ijet.v7i2.14.12824.

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Course timetabling is one of the most important processes faced by any educational institution. However, the course timetabling process is time consuming and tiresome as it needs to be done for each regular semester. This paper aims to study on the Optimization methods to solve the course timetabling problem. The study is obtained and discussed by categorizing between the classification of Hard Constraint and Soft Constraint and the classification of Optimization Methods. From the study, it shows that Meta-Heuristics are the mostly method used in solving the course timetabling problem. It is c
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9

Kiaer, Lynn, and Jay Yellen. "Weighted graphs and university course timetabling." Computers & Operations Research 19, no. 1 (1992): 59–67. http://dx.doi.org/10.1016/0305-0548(92)90059-e.

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10

Siame, Alinaswe, and Douglas Kunda. "University Course Timetabling using Bayesian based Optimization Algorithm." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 6, no. 2 (2018): 14. http://dx.doi.org/10.3991/ijes.v6i2.8990.

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<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. I
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Mazlan, Munirah, Mokhairi Makhtar, Ahmad Firdaus Khair Ahmad Khairi, and Mohamad Afendee Mohamed. "University course timetabling model using ant colony optimization algorithm approach." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (2019): 72. http://dx.doi.org/10.11591/ijeecs.v13.i1.pp72-76.

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<p>Due to the increased number of students and regulations, all educational institutions have renewed their interest to appear in the number of complexity and flexibility since the resources and events are becoming more difficult to be scheduled. Timetabling is the type of problems where the events need to be organized into a number of timeslots to prevent the conflicts in using a given set of resources. Thus in the intervening decades, significant progress has been made in the course timetabling problem monitoring with meta-heuristic adjustment. In this study, ant colony optimization (A
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Mazlan, Munirah, Mokhairi Makhtar, Ahmad Firdaus Khair Ahmad Khairi, and Mohamad Afendee Mohamed. "University course timetabling model using ant colony optimization algorithm approach." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (2019): 72–76. https://doi.org/10.11591/ijeecs.v13.i1.pp72-76.

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Due to the increased number of students and regulations, all educational institutions have renewed their interest to appear in the number of complexity and flexibility since the resources and events are becoming more difficult to be scheduled. Timetabling is the type of problems where the events need to be organized into a number of timeslots to prevent the conflicts in using a given set of resources. Thus in the intervening decades, significant progress has been made in the course timetabling problem monitoring with metaheuristic adjustment. In this study, ant colony optimization (ACO) algori
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Wahid, Juliana, Syariza Abdul-Rahman, Aniza Mohamed Din, and Naimah Mohd-Hussin. "Constructing population of initial university timetable: design and analysis." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 2 (2019): 1109. http://dx.doi.org/10.11591/ijeecs.v15.i2.pp1109-1118.

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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 unive
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14

Aguirre Lam, Marco Antonio, Fausto Balderas, Nelson Rangel-Valdez, Jose A. Martinez-Flores, Alan G. Aguirre L, and José A. Pérez-Vázquez. "University Timetabling Problem: An analysis of the state of the art." International Journal of Combinatorial Optimization Problems and Informatics 16, no. 3 (2025): 489–98. https://doi.org/10.61467/2007.1558.2025.v16i3.852.

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The University Course Timetabling Problem is regarded as one of the most significant administrative activities in academic institutions. It is framed as a combinatorial optimisation problem involving the scheduling of courses, students, lecturers, and classrooms, making it extremely complex. This work presents an analytical study of the Timetabling Problem, including its classifications, techniques, advantages, disadvantages, and a case study based on the model implemented at the Instituto Tecnológico de Ciudad Madero.
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15

Foong, Oi Mean, and Syamilla Bt Rahim. "Particle Swarm Inspired Timetabling for ICT Courses." Applied Mechanics and Materials 263-266 (December 2012): 2138–45. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2138.

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University course timetabling is a complex problem which must satisfy a list of constraints in order to allocate the right timeslots and venues for various courses. The challenge is to make the NP-hard problem user-friendly, highly interactive and faster run time complexity of algorithm. The objective of the paper is to propose Particle Swarm Optimization (PSO) timetabling model for Undergraduate Information and Communication Technology (ICT) courses. The PSO model satisfies hard constraints with minimal violation of soft constraints. Empirical results show that the rds: NP hard problem, timet
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Ruslaan, Mohd Asyraf, and Zalmiyah Zakaria. "University Course Timetabling System For Part-Time Students." International Journal of Advanced Science Computing and Engineering 1, no. 2 (2019): 68–75. http://dx.doi.org/10.30630/ijasce.1.2.5.

