Journal articles: '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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Ruslaan, Mohd Asyraf, and Zalmiyah Zakaria. "University Course Timetabling System For Part-Time Students." International Journal of Advanced Science Computing and Engineering 1, no. 2 (September 2, 2019): 68–75. http://dx.doi.org/10.30630/ijasce.1.2.5.

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University timetabling system is a part of a timetabling problem that aims to produce course timetable that meets student needs such as the maximum number of subjects that can be offered, the maximum number of elective subjects that can be offered and the number of subject students can take. In every semester, the timetabling process in UTMSPACE is done manually where there are likely to be a small number of students who will have problems because the subject to be taken is not in the subjects offering list. Additionally, the number of subjects offered is also not optimal and this will result in a loss on UTMSPACE because each subject is offered at a cost. Therefore, in order to solve this problem, the Heuristic-based approach is used to overcome the problems mentioned and speed up the process to generate timetable. Heuristic engines have been developed using PHP language programming. This approach has been successfully tested and implemented using real-time scheduling data at UTMSPACE for Software Engineering course. The results show that Heuristics has successfully solved the problem of producing a timetable without affecting students who want to enroll the subject offered.

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Küçükoğlu, İlker, and Alkın Yurtkuran. "Heuristic and genetic algorithm approaches to the real-world university examination timetabling problem." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (April 12, 2017): 143–51. http://dx.doi.org/10.18844/gjbem.v7i1.1409.

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Timetabling is one of the computationally difficult problems in scheduling and aims to find best time slots for a number of tasks which require limited resources. In this paper, we examine different solution approaches for the real-world examination timetabling problem (ETP) for university courses. The problem has unique hard and soft constraints, when compared to previous efforts, i.e. consecutive exams, sharing of rooms, room preferences, room capacity and number of empty slots. The aim of the problem is to achieve a timetable, which minimizes the total number of the examination slots without any conflicts. First, the real-world problem is formally defined and a mixed integer linear model is presented. Then, a constructive heuristic and a genetic algorithm based meta-heuristic are proposed in order to solve the ETP. Proposed approaches are tested on a problem set formed by using a real-life data. Results reveal that, proposed approaches are able to produce superior solutions in a limited time. Keywords: Timetabling, constructive heuristic, genetic algorithm;

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

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Problem of courses timetabling is a time consuming and demanding issues in any education environment that they are involved in every semester. The main aim of timetabling problem is the allocation of a number of courses to a limited set of resources such as classrooms, time slots, professors and students so that some predefined hard and soft constraints are satisfied. Furthermore, the available resources are used to the best.Â Â Â In fact course timetabling is one of optimization problems. It has been proved computational complexity of this problem is NP, so there is no optimal solution for that. Therefore, approximation and heuristic techniques are used to find near optimal solutions. Genetic algorithm for its multidirectional feature has been one of the most widely used approaches in recent years. Hence, in this paper an improved genetics algorithm for timetabling problem has been proposed. In proposed algorithm, the fitness of solutions to satisfy soft constraints due to ambiguous nature of those has been specified using fuzzy logic. Also, local search methods have been applied to avoid the genetic algorithm to be trapped in a local optimum. As well as, the multi-population property is intended to reduce the time to reach the optimum solution. Â Evaluation results show that the proposed solutions are able to produce promising results for the university courses timetabling.

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Siame, Alinaswe, and Douglas Kunda. "University Course Timetabling using Bayesian based Optimization Algorithm." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 6, no. 2 (August 29, 2018): 14. http://dx.doi.org/10.3991/ijes.v6i2.8990.

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

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Khair, Ahmad Firdaus, Mokhairi Makhtar, Munirah Mazlan, Mohamad Afendee Mohamed, and Mohd Nordin Abdul Rahman. "An ant colony algorithm for universiti sultan zainal abidin examination timetabling problem." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (January 1, 2019): 191. http://dx.doi.org/10.11591/ijeecs.v13.i1.pp191-198.

