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Relevant bibliographies by topics / Timetabling

Academic literature on the topic 'Timetabling'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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Journal articles on the topic "Timetabling"

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Chand, A. "A heuristic approach to constraint optimization in timetabling." South Pacific Journal of Natural and Applied Sciences 20, no. 1 (2002): 64. http://dx.doi.org/10.1071/sp02013.

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Timetabling is a difficult (NP-complete) problem and belongs to a general class of problems known as scheduling. Due to a variety of constraints typical in different timetabling environments, it has been difficult to develop a generic solution for timetabling. This paper is an attempt to define a generic computational model for examination timetabling for predefined constraints found in the problem, and proposes a heuristic method of developing an acceptable solution. The declarative nature of the developed constraints language (based on the structured query language) is utilized to construct constraints and specify the timetabling problem as a constraint satisfaction problem. A university examination timetabling problem is used to illustrate and test the model.

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Mile, Abdul Rasyid, Muhammad Rifai Katili, and Nurwan Nurwan. "Penjadwalan Mata Pelajaran Menggunakan Integer Nonlinear Programming." Research in the Mathematical and Natural Sciences 1, no. 1 (February 17, 2022): 12–18. http://dx.doi.org/10.55657/rmns.v1i1.2.

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Timetabling is one of the problems faced by large numbers of institutions, including schools. In this paper, this timetabling problem is mathematically modeled using Integer Nonlinear Programming to optimize the result with the non-linear objective function or constraint function. The model was implemented to solve the timetabling problem in one of Madrasah Tsanawiyah Islamic junior high school in Gorontalo. The result effective solutions in the form of subject and instructor timetabling that overcome the obstacles are obtained. To better the timetabling, supplementary teachers are still required for some subjects.

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Sakaliuk, O., and F. Trishyn. "ANALYSIS OF PROCESS CREATION OF THE COURSES TIMETABLING." Automation of technological and business processes 11, no. 2 (June 26, 2019): 30–35. http://dx.doi.org/10.15673/atbp.v11i2.1370.

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The organization of education for persons who are studying is carried out in accordance with the coursestimetabling. Courses timetabling should provide an effective implementation of the curriculum in full.The problem of creating the courses timetabling has been given sufficient attention during the last decades. But even today,the process of creating the courses timetabling requires a fairly large part of human control. The typical problem of creatingthe courses timetabling is to plan a set of meetings between teachers and students over a period of time that requires someresources and should meet some additional requirements. Significant requirements in scheduling are those in which theteacher, student, or audience is not used more than once at the one-time interval. Creating the courses timetabling ininstitutions of higher education is carried out every semester. Preparing the timetabling of the courses is a labor-intensive,tedious process that requires a significant amount of human resources and time.In this scientific article, an analysis of process creation of the courses timetabling is carried out. In the course of work areinvestigated basic guidance documents for the creation of the courses timetabling. On the basis of controlled observation,where the observer was included in the process and conducted in real conditions, on the basis of the dispatch of educationaldepartment of the Odessa National Academy of Food Technologies, the process of creation of the courses timetabling isdescribed, and the main structures of the institution of higher education, which are involved, are determined. On the basis ofcontrolled observation, where the observer was included in the process and conducted in real conditions, on the basis of thedispatch section of educational department of the Odessa National Academy of Food Technologies, the process of creation ofthe courses timetabling is described, and the main structures of the institution of higher education, which are involved, aredetermined. Constraints which must be observed during the process are divided into hard and soft. Actual solutions areinvestigated, such as: genetic algorithm, greedy algorithm, graph coloring method, method of logical programming withconstraints, etc. Describes what determines the performance of these methods and algorithms.

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Zhu, Kaixiang, Lily D. Li, and Michael Li. "School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method." Mathematics 10, no. 1 (December 26, 2021): 73. http://dx.doi.org/10.3390/math10010073.

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Although educational timetabling problems have been studied for decades, one instance of this, the school timetabling problem (STP), has not developed as quickly as examination timetabling and course timetabling problems due to its diversity and complexity. In addition, most STP research has only focused on the educators’ availabilities when studying the educator aspect, and the educators’ preferences and expertise have not been taken into consideration. To fill in this gap, this paper proposes a conceptual model for the school timetabling problem considering educators’ availabilities, preferences and expertise as a whole. Based on a common real-world school timetabling scenario, the artificial bee colony (ABC) algorithm is adapted to this study, as research shows its applicability in solving examination and course timetabling problems. A virtual search space for dealing with the large search space is introduced to the proposed model. The proposed approach is simulated with a large, randomly generated dataset. The experimental results demonstrate that the proposed approach is able to solve the STP and handle a large dataset in an ordinary computing hardware environment, which significantly reduces computational costs. Compared to the traditional constraint programming method, the proposed approach is more effective and can provide more satisfactory solutions by considering educators’ availabilities, preferences, and expertise levels.