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University timetabling system is a part of a timetabling problem that aims to produce course timetable that meets student needs such as the maximum number of subjects that can be offered, the maximum number of elective subjects that can be offered and the number of subject students can take. In every semester, the timetabling process in UTMSPACE is done manually where there are likely to be a small number of students who will have problems because the subject to be taken is not in the subjects offering list. Additionally, the number of subjects offered is also not optimal and this will result
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Ruslaan, Mohd Asyraf, Zalmiyah Zakaria, Mohd Zainuri Saringat, and Shahreen Kasim. "University Course Timetabling System For Part-Time Students." International Journal of Advanced Science Computing and Engineering 1, no. 2 (2019): 68–75. http://dx.doi.org/10.62527/ijasce.1.2.5.

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University timetabling system is a part of a timetabling problem that aims to produce course timetable that meets student needs such as the maximum number of subjects that can be offered, the maximum number of elective subjects that can be offered and the number of subject students can take. In every semester, the timetabling process in UTMSPACE is done manually where there are likely to be a small number of students who will have problems because the subject to be taken is not in the subjects offering list. Additionally, the number of subjects offered is also not optimal and this will result
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18

Chávez-Bosquez, Oscar, José Hernández-Torruco, Betania Hernández-Ocaña, and Juana Canul-Reich. "Modeling and Solving a Latin American University Course Timetabling Problem Instance." Mathematics 8, no. 10 (2020): 1833. http://dx.doi.org/10.3390/math8101833.

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Timetabling problem is a complex task that is performed by a number of institutions worldwide, which has been usually addressed as an optimization problem where every approach considers the particular constraints of each institution under consideration. In this paper, we describe, model, and propose a solution to the timetabling problem at the División Académica de Ciencias y Tecnologías de la Información of the Universidad Juárez Autónoma de Tabasco (UJAT), México. We modeled the specific constraints of this problem instance using the Object Constraint Language (OCL) of the Unified Modeling L
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Firdaus Khair, Ahmad, Mokhairi Makhtar, Munirah Mazlan, Mohamad Afendee Mohamed, and Mohd Nordin Abdul Rahman. "A study on university course and exam timetabling problems and methods: an optimization survey." International Journal of Engineering & Technology 7, no. 2.14 (2018): 191. http://dx.doi.org/10.14419/ijet.v7i2.14.12823.

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The objective of this paper was to retrieve the overview approaches that have been proposed and classification constraints related to previ-ous papers of timetabling problems. Optimisation and scheduling are essential problems in every type of timetabling that can be considered as a non-deterministic polynomial. The objective of this paper to investigate the course and exam timetabling problem by presented classifi-cation table of set of constraints and describes the most reliable method that has been used to solve university timetabling problem. The re-sult of study concerned the two most suc
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20

Asiyaban, Sedigheh, and Zohreh Mousavinasab. "University Course Timetabling using Multi-population Genetic Algorithm Guided with Local Search and Fuzzy Logic." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 10 (2013): 3043–50. http://dx.doi.org/10.24297/ijct.v11i10.2972.

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Problem of courses timetabling is a time consuming and demanding issues in any education environment that they are involved in every semester. The main aim of timetabling problem is the allocation of a number of courses to a limited set of resources such as classrooms, time slots, professors and students so that some predefined hard and soft constraints are satisfied. Furthermore, the available resources are used to the best.   In fact course timetabling is one of optimization problems. It has been proved computational complexity of this problem is NP, so there is no optimal solution for th
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Abdennadher, Slim, and Michael Marte. "University course timetabling using constraint handling rules." Applied Artificial Intelligence 14, no. 4 (2000): 311–25. http://dx.doi.org/10.1080/088395100117016.