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The real-life construction of examination timetabling problem is considered as a common problem that always encountered and experienced in educational institution whether in school, college, and university. This problem is usually experienced by the academic management department where they have trouble to handle complexity for assign examination into a suitable timeslot manually. In this paper, an algorithm approach of ant colony optimisation (ACO) is presented to find an effective solution for dealing with Universiti Sultan Zainal Abidin (UniSZA) examination timetabling problems. A combination of heuristic with ACO algorithm contributes the development solution in order to simplify and optimize the pheromone occurrence of matrix updates which include the constraints problem. The implementation of real dataset instances from academic management is applied to the approach for generating the result of examination timetable. The result and performance that obtained will be used for further use to evaluate the quality and observe the solution whether our examination timetabling system is reliable and efficient than the manual management that can deal the constraints problem.

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Haitan, О., and О. Nazarov. "HYBRID APPROACH TO SOLVING OF THE AUTOMATED TIMETABLING PROBLEM IN HIGHER EDUCATIONAL INSTITUTION." Системи управління, навігації та зв’язку. Збірник наукових праць 2, no. 60 (May 28, 2020): 60–69. http://dx.doi.org/10.26906/sunz.2020.2.060.

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The paper describes a hybrid approach to solving of the automated timetabling problem in higher educational institution based on the ant colony optimization, the genetic algorithm, and the Nelder–Mead method. The ant colony method is the basis of this algorithm, which forms the initial population for the genetic algorithm. The combination of this method with the genetic algorithm and the Nelder–Mead method reduces time of the convergence of an algorithm and eliminates the strong dependence of the results on the initial search parameters, which usually are selected experimentally. The Nelder–Mead method is used to find the parameters of the ant colony optimization method. Use of the genetic algorithm allows for reducing of algorithm running time and increasing of global optimum finding probability. The educational process timetabling in higher school is an important component of the educational process assurance system, since the schedule quality determines the comfort of the educational process participants and its quality and effectiveness. Therefore, the development of methods for computer-aided timetable generation is an important challenge. The subject of study is adaptive methods of automated university timetabling. The objective of the work is development of a hybrid approach to addressing the problem of automated timetabling in university. The results are development and research of a hybrid method and software for university timetabling that been implemented this method

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Adewumi, Aderemi O., Babatunde A. Sawyerr, and M. Montaz Ali. "A heuristic solution to the university timetabling problem." Engineering Computations 26, no. 8 (November 13, 2009): 972–84. http://dx.doi.org/10.1108/02644400910996853.

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Kubiak, Wiesław. "On a conjecture for the university timetabling problem." Discrete Applied Mathematics 299 (August 2021): 26–49. http://dx.doi.org/10.1016/j.dam.2021.04.010.

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

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University course timetabling is a complex problem which must satisfy a list of constraints in order to allocate the right timeslots and venues for various courses. The challenge is to make the NP-hard problem user-friendly, highly interactive and faster run time complexity of algorithm. The objective of the paper is to propose Particle Swarm Optimization (PSO) timetabling model for Undergraduate Information and Communication Technology (ICT) courses. The PSO model satisfies hard constraints with minimal violation of soft constraints. Empirical results show that the rds: NP hard problem, timetabling, particle swarm optimization

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Nourmohammadi-Khiarak, Jalil, Yashar Zamani-Harghalani, and Mohammad-Reza Feizi-Derakhshi. "Combined Multi-Agent Method to Control Inter-Department Common Events Collision for University Courses Timetabling." Journal of Intelligent Systems 29, no. 1 (December 21, 2017): 110–26. http://dx.doi.org/10.1515/jisys-2017-0249.

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

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Limota, Ushindi, Egbert Mujuni, and Allen Mushi. "Solving the University course timetabling problem using bat inspired algorithm." Tanzania Journal of Science 47, no. 2 (May 19, 2021): 674–85. http://dx.doi.org/10.4314/tjs.v47i2.23.

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Many mathematical optimization problems from real-life applications are NP-hard, and hence no algorithm that solves them to optimality within a reasonable time is known. For this reason, metaheuristic methods are mostly preferred when their size is big. Many meta-heuristic methods have been proposed to solve various combinatorial optimization problems. One of the newly introduced metaheuristic methods is a bat-inspired algorithm, which is based on the echolocation behaviour of microbats. Bat algorithm (BA) and its variants have been used successfully to solve several optimization problems. However, from the No-free Lunch Theorem, it is known that there is no universal metaheuristic method that can solve efficiently all optimization problems. Thus, this study work focused on investigating the usefulness of BA in solving an optimization problem called Course Teaching Problem (CTP). Since BA was originally designed to solve continuous problems, and CTP is a combinatorial optimization problem, a discrete version of BA for CPT has been proposed and tested using real-life data from the Dar es Salaam University College of Education (DUCE). The algorithm has produced promising results, as in each execution test, it generated a timetable in which all hard constraints were met and soft constraints were significantly satisfied within a few iterations. Keywords: Combinatorial optimization, Timetabling problem, Metaheuristic algorithms, Bat algorithm.

