Relevant bibliographies by topics / University timetabling problem

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  2. Dissertations / Theses
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Academic literature on the topic 'University timetabling problem'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "University timetabling problem"

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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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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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Shadkam, Elham. "An integer mathematical model for the problem of timetabling university exams." BEN Vol:2 Issue:3 2021 2, no. 3 (February 27, 2021): 11–15. http://dx.doi.org/10.36937/ben.2021.003.003.

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This research is an attempt to create optimized planning in educational units. The problem of university courses timetabling is one of the problems that is very important for educational units; establishing optimal distances to comply with students' study status as well as balancing other constraints of the timetabling problem is one of the challenges in a timetabling problem. Therefore, sometimes an educational unit may not be able to strike a good balance between all the constraints it faces and fail to achieve a proper timing table. In this paper, in order to achieve optimal exam timetabling with an integer scheduling approach, a model for exam timetabling is presented. The purpose of the proposed mathematical model is to maximize the appropriate time intervals that should be established between students' exams. In this mathematical model, according to the number of allowed exam days and the number of possible exam sessions per day, a number of positions have been considered and it is tried to assign these positions to the courses according to the courses related to the students of each entrance. The most important advantage of the proposed model is its simplicity while sufficient accuracy. Therefore, complex methods are not needed to solve this model.
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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 university course timetabling problem with the assignment of professor-course-time slot for an institution in Mexico. Timetabling is constructed for the disciplinary courses that are offered by one of the academic departments. The main characteristics are as follows: (1) there are full-time and part-time professors; (2) a mandatory fixed number of courses has to be assigned to each full-time professor according to their academic profile; (3) there is a maximum number of courses assigned to part-time professors; (4) a professor-course matrix that specifies the valid assignation is defined; and (5) mandatory time periods for courses in different semesters are established and other traditional constraints. We present the integer linear programming model proposed to solve the case studied. The optimal solution was obtained with low computational effort through the classical branch-and-bound algorithm. We describe the complete timetable to show the model effectiveness.
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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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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 (January 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 (ACO) algorithm approach has been developed for university course timetabling problem. ACO is believed to be a powerful solution approach for various combinatorial optimization problems. This approach is used according to the data set instances that have been collected. Its performance is presented using the appropriate algorithm. The results are arguably within the best results range from the literature. The performance assessment and results are used to determine whether they are reliable in preparing a qualifying course timetabling process.</p>
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Hambali, A. M., Y. A. Olasupo, and M. Dalhatu. "Automated university lecture timetable using Heuristic Approach." Nigerian Journal of Technology 39, no. 1 (April 2, 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), Course Timetabling, Space Complexity.
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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 (April 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 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.
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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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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 (October 19, 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 Language (UML), and we validated the model while using the state-of-the-art tool USE: UML-based Specification Environment. The solution strategy tackles the problem in two stages: (1) ACA: academic assignments, i.e., assign lectures to professors and (2) TTP: the timetabling process. We developed a Tabu Search customization named Tabu Search with Probabilistic Aspiration Criterion (TS-PAC) in order to solve the timetabling problem, and we developed a software prototype to test our proposal. Two feasible timetables for two different semesters were obtained according to the modeled constraints.
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Dissertations / Theses on the topic "University timetabling problem"

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Bucco, Guilherme Brandelli. "Construção de um modelo de programação linear para o University Timetabling Problem." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/101491.