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22

LAI, LIEN F., CHAO-CHIN WU, NIEN-LIN HSUEH, LIANG-TSUNG HUANG, and SHIOW-FEN HWANG. "AN ARTIFICIAL INTELLIGENCE APPROACH TO COURSE TIMETABLING." International Journal on Artificial Intelligence Tools 17, no. 01 (2008): 223–40. http://dx.doi.org/10.1142/s0218213008003868.

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Course Timetabling is a complex problem that cannot be dealt with by using only a few general principles. The various actors (the administrator, the chairman, the instructor and the student) have their own objectives, and these objectives usually conflict. The complexity of the relationships among time slots, classes, classrooms, and instructors makes it difficult to achieve a feasible solution. In this article, we propose an artificial intelligence approach that integrates expert systems and constraint programming to implement a course timetabling system. Expert systems are utilized to incorp
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González López, Brenda Sunuami, René Arnulfo García Hernández, and Yulia Ledeneva. "Personal Course Timetabling for University Students based on Genetic Algorithm." International Journal of Combinatorial Optimization Problems and Informatics 12, no. 3 (2021): 32–49. https://doi.org/10.61467/2007.1558.2021.v12i3.237.

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This paper presents the optimization problem of generating a Personal University Course Timetabling (PUCT) for students from a Published Course Catalog (PCC) and an evolutionary approach to solve it. A PPC is a result of a manual or automatic process of a university course timetabling system to assign classes to times and spaces, considering constraints and preferences. However, independently of how the PPC was created, each university student faces particular constraints to select its timetable's courses. Due to the complexity of generating the timetable manually, several students were curren
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Arratia-Martinez, Nancy M., Paulina A. Avila-Torres, and Juana C. Trujillo-Reyes. "Solving a University Course Timetabling Problem Based on AACSB Policies." Mathematics 9, no. 19 (2021): 2500. http://dx.doi.org/10.3390/math9192500.

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The purpose of this research is to solve the university course timetabling problem (UCTP) that consists of designing a schedule of the courses to be offered in one academic period based on students’ demand, faculty composition and institutional constraints considering the policies established in the standards of the Association to Advance Collegiate Schools of Business (AACSB) accreditation. These standards involve faculty assignment with high level credentials that have to be fulfilled for business schools on the road to seek recognition and differentiation while providing exceptional learnin
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Ogunkan, Stella Kehinde, Peter Olalekan Idowu, Elijah Olusayo Omidiora, and Christopher Akin Oyeleye. "First Fit Algorithm: A Graph Coloring Approach to Conflict-Free University Course Timetabling." Asian Journal of Research in Computer Science 17, no. 5 (2024): 125–39. http://dx.doi.org/10.9734/ajrcos/2024/v17i5443.

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Aims: Tackling scheduling issues with the most optimal graph coloring algorithm has consistently posed significant difficulties. The university scheduling problem can be expressed as a graph coloring problem, where courses are depicted as vertices and the connections between courses that have common students or teachers are represented as edges. Subsequently, the task at hand is to assign the vertices with the minimum number of colors. In order to accomplish this task, this paper present a graph coloring technique to conflict free university course timetabling using first fit algorithms.
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Alfian, Akbar Gozali, and Fujimura Shigeru. "Reinforced Island Model Genetic Algorithm to Solve University Course Timetabling." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 6 (2018): 2747 − 2755. https://doi.org/10.12928/TELKOMNIKA.v16i6.9691.

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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, departments, etc. This problem becomes complicated for universities which have immense number of students and lecturers. Therefore, a scalable and reliable timetabling solver is needed. However, current solvers and generic solution failed to meet several specific UCTP. Moreover, some universities implement student sectioning problem with individual student specific constraints. This r
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Muklason, Ahmad, Ahsanul Marom, and I. Gusti Agung Premananda. "Automated Course Timetabling Optimization Using Tabu-Simulated Annealing Hyper-Heuristics Algorithm." Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika 10, no. 1 (2024): 15–21. http://dx.doi.org/10.23917/khif.v10i1.4835.

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The topic of solving Timetabling Problems is an interesting area of study. These problems are commonly encountered in many institutions, particularly in the educational sector, including universities. One of the challenges faced by universities is the Course Timetabling Problem, which needs to be addressed regularly in every semester, taking into consideration the available resources. Solving this problem requires a significant amount of time and resources to create the optimal schedule that adheres to the predefined constraints, including both hard and soft constraints. As a problem of comput
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Gu, Xin, Muralee Krish, Shaleeza Sohail, Sweta Thakur, Fariza Sabrina, and Zongwen Fan. "From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling Problems." Computation 13, no. 1 (2025): 10. https://doi.org/10.3390/computation13010010.

