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Dissertations / Theses on the topic 'University course timetabling'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Lewis, Rhydian M. R. "Metaheuristics for university course timetabling." Thesis, Edinburgh Napier University, 2006. http://researchrepository.napier.ac.uk/Output/2392.

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The work presented in this thesis concerns the problem of timetabling at universities – particularly course-timetabling, and examines the various ways in which metaheuristic techniques might be applied to these sorts of problems. Using a popular benchmark version of a university course timetabling problem, we examine the implications of using a “twostaged” algorithmic approach, whereby in stage-one only the mandatory constraints are considered for satisfaction, with stage-two then being concerned with satisfying the remaining constraints but without re-breaking any of the mandatory constraints

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2

Autry, Brian M. "University course timetabling with probability collectives." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://bosun.nps.edu/uhtbin/hyperion-image.exe/08Mar%5FAutry.pdf.

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Thesis (M.S. in Computer Science)--Naval Postgraduate School, March 2008.<br>Thesis Advisor(s): Squire, Kevin. "March 2008." Description based on title screen as viewed on April 24, 2008. Includes bibliographical references (p. 33-35). Also available in print.

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3

Koshich, P. A. "University course timetabling of meta-heuristics." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433470.

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4

Naseem, Jat Sadaf. "Genetic algorithms for university course timetabling problems." Thesis, University of Leicester, 2012. http://hdl.handle.net/2381/10997.

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The university course timetabling problem is a difficult optimisation problem due to its highly-constrained nature. Finding an optimal, or even a high quality, timetable is a challenging task, especially when resources (e.g., rooms and time slots) are limited. In the literature, many approaches have been studied to solve this problem. In this thesis, we investigate genetic algorithms to solve the problem because they have been successfully used for a wide range of real-world problems. However, for university course timetabling problems, traditional genetic algorithms are not usually considered

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5

Zibran, Minhaz Fahim, and University of Lethbridge Faculty of Arts and Science. "A multi-phase approach to university course timetabling." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2007, 2007. http://hdl.handle.net/10133/633.

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Course timetabling is a well known constraint satisfaction optimization (CSOP) problem, which needs to be solved in educational institutions regularly. Unfortunately, this course timetabling problem is known to be NP-complete [7, 39]. This M.Sc. thesis presents a multi-phase approach to solve the university level course timetabling problem. We decompose the problem into several sub-problems with reduced complexity, which are solved in separate phases. In phase-1a we assign lectures to professors, phase-1b assigns labs and tutorials to academic assistances and graduate assistants. Phase-2 assig

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6

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

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7

Sandh, David, and Lucas Knutsäter. "University Course Scheduling Optimization under Uncertainty based on a Probability Model." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20353.

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In this thesis, we present a way to model uncertainty when optimizing the UniversityTimetabling Problem. It is an NP-hard, combinatorial and highly constrained problem.In this thesis, we first propose a standardized model based on the data from MalmöUniversity. Then, we propose our extended model, which, during the creation of the solution, accounts for the probability of unexpected events to occur and changes the solution accordingly. To implement our model, we use a Particle Swarm Optimization (PSO) algorithm.In our experiments, we find problems with the algorithm converging too early.We ana

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8

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

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9

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 metaheuristi

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10

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 implementa

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11

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

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12

Almay, Felix, and Oskar Strömberg. "Applicability of Constraint Solving and Simulated Annealing to Real-World Scale University Course Timetabling Problems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259761.

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The university course timetabling problem is the problem of creating a schedule for university courses under certain constraints. The decision variant of this optimisation problem is NP-complete. We have researched this problem and implemented the heuristic simulated annealing. This implementation has been compared with respect to time to the constraint solver CPSolver, based on iterative forward search. Our results show that CPSolver scales better for large problem instances. Simulated annealing as implemented by us is thus not suitable in itself for generating valid solutions to this problem

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13

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

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14

Renman, Casper, and Hampus Fristedt. "A comparative analysis of a Tabu Search and a Genetic Algorithm for solving a University Course Timetabling Problem." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166276.

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This work implements a Tabu search (TS) algorithm for solving an instance of the University Course Timetabling Problem (UCTP). The algorithm is com- pared to a Genetic algorithm (GA) implemented by Yamazaki and Pertoft (2014) that solves the same instance. The purpose of the work is to explore how the approaches dier for this particular instance, and specifically inves- tigate whether a TS algorithm alone is sucient, considering the small size of the UCTP instance. The TS was based on the OpenTS library (Harder, 2001) and reuses parts from Yamazaki and Pertoft’s (2014) GA. The results show tha

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15

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 ti

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16

Salman, Alzahraa, and Rouwayd Hanna. "A Comparative Study between Genetic Algorithm, Simulated Annealing and a Hybrid Algorithm for solving a University Course Timetabling Problem." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229432.

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Every year, universities are faced with the problem of having to schedule events to various resources such as lecturers, classrooms and time slots while considering different constraints. The University Course Timetabling Problem is a NP-complete combinatorial optimization problem that, if solved manually, requires great investment in time and money. Thus finding an algorithm that automates this process would prove beneficial for society. The aim of this thesis is to compare the performance of a Genetic Algorithm-Simulated Annealing hybrid implementation with the performance of each of the alg

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17

Norgren, Eric, and Johan Jonasson. "Investigating a Genetic Algorithm-Simulated Annealing Hybrid Applied to University Course Timetabling Problem : A Comparative Study Between Simulated Annealing Initialized with Genetic Algorithm, Genetic Algorithm and Simulated Annealing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186364.

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Every semester universities around the world have to create new schedules. This task can be very complex considering that a number of constraints has to be taken into account, e.g. there should not exist any timetable clashes for students and a room cannot be double-booked. This can be very hard and time-consuming for a human to do by hand, which is why methods to automate this problem, the University Course Timetabling Problem, has been researched for many years. This report investigates the performance of a hybrid consisting of Genetic Algorithm and Simulated Annealing when solving the Unive

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18

Hahn-Goldberg, Shoshana. "Defining, modeling, and solving a real university course timetabling problem." 2007. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=452884&T=F.

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19

Lay, Young-Jinn, and 賴永進. "University-level Automated Course Scheduling by Integrating AI Technique and Group Decision Support System - Group Negotiation Timetabling." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/88277612855266149559.

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碩士<br>大葉大學<br>電機工程研究所<br>82<br>University-level course scheduling is basically a multiple constraint satisfaction problem. It needs to rely on a preceding process to get a feasible solution satisfactory to almost all constraints and on a nogotiation process to achieve a all- satisfying solution. Researches in autometed course scheduling proposed various algorithms, empirical rules and reasoning thods. Proposals were differentiated by computation time and memory space usage, but they were no

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