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A construção de grades horárias dos cursos de uma universidade é um problema que deve ser enfrentado no início de todos os semestres e, por mobilizar quantidades significativas de recursos, se constitui numa das mais importantes tarefas administrativas de uma universidade. Trata-se de um problema clássico, combinatório, que tem atraído atenção por conta da dificuldade de se encontrar boas soluções. É classificado, em termos de complexidade computacional, como NP-hard, o que implica grande exigência de capacidade de processamento. É modelado de maneiras muito diversas, no intuito de se obter adequação quanto ao contexto educacional do país, às regras específicas da instituição ou aos objetivos específicos dos gestores, entre outros. Foi feita uma revisão de literatura no intuito de apoiar a modelagem do problema, nesse trabalho, e de contribuir com a comunidade de pesquisadores sobre o tema ao agregar informações a respeito das pesquisas publicadas até então. O problema é modelado, neste trabalho, por meio de técnicas de Pesquisa Operacional com o objetivo de produzir grades horárias com aulas distribuídas uniformemente ao longo da semana, em uma primeira etapa, para que, na etapa seguinte, ao se atribuir salas de aula às turmas, a utilização dos espaços físicos da Universidade seja otimizada. Dados foram coletados de uma instituição federal de ensino superior para a implementação do modelo. Resultados obtidos no processamento com os dados reais mostraram que o modelo reduz consideravelmente a utilização de salas de aula.
The timetabling construction for University courses is a problem that must be faced at each beginning of semester and, since it mobilizes significant amounts of resources, it constitutes in one of the most important administrative tasks in a University. It's a classic, combinatorial problem that has attracted attention due to its difficulty in finding good solutions. In terms of computational complexity, it's classified as NP-hard, which involves great processing capacity. It's modeled in a number of different ways, aimed to obtain adequacy to the educational context of the country, to the specific higher education institutional rules, or to the specific managers goals, amongst others. A literature review was performed, aimed to support, in this research, the problems modeling, and to contribute to the researchers community, adding the research information published so far. The problem is modeled, in this work, by means of Operations Research techniques, aiming to produce evenly distributed timetables along the week, in the first step, and to assign the classrooms to the groups of students in the next, in such a way that the physical spaces utilization of the University is optimized. Data was collected from a federal higher education institution in order to implement de model. Results obtained through its processing with this data showed that the model considerably reduces the classrooms utilization.
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Andersson, Isabella, and Carl Petter Svensson. "Comparing Two-Phase Hybrid Metaheuristics for the University Course Timetabling Problem (UCTP)." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259689.

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Timetabling is a time consuming and difficult task for large organizations. One popular research field is the university course timetabling problem (UCTP). UCTP is the NP-hard combinatorial problem of scheduling courses at a university while satisfying some constraints. In most definitions of the UCTP, there are hard constraints that defines what a valid timetable is and there are soft constraints that are only desired features of the timetable. In this research, a few different hybrid methods for solving the UCTP are tested and compared. The hybrids tested are combinations of the metaheuristics simulated annealing, tabu search and iterated local search. The approach is to first use one of these methods to find a valid timetable that is not breaking any hard constraint. Then, a second phase begins that is attempting to minimize the soft constraint violations. The results showed that simulated annealing was the fastest method for removing all hard constraint violations. Given a partial solution solved by simulated annealing, iterated local search was found to minimize soft constraint violations most successfully within the time limit for all tested sizes of problem instances. It was found that this hybrid in two phases could give better solutions than only using simulated annealing.
Schemaläggning är en tidskrävande och svår uppgift för stora organisationer. Schemaläggningsproblemet UCTP är ett NP-svårt kombinatoriskt problem som går ut på att, med hjälp av en dator, lägga ett schema för ett universitet. Schemat måste också följa vissa regler och begränsningar för hur ett schema får se ut. I de flesta definitionerna av UCTP-problemet så finns det en uppdelning mellan hårda och mjuka begränsningar. För att ett schema ska vara giltigt så får det inte finnas några brott mot de hårda begränsningarna. De mjuka begränsningarna däremot är bara önskvärda egenskaper för ett schema. I denna rapport har vi jämfört olika hybridmetoder av metaheuristiker för att lösa UCTP. De hybrider som undersökts är olika kombinationer av simulerad härdning, itererad lokalsökning och tabusökning. Först används en av dessa algoritmer för att hitta en halvfärdig lösning som inte bryter mot några hårda begränsningar. Denna lösning ges sedan till en andra fas där olika metaheuristiker jämförs utifrån hur bra de begränsar brotten mot de mjuka begränsningarna. Resultaten visade att simulerad härdning var snabbast för att hitta en giltig lösning till UCTP. Givet en påbörjad lösning från simulerad härdning, så lyckades itererad lokalsökning minimera brotten mot svaga begränsningar mest framgångsrikt inom tidsgränsen för alla storlekar på probleminstanser som testades. Slutsatsen blev att en hybrid i två faser kunde ge bättre lösningar än att endast använda simulerad härdning för schemaläggningsproblemet UCTP.
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Abdul, Rahim Siti Khatijah Nor. "Transformation of the university examination timetabling problem space through data pre-processing." Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/28895/.