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Berisha, Artan, Eliot Bytyçi, and Ardeshir Tershnjaku. "Parallel Genetic Algorithms for University Scheduling Problem." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 2 (April 1, 2017): 1096. http://dx.doi.org/10.11591/ijece.v7i2.pp1096-1102.

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University scheduling timetabling problem, falls into NP hard problems. Re-searchers have tried with many techniques to find the most suitable and fastest way for solving the problem. With the emergence of multi-core systems, the parallel implementation was considered for finding the solution. Our approaches attempt to combine several techniques in two algorithms: coarse grained algorithm and multi thread tournament algorithm. The results obtained from two algorithms are compared, using an algorithm evaluation function. Considering execution time, the coarse grained algorithm performed twice better than the multi thread algorithm.

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Sethanan, Kanchana, Somnuk Theerakulpisut, and Chatchai Benjapiyaporn. "Improving Energy Efficiency by Classroom Scheduling: A Case Study in a Thai University." Advanced Materials Research 931-932 (May 2014): 1089–95. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1089.

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In an attempt to improve energy efficiency in classrooms by classroom scheduling, an algorithm was developed to solve the timetabling problem in a Thai university. The algorithm using Tabu Search, unlike all other algorithms for time tabling, aims at reducing classroom energy consumption. The algorithm took into account the number of students in each class, class time, class size, time available for both students enrolling in each class and faculty members in charge of the classes, and energy consumption in each time period of the classroom. The algorithm was used to solve the past timetabling problem of a Thai university over the academic year 2006 to 2012 and the results were compared with those of the trial-and-error traditional timetabling. It was found that the algorithm can be used to significantly lower energy consumption of the classrooms.

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

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

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

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Firdaus Khair, Ahmad, Mokhairi Makhtar, Munirah Mazlan, Mohamad Afendee Mohamed, and Mohd Nordin Abdul Rahman. "Solving examination timetabling problem in UniSZA using ant colony optimization." International Journal of Engineering & Technology 7, no. 2.15 (April 6, 2018): 132. http://dx.doi.org/10.14419/ijet.v7i2.15.11369.

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At all educational institutions, timetabling is a conventional problem that has always caused numerous difficulties and demands that need to be satisfied. For the examination timetabling problem, those matters can be defined as complexity in scheduling exam events or non-deterministic polynomial hard problems (NP-hard problems). In this study, the latest approach using an ant colony optimisation (ACO) which is the ant system (AS) is presented to find an effective solution for dealing with university exam timetabling problems. This application is believed to be an impressive solution that can be used to eliminate various types of problems for the purpose of optimising the scheduling management system and minimising the number of conflicts. The key of this feature is to simplify and find shorter paths based on index pheromone updating (occurrence matrix). With appropriate algorithm and using efficient techniques, the schedule and assignation allocation can be improved. The approach is applied according to the data set instance that has been gathered. Therefore, performance evaluation and result are used to formulate the proposed approach. This is to determine whether it is reliable and efficient in managing feasible final exam timetables for further use.

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TESFALDET, BEREKET T. "AUTOMATED LECTURE TIMETABLING USING A MEMETIC ALGORITHM." Asia-Pacific Journal of Operational Research 25, no. 04 (August 2008): 451–75. http://dx.doi.org/10.1142/s021759590800181x.

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The lecture timetabling problem is known to be a highly constrained combinatorial optimization problem. There have been many attempts to address this problem using integer programming, graph coloring and several heuristic search methods. However, since each university has its own timetable setting requirements, it is difficult to develop a general solution method. Thus, the work is generally done manually. This paper attempts to solve the lecture timetabling problem of the University of Asmara using a customized memetic algorithm that we have called ALTUMA. It is a hybrid of genetic algorithms with hill-climbing operators. The performance of ALTUMA was evaluated using data obtained from the University. Empirical results show that ALTUMA is capable of producing good results in a reasonable amount of time. Besides, the results demonstrate that incorporating local search operators with a probabilistic scheme and delta method of fitness evaluation into the memetic algorithm significantly improves the search capabilities of the algorithm.