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Solving the university timetabling problem is crucial as it ensures efficient use of resources, minimises scheduling conflicts, and enhances overall productivity. This paper presents a comprehensive review of university timetabling problems using integer programming algorithms. This study explores various integer programming techniques and their effectiveness in optimising complex scheduling requirements in higher education institutions. We analysed 95 integer programming-based models developed for solving university timetabling problems, covering relevant research from 1990 to 2023. The goal
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Strnad, Damjan, and Nikola Guid. "A MULTI-AGENT SYSTEM FOR UNIVERSITY COURSE TIMETABLING." Applied Artificial Intelligence 21, no. 2 (2007): 137–53. http://dx.doi.org/10.1080/08839510601147554.

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Chiarandini, Marco, Mauro Birattari, Krzysztof Socha, and Olivia Rossi-Doria. "An effective hybrid algorithm for university course timetabling." Journal of Scheduling 9, no. 5 (2006): 403–32. http://dx.doi.org/10.1007/s10951-006-8495-8.

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Pongcharoen, P., W. Promtet, P. Yenradee, and C. Hicks. "Stochastic Optimisation Timetabling Tool for university course scheduling." International Journal of Production Economics 112, no. 2 (2008): 903–18. http://dx.doi.org/10.1016/j.ijpe.2007.07.009.

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Song, Kwonsik, Sooyoung Kim, Moonseo Park, and Hyun-Soo Lee. "Energy efficiency-based course timetabling for university buildings." Energy 139 (November 2017): 394–405. http://dx.doi.org/10.1016/j.energy.2017.07.176.

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Al-Betar, Mohammed Azmi, and Ahamad Tajudin Khader. "A harmony search algorithm for university course timetabling." Annals of Operations Research 194, no. 1 (2010): 3–31. http://dx.doi.org/10.1007/s10479-010-0769-z.

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Burke, Edmund K., Jakub Mareček, Andrew J. Parkes, and Hana Rudová. "Decomposition, reformulation, and diving in university course timetabling." Computers & Operations Research 37, no. 3 (2010): 582–97. http://dx.doi.org/10.1016/j.cor.2009.02.023.

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Nourmohammadi-Khiarak, Jalil, Yashar Zamani-Harghalani, and Mohammad-Reza Feizi-Derakhshi. "Combined Multi-Agent Method to Control Inter-Department Common Events Collision for University Courses Timetabling." Journal of Intelligent Systems 29, no. 1 (2017): 110–26. http://dx.doi.org/10.1515/jisys-2017-0249.

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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
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Wu, Liping. "The application of Coarse-Grained Parallel Genetic Algorithm with Hadoop in University Intelligent Course-Timetabling System." International Journal of Emerging Technologies in Learning (iJET) 10, no. 8 (2015): 11. http://dx.doi.org/10.3991/ijet.v10i8.5206.

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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 applicat
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Mohd Zaulir, Zahidah, Nurul Liyana Abdul Aziz, and Nur Aidya Hanum Aizam. "General University Course Timetabling Mathematical Model to a Malaysian Public University Problem." Malaysian Journal of Fundamental and Applied Sciences 18, no. 1 (2022): 82–94. http://dx.doi.org/10.11113/mjfas.v18n1.2408.

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University course timetabling is a well-known management problem amongst researchers, thus the rich body of literature. However, published articles are mainly on improved solution approaches leads to presentation on different constraints used and ignoring human preferences. This however, limits the model application to other universities. The research aims to bridge the gap by acknowledging these varieties of demands. In the process of generating our mathematical model, we have gone through meticulously researches that have been carried out in the past years to determine the demands of individ
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Cambazard, Hadrien, Barry O’Sullivan, and Helmut Simonis. "The Deployment of a Constraint-Based Dental School Timetabling System." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 2 (2013): 1459–64. http://dx.doi.org/10.1609/aaai.v27i2.18983.