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This research investigates Examination Timetabling or Scheduling, with the aim of producing good quality, feasible timetables that satisfy hard constraints and various soft constraints. A novel approach to scheduling, that of transformation of the problem space, has been developed and evaluated for its effectiveness. The examination scheduling problem involves many constraints due to many relationships between students and exams, making it complex and expensive in terms of time and resources. Despite the extensive research in this area, it has been observed that most of the published methods do not produce good quality timetables consistently due to the utilisation of random-search. In this research we have avoided random-search and instead have proposed a systematic, deterministic approach to solving the examination scheduling problem. We pre-process data and constraints to generate more meaningful aggregated data constructs with better expressive power that minimise the need for cross-referencing original student and exam data at a later stage. Using such aggregated data and custom-designed mechanisms, the timetable construction is done systematically, while assuring its feasibility. Later, the timetable is optimized to improve the quality, focusing on maximizing the gap between consecutive exams. Our solution is always reproducible and displays a deterministic optimization pattern on all benchmark datasets. Transformation of the problem space into new aggregated data constructs through pre-processing represents the key novel contribution of this research.
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Lehman, Jeffrey L. "An Extensible Markup Language (XML) Application for the University Course Timetabling Problem." NSUWorks, 2004. http://nsuworks.nova.edu/gscis_etd/666.

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The university course timetabling problem involves the assignment of instructors, courses, and course sections to meeting rooms, dates, and times. Timetabling research has generally focused on the algorithms and techniques for solving specific scheduling problems. The independent evaluation and comparison of timetabling problems and solutions is limited by the lack of a standard timetabling language. This dissertation created an Extensible Markup Language (XML) application, called Course Markup Language (CourseML), for the university course timetabling problem. CourseML addressed the need for a standardized timetabling language to facilitate the efficient exchange of timetabling data and provided a means for the independent evaluation and comparison of time tabling problems and solutions. A sample real-world university course timetabling problem was defined. CourseML was used to define the sample problem. CourseML was evaluated based on how well it captured the sample problem, including hard and soft constraints, and how well it represented a solution instance. The qualities that made CourseML a candidate for general use were identified. The set of characteristics that made XML an appropriate language for specifying university course timetabling problems and solutions were identified.
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Chammas, Kristoffer, and Simon Sirak. "An Evaluation of the Great Deluge Algorithm in Course Timetabling : As Applied to the KTH-Inspired University Course Timetabling Problem." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259907.

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The University Course Timetabling Problem (UCTP) can be loosely described as assigning events (e.g lectures) to rooms and timeslots in a way that results in a feasible timetable that is optimal according to some custom criteria. The problem has become increasingly relevant as more programs become available in universities. Due to the complexity of UCTP, the problem is usually solved approximately using heuristics. The KTH-inspired UCTP is a variant of the UCTP that is adapted to KTH Royal Institute of Technology. However, few heuristics have been implemented for this variant of UCTP. Therefore, this study introduces an implementation of The Great Deluge heuristic to the KTH-inspired UCTP, and compares it to a state-of-the-art solver for KTH-inspired UCTP. The Great Deluge implementation was compared against the state-of-the-art KTH-inspired UCTP solver for different time limits. For each time limit, the output timetable quality was recorded over several executions. The comparison was done on two problem instances of varying complexity. The results suggest a behavior that varies over time. For larger time limits, GD produced better timetables than the state-of-the-art and the overall quality of timetables was consistent over several executions. For smaller time limits, GD produced worse timetables than the state-of-the-art and the overall quality of timetables was inconsistent over several executions. A few potential causes for the improved performance during the later stages of execution were found through further analysis of the results. Perhaps the biggest potential cause was utilizing the greedy behavior obtained during the mid to late stages of execution.
”The University Course Timetabling Problem” (UCTP) handlar i grova drag om att, baserat på ett antal kriterier, schemalägga föreläsningar, övningar och laborationer på ett optimalt sätt. Problemets relevans har ökat allt eftersom universitet utökar sina programutbud. På grund av komplexiteten hos UCTP löses problemet vanligtvis approximativt med hjälp av heuristiker. ”KTH-inspired UCTP” är en KTH-anpassad variant av UCTP för vilken endast ett fåtal heuristiker har implementerats. Denna variant har exempelvis inte lösts av en vanlig heuristik inom UCTP, ”The Great Deluge” (GD). Denna studie fokuserar därför på att applicera GD på ”KTH-inspired UCTP” och jämföra denna med äldre implementationer, med fokus på den bästa tillgängliga implementationen. GD-implementationen jämförs med den bästa tillgängliga implementationen för ”KTH-inspired UCTP” för olika tidsgränser. Kvaliteten hos de resulterande schemana evalueras och sparas sedan över flera körningar. Jämförelsen gjordes på två probleminstanser av olika komplexitet. Resultatet av jämförelsen föreslår att GD producerade bättre scheman för högre tidsgränser men sämre scheman för lägre tidsgränser. Vidare analys föreslår att denna förbättring beror på utnyttjandet av det giriga beteendet som vår GD-implementation uppvisar vid senare delar av exekvering.
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Forsberg, Mikael. "Local search hybridization of a genetic algorithm for solving the University Course Timetabling Problem." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229677.