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Zhu, Kaixiang, Lily D. Li, and Michael Li. "A Survey of Computational Intelligence in Educational Timetabling." International Journal of Machine Learning and Computing 11, no. 1 (January 2021): 40–47. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1012.

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Timetabling problems have been widely studied, of which Educational Timetabling Problem (ETP) is the biggest section. Generally, ETP can be divided into three modules, namely, course timetabling, school timetabling, and examination timetabling. For solving ETP, many techniques have been developed including conventional algorithms and computational intelligence approaches. Several surveys have been conducted focusing on those methods. Some surveys target on particular categories; some tend to cover all types of approaches. However, there are lack of reviews specifically focusing on computational intelligence in ETP. Therefore, this paper aims at providing a reference of selecting a method for the applications of ETP by reviewing popular computational intelligent algorithms, such as meta-heuristics, hyper-heuristics, hybrid methods, fuzzy logic, and multi-agent systems. The application would be categorised and described into the three types of ETP respectively.

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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 (April 6, 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 time, student, lecturer and room, besides satisfying the constraints. The implementation of this method is to find an effective and better solution for university course timetabling. The result and performance evaluation is used to determine whether it is reliable in providing the feasible timetable.

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M, Pharsana Parveen, and Julia Rose Mary. "Graph Colouring, Class Scheduling and Application in Modern Computer Science." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 1065–69. http://dx.doi.org/10.22214/ijraset.2023.49590.

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Abstract: Course timetabling and exam timetabling are two most common academic scheduling problems in any of the educational institution. A desirable schedule combines resources in a way which satisfies essential and preferential constrains. In this paper, we focus on college course timetabling including hard and soft constraints. Our Objective is to inculcate graph colouring approach to obtain a conflict free course graph.

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Lei, Yu, Maoguo Gong, Licheng Jiao, Wei Li, Yi Zuo, and Qing Cai. "A Double Evolutionary Pool Memetic Algorithm for Examination Timetabling Problems." Mathematical Problems in Engineering 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/867645.

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A double evolutionary pool memetic algorithm is proposed to solve the examination timetabling problem. To improve the performance of the proposed algorithm, two evolutionary pools, that is, the main evolutionary pool and the secondary evolutionary pool, are employed. The genetic operators have been specially designed to fit the examination timetabling problem. A simplified version of the simulated annealing strategy is designed to speed the convergence of the algorithm. A clonal mechanism is introduced to preserve population diversity. Extensive experiments carried out on 12 benchmark examination timetabling instances show that the proposed algorithm is able to produce promising results for the uncapacitated examination timetabling problem.

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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 (April 6, 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 successfully method that widely used for optimising course and exam timetable. The contribution of this study also help to provide knowledge and idea for further surveys.

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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 (April 6, 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 concluded that this method is suitable for future used compared to other techniques studied. An analysis and observation will be carried out for the research future.

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Dissertations / Theses on the topic "Timetabling"

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Weare, Rupert. "Automated examination timetabling." Thesis, University of Nottingham, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282578.

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Ismail, Zuhaimy H. "Aspects of computerised timetabling." Thesis, Loughborough University, 1994. https://dspace.lboro.ac.uk/2134/13825.

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This research considers the problem of constructing high school timetables using a computer. In the majority of high schools, termly or yearly timetables are still being produced manually. Constructing a timetable is a hard and time consuming task which is carried out repeatedly thus a computer program for assisting with this problem would be of great value. This study is in three parts. First. an overall analysis of the problem is undertaken to provide background knowledge and to identify basic principles in the construction of a school timetable. The characteristics of timetabling problems are identified and the necessary data for the construction of a timetable is identified. The first part ends with the production of a heuristic model for generating an initial solution that satisfies all the hard constraints embodied in the curriculum requirements. The second stage of the research is devoted to designing a heuristic model for solving a timetable problem with hard and medium constraints. These include constraints like the various numbers of common periods, double periods and reducing the repeated allocation of a subject within any day. The approaches taken are based on two recently developed techniques, namely tabu search and simulated annealing. Both of these are used and comparisons of their efficiency are provided. The comparison is based on the percentage fulfilment of the hard and medium requirements. The third part is devoted to one of the most difficult areas in timetable construction, that is the softer requirements which are specific to particular schools and whose satisfaction is not seen as essential. This section describes the development of an expert system based on heuristic production rules to satisfy a range of soft requirements. The soft requirements are studied and recorded as rules and a heuristic solution is produced for each of the general requirements. Different levels of rule are developed, from which the best possible solution to a particular timetable problem is expertly produced. Finally, possible extensions of the proposed method and its application to other types of the timetabling problem are discussed.