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We describe a constraint-based timetabling system that was developed for the dental school based at Cork University Hospital in Ireland. This system 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, whic
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Larabi-Marie-Sainte, Souad, Roohi Jan, Ali Al-Matouq, and Sara Alabduhadi. "The impact of timetable on student’s absences and performance." PLOS ONE 16, no. 6 (2021): e0253256. http://dx.doi.org/10.1371/journal.pone.0253256.

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Student’s academic performance is the point of interest for both the student and the academic institution in higher education. This performance can be affected by several factors and one of them is student absences. This is mainly due to the missed lectures and other class activities. Studies related to university timetabling investigate the different techniques and algorithms to design course timetables without analyzing the relationship between student attendance behavior and timetable design. This article first aimed at demonstrating the impact of absences and timetabling design on student’
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FUKUSHIMA, Makoto. "A Hybrid Algorithm for the University Course Timetabling Problems." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 22, no. 1 (2010): 142–47. http://dx.doi.org/10.3156/jsoft.22.142.

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Kanoh, Hitoshi, and Yuusuke Sakamoto. "Knowledge-based genetic algorithm for university course timetabling problems." International Journal of Knowledge-based and Intelligent Engineering Systems 12, no. 4 (2008): 283–94. http://dx.doi.org/10.3233/kes-2008-12403.

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Irohara, Takashi, and Chompoonoot Kasemset. "University course timetabling problem considering day and time pattern." International Journal of Operational Research 36, no. 3 (2019): 375. http://dx.doi.org/10.1504/ijor.2019.10024716.

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43

Kasemset, Chompoonoot, and Takashi Irohara. "University course timetabling problem considering day and time pattern." International Journal of Operational Research 36, no. 3 (2019): 375. http://dx.doi.org/10.1504/ijor.2019.103124.

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44

Dimopoulou, M., and P. Miliotis. "Implementation of a university course and examination timetabling system." European Journal of Operational Research 130, no. 1 (2001): 202–13. http://dx.doi.org/10.1016/s0377-2217(00)00052-7.

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45

Babaei, Hamed, Jaber Karimpour, and Amin Hadidi. "A survey of approaches for university course timetabling problem." Computers & Industrial Engineering 86 (August 2015): 43–59. http://dx.doi.org/10.1016/j.cie.2014.11.010.

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46

Abuhamdah, Anmar, Masri Ayob, Graham Kendall, and Nasser R. Sabar. "Population based Local Search for university course timetabling problems." Applied Intelligence 40, no. 1 (2013): 44–53. http://dx.doi.org/10.1007/s10489-013-0444-6.

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47

Dammak, Abdelaziz, Abdelkarim Elloumi, Hichem Kamoun, and Jacques A. Ferland. "Course timetabling at a Tunisian University: A case study." Journal of Systems Science and Systems Engineering 17, no. 3 (2008): 334–52. http://dx.doi.org/10.1007/s11518-008-5079-5.

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48

Rezaeipanah, Amin, Zahra Abshirini, and Milad Boshkani Zade. "Solving University Course Timetabling Problem Using Parallel Genetic Algorithm." International Journal of Scientific Research in Computer Science and Engineering 7, no. 5 (2019): 5–13. http://dx.doi.org/10.26438/ijsrcse/v7i5.513.

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ÖZKAN, Akın, and Aydın ULUCAN. "A Mixed-Integer Linear Programming approach for university timetabling problem with the multi-section courses: an application for Hacettepe University Department of Business Administration." International Advanced Researches and Engineering Journal 6, no. 2 (under construction) (2022): 132–41. http://dx.doi.org/10.35860/iarej.1118213.

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Abstract:
University course timetabling is an NP-Complete problem type which becomes even more difficult due to the specific requirements of each university. In this study, it was aimed to solve a university course timetabling problem by using integer programming and to develop assignment models that can be easily adapted to similar problems. The models that we developed for the solution are based on the integer programming model of Daskalaki et al. [1]. In addition, the models were developed taking into account the fact that there was an availability of multi-section courses, the minimum overlap of ele
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Cambazard, Hadrien, Barry O'Sullivan, and Helmut Simonis. "A Constraint-Based Dental School Timetabling System." AI Magazine 35, no. 1 (2014): 53. http://dx.doi.org/10.1609/aimag.v35i1.2503.

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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, whi
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