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The University Course Timetabling Problem (UCTP) is the problem of assigning locations (lecture halls, computer rooms) and time slots (time and date) to a set of events (lectures, labs) while satisfying a number of constraints such as avoiding double-bookings. Many variants of problem formulations exist, and most realistic variants are thought to be NP-hard. A recent trend in solving hard scheduling problems lies in the application of hybrid metaheuristics, where improvements are often found by hybridizing a population-based approach with some form of local search. In this paper, an implementation of a Genetic Algorithm (GA) that solves the UCTP is hybridized with local search in the form of Tabu Search (TS). The results show significant improvements to the performance and scalability over the non-hybridized GA. Two application strategies for the TS are investigated. The first strategy performs a switch-over from the GA to the TS, while the second interleaves the two algorithms. The effectiveness of each application strategy is seen to depend on the characteristics of the individual algorithms.
Schemaläggningsproblemet UCTP (University Course Timetabling Problem) består av problemet att tilldela platser (föreläsningssalar, laborationssalar) och tidpunkter (datum och klockslag) till en mängd tillställningar (föreläsningar, laborationer) under kravet att upprätthålla en mängd restriktioner, exempelvis att undvika dubbelbokningar. Det finns många varianter av problemformuleringen och de flesta realistiska formuleringer anses ge upphov till NP-svåra optimeringsproblem. En förhållandevis ny trend för lösningsmodeller till svåra schemaläggningsproblem ligger i tillämpningen av hybrida metaheuristiker, där förbättringar ofta ses när populationsbaserade algoritmer kombineras med någon typ av lokalsökning. I denna rapport undersöks en UCTP-lösning baserad på en Genetisk Algoritm (GA) som hybridiseratsmed en lokalsökning i form av en Tabusökning (TS). Resultaten visar på signifikanta förbättringar i prestanda och skalbarhet jämfört med den icke-hybridiserade GA:n. Två appliceringsstrategier för TS undersöks. Den första strategin utgörs av att byta algoritm från GA till TS, medan den andra utgörs av att sammanfläta de två algoritmerna. Appliceringsstrategiernas effektivitet ses bero av de individuella algoritmernas egenskaper.
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Berggren, Robert, and Timmy Nielsen. "Investigating the Reliability of Known University Course Timetabling Problem Solving Algorithms with Updated Constraints." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229695.

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Scheduling lectures, exams, seminars etc. for a university turns out to be a harder task than what it seems to be at first glance. This problem is known as the University Course Timetabling Problem (UCTP). The UCTP has been hosted for a number of competitions throughout the years by an organization called Practice and Theory of Automated Timetabling (PATAT). Because of these competitions, the problem has been given a standard description and set of constraints as well as standard problem instances for easier comparison of research and work on the subject. However, setting a standard like this have a major drawback; no variety is introduced since new research for finding the greatest method to solve the UCTP is forced to focus on a specific set of constraints, and algorithms developed will only be optimized with these constraints in consideration. In this research we compared five well known UCTP algorithms with the standard set of constraints to a different set of constraints. The comparisons showed a difference in the rank of performance between the algorithms when some constraints were changed to fit a certain need. The differences were not great but big enough to state that previous research declaring what algorithms are best for the UCTP problem cannot be relied upon unless you use close to identical sets of constraints. If the goal is to find the best algorithm for a new set of constraints then one should not rely on a single previously defined great algorithm but instead take two or three of the top performing ones for the greatest chance of finding the most optimized solution possible.
Schemaläggning av föreläsningar, tentamen, seminarier etc. för ett universitet visar sig vara en svårare uppgift än vad det verkar vid första anblicken. Detta problem är känt som University Course Timetabling Problem (UCTP). UCTP har varit centralt i ett antal tävlingar genom åren av organisationen Practice and Theory of Automated Timetabling (PATAT). På grund av dessa tävlingar har problemet fått en standardbeskrivning och en uppsättning specifika begränsningar samt standard problemdata för enklare jämförelse av forskning och arbete i ämnet. Att sätta denna typ av standard har dock en stor nackdel; ingen variation tillförs då ny forskning för att hitta den bästa optimeringsmetoden inom UCTP tvingas att fokusera på en specifik uppsättning begränsningar och algoritmer som utvecklas kommer då endast att optimeras med dessa begränsningar i beaktande. I den här rapporten jämförde vi fem välkända UCTP algoritmer med standarduppsättningen av begränsningar mot en annan uppsättning begränsningar. Jämförelserna visade en skillnad i prestationsordningen mellan algoritmerna när vissa begränsningar ändrats för att passa ett visst behov. Skillnaderna var inte enorma men tillräckligt stora för att påvisa att tidigare forskning som förklarar vilka algoritmer som är bäst för UCTP-problemet ej är pålitlig om du inte använder nära till identiska uppsättningar av begränsningar. Om målet är att hitta den bästa algoritmen för en ny uppsättning begränsningar, bör man inte lita på en tidigare definierad effektiv algoritm utan istället använda sig utav två eller tre av de starkaste algoritmerna för den största chansen att hitta den mest optimerade lösningen.
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8