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Hellkvist, Henning, and William Sjöstedt. "Toward Automated Timetabling at TekNat." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-169531.

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This is a report on our approach to use constraint programming to automatically solve the Uppsala University timetabling problem at the Faculty of Science and Technology (TekNat). The project has been successful as the program produced can quickly solve the problem of assigning courses to specific times and rooms. The task of scheduling the entire TekNat problem at once is in most cases unsuccessful, but the problem can be split up over multiple departments, each scheduled one by one. Scheduling an instance of roughly 40 courses whichis the size of the IT department takes less than a minute while solving data resembling the entire TekNat instance of about 200 courses takes about 5 minutes if successful. However, the model has only hard constraints and may disregard solutions that would be acceptable when reviewed by human scheduling officers. Therefore the program will not work in all problem instances, and no solution is ever guaranteed.The report contains a brief introduction on constraint programming in general as well as amore detailed overview of the specific parts we have utilized of the constraint programming library we choose to use for this task; Gecode. Furthermore we will present a fully functional prototype for a user interface we designed during the project that can benefit both teachers and schedulers alike. We present the mechanics of our program and discuss its advantages and drawbacks as well as outlining what areas a continuation of this project should focus on.

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Murugan, Anandaraj Soundarya Raja. "University Timetabling using Genetic Algorithm." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3792.

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The field of automated timetabling and scheduling meeting all the requirementsthat we call constraints is always difficult task and already proved as NPComplete. The idea behind my research is to implement Genetic Algorithm ongeneral scheduling problem under predefined constraints and check the validityof results, and then I will explain the possible usage of other approaches likeexpert systems, direct heuristics, network flows, simulated annealing and someother approaches. It is observed that Genetic Algorithm is good solutiontechnique for solving such problems. The program written in C++ and analysisis done with using various tools explained in details later.

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Pendlebury, J. "A computer-aided timetabling system." Thesis, Lancaster University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277233.

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Abdul, Rahman Syariza. "Search methodologies for examination timetabling." Thesis, University of Nottingham, 2012. http://eprints.nottingham.ac.uk/12709/.

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Working with examination timetabling is an extremely challenging task due to the difficulty of finding good quality solutions. Most of the studies in this area rely on improvement techniques to enhance the solution quality after generating an initial solution. Nevertheless, the initial solution generation itself can provide good solution quality even though the ordering strategies often using graph colouring heuristics, are typically quite simple. Indeed, there are examples where some of the produced solutions are better than the ones produced in the literature with an improvement phase. This research concentrates on constructive approaches which are based on squeaky wheel optimisation i.e. the focus is upon finding difficult examinations in their assignment and changing their position in a heuristic ordering. In the first phase, the work is focused on the squeaky wheel optimisation approach where the ordering is permutated in a block of examinations in order to find the best ordering. Heuristics are alternated during the search as each heuristic produces a different value of a heuristic modifier. This strategy could improve the solution quality when a stochastic process is incorporated. Motivated by this first phase, a squeaky wheel optimisation concept is then combined with graph colouring heuristics in a linear form with weights aggregation. The aim is to generalise the constructive approach using information from given heuristics for finding difficult examinations and it works well across tested problems. Each parameter is invoked with a normalisation strategy in order to generalise the specific problem data. In the next phase, the information obtained from the process of building an infeasible timetable is used. The examinations that caused infeasibility are given attention because, logically, they are hard to place in the timetable and so they are treated first. In the adaptive decomposition strategy, the aim is to automatically divide examinations into difficult and easy sets so as to give attention to difficult examinations. Within the easy set, a subset called the boundary set is used to accommodate shuffling strategies to change the given ordering of examinations. Consequently, the graph colouring heuristics are employed on those constructive approaches and it is shown that dynamic ordering is an effective way to permute the ordering. The next research chapter concentrates on the improvement approach where variable neighbourhood search with great deluge algorithm is investigated using various neighbourhood orderings and initialisation strategies. The approach incorporated with a repair mechanism in order to amend some of infeasible assignment and at the same time aiming to improve the solution quality.