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Aprilius, William, Lorentzo Augustino, and Ong Yeremia M. H. "Implementasi Algoritma MAX-MIN Ant System pada Penjadwalan Mata Kuliah." Jurnal ULTIMATICS 5, no. 2 (December 1, 2013): 48–53. http://dx.doi.org/10.31937/ti.v5i2.320.

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University Course Timetabling Problem is a problem faced by every university, one of which is Universitas Multimedia Nusantara. Timetabling process is done by allocating time and space so that the whole associated class and course can be implemented. In this paper, the problem will be solved by using MAX-MIN Ant System Algorithm. This algorithm is an alternative approach to ant colony optimization. This algorithm uses two tables of pheromones as stigmergy, i.e. timeslot pheromone table and room pheromone table. In addition, the selection of timeslot and room is done by using the standard deviation of the value of pheromones. Testing is carried out by using 105 events, 45 timeslots, and 3 types of categories based on the number of rooms provided, i.e. large, medium, and small. In each category, testing is performed 5 times and for each testing, the data recorded is the unplace and Soft Constraint Penalty. In general, the greater the number of rooms, the smaller the unplace. Index Terms—ant colony optimization, max-min ant system, timetabling

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Wahid, Juliana, Syariza Abdul-Rahman, Aniza Mohamed Din, and Naimah Mohd-Hussin. "Constructing population of initial university timetable: design and analysis." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 2 (August 1, 2019): 1109. http://dx.doi.org/10.11591/ijeecs.v15.i2.pp1109-1118.

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

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Wutthipong Chinnasri, Soradech Krootjohn, and Nidapan Sureerattanan. "Performance Study of Genetic Operators on University Course Timetabling Problem." International Journal of Advancements in Computing Technology 4, no. 20 (November 30, 2012): 61–71. http://dx.doi.org/10.4156/ijact.vol4.issue20.8.

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Kohshori, Meysam Shahvali, Dariush Zeynolabedini, Mehrnaz Shirani Liri, and Leila Jadidi. "Multi Population Hybrid Genetic Algorithms for University Course Timetabling Problem." International Journal of Information Technology and Computer Science 4, no. 6 (June 1, 2012): 1–11. http://dx.doi.org/10.5815/ijitcs.2012.06.01.

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R., Sanjay, and Rajan S. "An Application of Genetic Algorithm for University Course Timetabling Problem." International Journal of Applied Information Systems 11, no. 3 (August 6, 2016): 26–30. http://dx.doi.org/10.5120/ijais2016451590.

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Abdullah, Salwani, Hamza Turabieh, Barry McCollum, and Paul McMullan. "A hybrid metaheuristic approach to the university course timetabling problem." Journal of Heuristics 18, no. 1 (December 24, 2010): 1–23. http://dx.doi.org/10.1007/s10732-010-9154-y.

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Gozali, Alfian Akbar, and Shigeru Fujimura. "Solving University Course Timetabling Problem Using Multi-Depth Genetic Algorithm." SHS Web of Conferences 77 (2020): 01001. http://dx.doi.org/10.1051/shsconf/20207701001.

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The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, and departments. The constraints could be hard (encouraged to be satisfied) or soft (better to be fulfilled). This problem becomes complicated for universities which have an immense number of students and lecturers. Moreover, several universities are implementing student sectioning which is a problem of assigning students to classes of a subject while respecting individual student requests along with additional constraints. Such implementation enables students to choose a set of preference classes first then the system will create a timetable depend on their preferences. Subsequently, student sectioning significantly increases the problem complexity. As a result, the number of search spaces grows hugely multiplied by the expansion of students, other variables, and involvement of their constraints. However, current and generic solvers failed to meet scalability requirement for student sectioning UCTP. In this paper, we introduce the Multi-Depth Genetic Algorithm (MDGA) to solve student sectioning UCTP. MDGA uses the multiple stages of GA computation including multi-level mutation and multi-depth constraint consideration. Our research shows that MDGA could produce a feasible timetable for student sectioning problem and get better results than previous works and current UCTP solver. Furthermore, our experiment also shows that MDGA could compete with other UCTP solvers albeit not the best one for the ITC-2007 benchmark dataset.