Fredrikson, Rasmus, and Jonas Dahl. "A comparative study between a simulated annealing and a genetic algorithm for solving a university timetabling problem." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187158.

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Abstract:
The university timetabling problem is an NP-complete problem which schools all over the world face every semester. The aim of the problem is to schedule sets of events such as lectures and seminars into certain time slots without violating numerous specified constraints. This study aimed to automate this process with the help of simulated annealing and compare the results with a genetic algorithm. The input data sets were inspired by the Royal Institute of Technology in Stockholm. The results showed a great run time difference between the two algorithms where the simulated annealing performed much better. They also showed that even though the simulated annealing algorithm was better during all stages, the genetic algorithm had a much better performance in early stages than it had in latter. This led to the conclusion that a more optimized, hybrid algorithm could be created from the two algorithms provided that the genetic algorithm could benefit from the improvements suggested in previous research.
Universitetsschemaläggningsproblemet är ett NP-fullständigt problem som skolor över hela världen måste hantera innan varje termin. Syftet med problemet är att schemalägga händelser, såsom föreläsningar och seminarier, utan att bryta flertalet fördefinierade villkor. Denna studie hade som mål att automatisera denna process med hjälp av algoritmkonstuktionsmetoden simulerad glödgning och sedan jämföra resultatet med en genetisk algoritm. De datamängder som användes är inspirerade av den verkliga situationen på KTH. Resultaten visar stora tidsmässiga skillnader där algoritmen baserad på simulerad glödgning går snabbare. De visar dock också att den genetiska algoritmen har en bättre prestanda i tidigare stadier än i senare. Detta ledde till slutsatsen att en mer optimerad hybridalgoritm kan skapas av de två algoritmerna, förutsatt att den genetiska algoritmen kan dra nytta av förbättringar som föreslagits i tidigare forskning.
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9

Wang, Yuqiang. "Models and Algorithms for Some Combinatorial Optimization Problems: University Course Timetabling, Facility Layout and Integrated Production-Distribution Scheduling." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28757.