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Newall, James P. "Hybrid methods for automated timetabling." Thesis, University of Nottingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285687.

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Lindberg, Viktor. "Evaluation of School Timetabling Algorithms." Thesis, Umeå universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-128602.

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Most schools have the problem that they need to organise the meetings between students and teachers in lectures and place these lectures in a timetable. Four different algorithms that can be used to solve this problem will be evaluated in this thesis. The algorithms are Simulated Annealing, Particle Swarm Optimisation, Hyper-Heuristic Genetic Algorithm and Iterated Local Search. In this thesis a description of the algorithms will be given and then evaluated by running them on a set of different known timetabling problems and have their results compared with each other to find out which algorithm is best suited for use in a potential end-user application. Simulated Annealing combined with Iterated Local Search gave the best resultsin this thesis.

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Jeřábková, Eva. "Timetabling - příprava rozvrhu pro školu." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-198439.

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The aim of the thesis is to create a model of timetable for primary school for all teachers and classes so that the timetable is in compliance with all teaching plans, the conditions of the Ministry of Education, Youth and Sports (general education program), the school's needs and requirements of teachers. The timetable is created for a particular school, ZŠ nám. Svobody in Prague. The first part of the thesis is focused on the theoretical description of methods and procedures for dealing with the problem of timetable. The second part describes the elementary school, for which the timetable is made, it's classes, teachers and conditions of the tuition. It is also formulated and described a mathematical model of the problem that is solved by using the program Lingo.

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Arbaoui, Taha. "Modeling and solving university timetabling." Thesis, Compiègne, 2014. http://www.theses.fr/2014COMP2167/document.

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Cette thèse s’intéresse aux problèmes d’emploi du temps d’universités. Ces problèmes sont rencontrés chaque année par les utilisateurs. Nous proposons des bornes inférieures, des méthodes heuristiques et des modèles de programmation mixte en nombres entiers et de programmation par contraintes. Nous traitons le problème d’emploi du temps d’examens et celui d’affectation des étudiants. Nous proposons de nouvelles méthodes et formulations et les comparons aux approches existantes. Nous proposons, pour le problème d’emploi du temps d’examens, une amélioration d’un modèle mathématique en nombres entiers qui permettra d’obtenir des solutions optimales. Ensuite, des bornes inférieures, une formulation plus compacte des contraintes et un modèle de programmation par contraintes sont proposés. Pour le problème d’emploi du temps d’examens à l’Université de Technologie de Compiègne, nous proposons une approche mémétique. Enfin, nous présentons un modèle mathématique pour le problème d’affectation des étudiants et nous étudions sa performance sur un ensemble d’instances réelles
This thesis investigates university timetabling problems. These problems occur across universities and are faced each year by the practitioners. We propose new lower bounds, heuristic approaches, mixed integer and constraint programming models to solve them. We address the exam timetabling and the student scheduling problem. We investigate new methods and formulations and compare them to the existing approaches. For exam timetabling, we propose an improvement to an existing mixed integer programming model that makes it possible to obtain optimal solutions. Next, lower bounds, a more compact reformulation for constraints and a constraint programming model are proposed. For the exam timetabling problem at Université de Technologie de Compiègne, we designed a memetic approach. Finally, we present a new formulation for the student scheduling problem and investigate its performance on a set of real-world instances

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Books on the topic "Timetabling"

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Burghes, D. N. Timetabling express trains. Cambridge: Hobsons Limited, 1985.

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Hargreaves, Richard J. An exam timetabling application. Manchester: UMIST, 1998.

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Mühlenthaler, Moritz. Fairness in Academic Course Timetabling. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12799-6.

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Burke, Edmund, and Peter Ross, eds. Practice and Theory of Automated Timetabling. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61794-9.

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Warr, Noel M. Timetabling procedures using a BBC microcomputer. [S.l: The Author], 1991.

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Barrett, Richard. Computerised timetabling - some of the options. Blagdon: The Staff College, 1991.

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Burke, Edmund, and Wilhelm Erben, eds. Practice and Theory of Automated Timetabling III. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44629-x.

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Burke, Edmund, and Michael Carter, eds. Practice and Theory of Automated Timetabling II. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0055877.