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In this dissertation, we address three different combinatorial optimization problems (COPs), each of which has specific real-life applications. Owning to their specific nature, these problems are different from those discussed in the literature. For each of these problems, we present a mathematical programming formulation, analyze the problem to determine its useful, inherent structural properties, and develop an efficient methodology for its solution by exploiting these properties. The first problem that we address is the course timetabling problem encountered at Virginia Tech. The course timetabling problem for a university is a difficult problem and has been studied by many researchers over the years. As a result, a plethora of models and approaches have been reported in the literature. However, most of these studies have focused on applications pertaining to course scheduling for a single or at most few departments of a university. The sheer size of the university-wide timetabling problem that we address, involving thousands of courses to be scheduled in hundreds of classrooms in each semester, makes it a challenging problem. We employ an appropriate decomposition technique that relies on some inherent structural properties of the problem both during the modeling and algorithmic development phases. We show the superiority of the schedules generated by our methodology over those that are currently being used. Also, our methodology requires only a reasonable amount of computational time in solving this large-size problem. A facility layout problem involving arbitrary-shaped departments is the second problem that we investigate in this dissertation. We designate this problem as the arbitrary-shaped facility layout problem (ASFLP). The ASFLP deals with arranging a given set of departments (facilities, workstations, machines) within the confines of a given floor space, in order to optimize a desired metric, which invariably relates to the material handling cost. The topic of facility planning has been addressed rather extensively in the literature. However, a major limitation of most of the work reported in the literature is that they assume the shape of a department to be a rectangle (or even a square). The approach that relies on approximating an arbitrary-shaped department by a rectangle might result in an unattractive solution. The key research questions for the ASFLP are: (1) how to accurately model the arbitrary-shaped departments, and (2) how to effectively and efficiently determine the desired layout. We present a mixed-integer programming model that maintains the arbitrary shapes of the departments. We use a meta-heuristic to solve the large-size instances of the ASFLP in a reasonable amount of time. The third problem that we investigate is a supply chain scheduling problem. This problem involves two stages of a supply chain, specifically, a manufacturer and one or more customers. The key issue is to achieve an appropriate coordination between the production and distribution functions of the manufacturer so as to minimize the sum of the shipping and job tardiness costs. We, first, address a single customer problem, and then, extend our analysis to the case of multiple customers. For the single-customer problem, we present a polynomial-time algorithm to solve it to optimality. For the multiple-customer problem, we prove that this problem is NP-hard and solve it by appropriately decomposing it into subproblems, one of which is solvable in polynomial time. We propose a branch-and-bound-based methodology for this problem that exploits its structural properties. Results of an extensive computational experimentation are presented that show the following: (1) our algorithms are efficient to use and effective to implement; and (2) significant benefits accrue as a result of integrating the production and distribution functions.
Ph. D.
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10

Broberg, Felix, and Emelie Eriksson. "Comparing MAX-MIN and Rank-based Ant Colony Optimization Algorithms for solving the University Course Timetabling Problem." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229790.

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The University Course Timetabling Problem (UCTP) is a scheduling problem regarding courses, time slots and rooms, and is often accompanied by a set of feature requirements. As non-trivial instances of the UCTP are NP-hard, traditional computational methods are ineffective. A meta-heuristic alternative is the Ant Colony Optimization (ACO) algorithm, which has previously been proven to successfully solve the UCTP. This paper investigates the relative effectiveness of the MAX-MIN ACO variation to the Rank-based ACO variation on UCTP problem sets of varying difficulty. They are also compared when using a local-search help function and a path attractiveness heuristic. This study shows that the ACO variations perform similarly well for all problem difficulties when utilizing the path attractiveness heuristic. Utilizing both local search and the heuristic produces the best results across the difficulties and ACO variations. There is, however, a need for further investigation into the parameters for both ACO variations to ensure the validity of the conclusion.
Det universitetsbaserade schemaläggningsproblemet (UCTP) avser schemaläggning av kurser till rum och tider där hänsyn till en mängd funktionella krav ofta måste tas. Traditionella beräkningsmetoder har visats vara ineffektiva då icke-triviala fall av problemet är NP-svåra. Myrkolonisystemsalgoritmen (ACO) är ett meta-heuristiskt alternativ som framgångsrikt har använts för att lösa UCTP. Denna rapport jämför effektiviteten mellan MAX-MIN ACO-variationen och den Rank-baserade ACO-variationen i att hitta lösningar till UCTP. Variationerna jämförs också vid använding av "local search" och en vägvalsheuristik. Rapporten visar att ACO-variationerna presterar likvärdigt vid användande av vägvalsheuristiken. Användandet av både "local search" och vägvalsheuristiken leder till bästa resultat för samtliga svårighetsgrader och ACO-variationer. Efterforskning krävs angående parametrarna för ACO-variationerna för att säkerställa giltigheten av slutsatserna.
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Books on the topic "University timetabling problem"

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Hahn-Goldberg, Shoshana. Defining, modeling, and solving a real university course timetabling problem. 2007.

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Book chapters on the topic "University timetabling problem"

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Mühlenthaler, Moritz. "The University Course Timetabling Problem." In Lecture Notes in Economics and Mathematical Systems, 11–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12799-6_2.

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Kampke, Edmar Hell, Erika Almeida Segatto, Maria Claudia Silva Boeres, Maria Cristina Rangel, and Geraldo Regis Mauri. "Neighborhood Analysis on the University Timetabling Problem." In Computational Science and Its Applications – ICCSA 2017, 148–64. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62398-6_11.