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Burke, Edmund K., and Hana Rudová, eds. Practice and Theory of Automated Timetabling VI. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-77345-0.

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Burke, Edmund, and Patrick De Causmaecker, eds. Practice and Theory of Automated Timetabling IV. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b11828.

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Book chapters on the topic "Timetabling"

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Schmidt, Marie E. "Timetabling." In Springer Optimization and Its Applications, 73–112. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9566-6_3.

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Carter, Michael W. "Timetabling." In Encyclopedia of Operations Research and Management Science, 1552–56. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1047.

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Kingston, Jeffrey H. "Educational Timetabling." In Studies in Computational Intelligence, 91–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39304-4_4.

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Picard, Gauthier, Carole Bernon, and Marie-Pierre Gleizes. "Emergent Timetabling Organization." In Multi-Agent Systems and Applications IV, 440–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11559221_44.

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Cacchiani, Valentina, and Paolo Toth. "Robust Train Timetabling." In Handbook of Optimization in the Railway Industry, 93–115. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72153-8_5.

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Frühwirth, Thom, and Slim Abdennadher. "University Course Timetabling." In Cognitive Technologies, 117–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05138-2_17.

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Pillay, Nelishia, and Rong Qu. "Examination Timetabling Problems." In Natural Computing Series, 75–82. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96514-7_10.

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Dige, Poul, Carlo Lund, and Hans F. Ravn. "Timetabling by Simulated Annealing." In Lecture Notes in Economics and Mathematical Systems, 151–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-46787-5_8.

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Soares, Pedro, and Nuno J. Mamede. "Timetabling using demand profiles." In Progress in Artificial Intelligence, 97–110. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0023914.

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Erben, Wilhelm. "Timetabling Using Genetic Algorithms." In Artificial Neural Nets and Genetic Algorithms, 30–32. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_10.

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Conference papers on the topic "Timetabling"

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Mansour, Nashat, and Hanaa El-Jazzar. "Curriculum based course timetabling." In 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6818082.

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Aizam, Nur Aidya Hanum binti, Zuliyana Farahi binti Ismail, and Paramesuaran Kovindan. "General mathematical model and timetabling problems: Expansion and application to real-time timetabling." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND TECHNOLOGY 2018 (MATHTECH2018): Innovative Technologies for Mathematics & Mathematics for Technological Innovation. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5136383.

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Cuupic, Marko, Marin Golub, and Domagoj Jakobovic. "Exam timetabling using genetic algorithm." In Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces (ITI). IEEE, 2009. http://dx.doi.org/10.1109/iti.2009.5196109.

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LI, Jiawei, and Tad Gonsalves. "Genetic Algorithm for Exam Timetabling Problem - A Specific Case for Japanese University Final Presentation Timetabling." In 8th International Conference on Computational Science and Engineering (CSE 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101701.

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This paper presents a Genetic Algorithm approach to solve a specific examination timetabling problem which is common in Japanese Universities. The model is programmed in Excel VBA programming language, which can be run on the Microsoft Office Excel worksheets directly. The model uses direct chromosome representation. To satisfy hard and soft constraints, constraint-based initialization operation, constraint-based crossover operation and penalty points system are implemented. To further improve the result quality of the algorithm, this paper designed an improvement called initial population pre-training. The proposed model was tested by the real data from Sophia University, Tokyo, Japan. The model shows acceptable results, and the comparison of results proves that the initial population pre-training approach can improve the result quality.

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"AN INTEGRATED SYSTEM FOR SCHOOL TIMETABLING." In 3rd International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003181805990603.

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Budiono, Tri A., and Kok Wai Wong. "Memetic algorithm behavior on timetabling infeasibility." In TENCON 2011 - 2011 IEEE Region 10 Conference. IEEE, 2011. http://dx.doi.org/10.1109/tencon.2011.6129070.

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Runarsson, Thomas Philip. "Improving Curriculum Timetabling Models Using Clustering." In 2015 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2015. http://dx.doi.org/10.1109/csci.2015.94.

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Qaurooni, Dan. "A memetic algorithm for course timetabling." In the 13th annual conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001576.2001637.

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El Den, Mohamed Bader, and Riccardo Poli. "Grammar-based genetic programming for timetabling." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983259.

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Rane, Mrunmayee V., Vikram M. Apte, Vishakha N. Nerkar, Mani Roja Edinburgh, and K. Y. Rajput. "Automated Timetabling System for University Course." In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2021. http://dx.doi.org/10.1109/esci50559.2021.9396906.

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