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Nguyen, Khang, Tien Lu, Trung Le, and Nuong Tran. "Memetic Algorithm for a University Course Timetabling Problem." In Informatics in Control, Automation and Robotics, 67–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25899-2_10.

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Kostuch, Philipp, and Krzysztof Socha. "Hardness Prediction for the University Course Timetabling Problem." In Evolutionary Computation in Combinatorial Optimization, 135–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24652-7_14.

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Nagata, Yuichi, and Isao Ono. "Random Partial Neighborhood Search for University Course Timetabling Problem." In Parallel Problem Solving from Nature – PPSN XIII, 782–91. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10762-2_77.

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Kostuch, Philipp. "The University Course Timetabling Problem with a Three-Phase Approach." In Practice and Theory of Automated Timetabling V, 109–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11593577_7.

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Thomas, J. Joshua, Ahamad Tajudin Khader, and Bahari Belaton. "Information visualization Approach on the University Examination Timetabling Problem." In Visual Information Communication, 255–64. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0312-9_17.

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Datta, Dilip, Kalyanmoy Deb, and Carlos M. Fonseca. "Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem." In Evolutionary Scheduling, 197–236. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48584-1_8.

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Rodriguez-Maya, Noel, Juan J. Flores, and Mario Graff. "Predicting the RCGA Performance for the University Course Timetabling Problem." In Intelligent Computing Systems, 31–45. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30447-2_3.

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Aldeeb, Bashar A., Norita Md Norwawi, Mohammed A. Al-Betar, and Mohd Zalisham Bin Jali. "Solving University Examination Timetabling Problem Using Intelligent Water Drops Algorithm." In Swarm, Evolutionary, and Memetic Computing, 187–200. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20294-5_17.

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Conference papers on the topic "University timetabling problem"

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Turabieh, Hamza, and Esam El-Daoud. "University course timetabling problem at Zarqa University." In the 3rd International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2222444.2222452.

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Nouri, H. E., and O. B. Driss. "Distributed model for university course timetabling problem." In 2013 International Conference on Computer Applications Technology (ICCAT 2013). IEEE, 2013. http://dx.doi.org/10.1109/iccat.2013.6521990.

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Van Hoai, Nguyen, Luu Thanh Cong, and Pham Quang Dung. "Solving the TimeTabling problem at FPT University." In SoICT 2015: The Sixth International Symposium on Information and Communication Technology. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2833258.2833311.

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Ahandani, Morteza Alinia, and Mohammad Taghi Vakil Baghmisheh. "Memetic algorithms for solving university course timetabling problem." In 2011 International eConference on Computer and Knowledge Engineering (ICCKE). IEEE, 2011. http://dx.doi.org/10.1109/iccke.2011.6413321.

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Davoudzadeh, Maboud, Reza Rafeh, and Roya Rashidi. "A Linear Solution for the University Timetabling Problem." In 2009 Second International Conference on Computer and Electrical Engineering. IEEE, 2009. http://dx.doi.org/10.1109/iccee.2009.131.

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Jat, Sadaf N., and Shengxiang Yang. "A Memetic Algorithm for the University Course Timetabling Problem." In 2008 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2008. http://dx.doi.org/10.1109/ictai.2008.126.

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Wang, Zan, Jin-lan Liu, and Xue Yu. "Self-fertilization based genetic algorithm for university timetabling problem." In the first ACM/SIGEVO Summit. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1543834.1543993.

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Irene, Sheau Fen Ho, Safaai Deris, and Mohd Hashim Siti Zaiton. "A Study on PSO-Based University Course Timetabling Problem." In 2009 International Conference on Advanced Computer Control (ICACC). IEEE, 2009. http://dx.doi.org/10.1109/icacc.2009.112.

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Al-Betar, Mohammed Azmi, Ahamad Tajudin Khader, and Osama Muslih. "A multiswap algorithm for the university course timetabling problem." In 2012 International Conference on Computer & Information Science (ICCIS). IEEE, 2012. http://dx.doi.org/10.1109/iccisci.2012.6297258.

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Junli, Liu, Chen Shuangxi, Mao Jie, and Huang Xuefeng. "Genetic algorithm study on the university course timetabling problem." In 2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2012. http://dx.doi.org/10.1109/cyber.2012.6319917.

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