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Ferland, Jacques A., and Serge Roy. "Timetabling problem for university as assignment of activities to resources." Computers & Operations Research 12, no. 2 (January 1985): 207–18. http://dx.doi.org/10.1016/0305-0548(85)90045-0.

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Yu, Enzhe, and Ki-Seok Sung. "A genetic algorithm for a university weekly courses timetabling problem." International Transactions in Operational Research 9, no. 6 (November 2002): 703–17. http://dx.doi.org/10.1111/1475-3995.00383.

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Daskalaki, S., and T. Birbas. "Efficient solutions for a university timetabling problem through integer programming." European Journal of Operational Research 160, no. 1 (January 2005): 106–20. http://dx.doi.org/10.1016/j.ejor.2003.06.023.

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Sangsuwan, Phakawadee, and Ohm Sornil. "Solving Timetabling Problem Using A Co-Operative Co-Evolution Multi-Objective Genetic Algorithm." Applied Mechanics and Materials 411-414 (September 2013): 1966–70. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1966.

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Timetabling problem is important for efficient allocations of resources. University timetabling is to allocate a weekly schedule for instructors and students under a number of requirements and constraints. This paper purposes a solution based on a co-operative co-evolution multi-objective genetic algorithm (CCMOGA). The idea is to split the complexity into several simpler parts and co-operatively evolve them. Two definitions of chromosomes are studied: instructor-based and student-based. Experimental results with an actual situation show that the proposed method is able to give effective solutions, and the chromosome definition based on students yields higher quality schedules than does the definition based on instructors.

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Wutthipong Chinnasri, Soradech Krootjohn, and Nidapan Sureerattanan. "The Suitable Genetic Operators for Solving the University Course Timetabling Problem." Journal of Convergence Information Technology 8, no. 12 (July 31, 2013): 60–66. http://dx.doi.org/10.4156/jcit.vol8.issue12.7.

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Mahiba, A. Araisa, and C. Anand Deva Durai. "Genetic Algorithm with Search Bank Strategies for University Course Timetabling Problem." Procedia Engineering 38 (2012): 253–63. http://dx.doi.org/10.1016/j.proeng.2012.06.033.

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Junn, Kuan Yik, Joe Henry Obit, and Rayner Alfred. "A Constraint Programming Approach to Solving University Course Timetabling Problem (UCTP)." Advanced Science Letters 23, no. 11 (November 1, 2017): 11023–26. http://dx.doi.org/10.1166/asl.2017.10211.

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Song, Ting, Sanya Liu, Xiangyang Tang, Xicheng Peng, and Mao Chen. "An iterated local search algorithm for the University Course Timetabling Problem." Applied Soft Computing 68 (July 2018): 597–608. http://dx.doi.org/10.1016/j.asoc.2018.04.034.

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Gülcü, Ayla, and Can Akkan. "Robust university course timetabling problem subject to single and multiple disruptions." European Journal of Operational Research 283, no. 2 (June 2020): 630–46. http://dx.doi.org/10.1016/j.ejor.2019.11.024.

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De Causmaecker, Patrick, Peter Demeester, and Greet Vanden Berghe. "A decomposed metaheuristic approach for a real-world university timetabling problem." European Journal of Operational Research 195, no. 1 (May 2009): 307–18. http://dx.doi.org/10.1016/j.ejor.2008.01.043.

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Chen, Mei Ching, San Nah Sze, Say Leng Goh, Nasser R. Sabar, and Graham Kendall. "A Survey of University Course Timetabling Problem: Perspectives, Trends and Opportunities." IEEE Access 9 (2021): 106515–29. http://dx.doi.org/10.1109/access.2021.3100613.

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Abbaszadeh, Mortaza, and Saeed Saeedvand. "A Fast Genetic Algorithm for Solving University Scheduling Problem." IAES International Journal of Artificial Intelligence (IJ-AI) 3, no. 1 (March 1, 2014): 7. http://dx.doi.org/10.11591/ijai.v3.i1.pp7-15.

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University course timetabling is a NP-hard problem which is very difficult to solve by conventional methods, we know scheduling problem is one of the Nondeterministic Polynomial (NP) problems. This means, solving NP problems through normal algorithm is a time-consuming process (it takes days or months with available equipment) which makes it impossible to be solved through a normal algorithm like this. In purposed algorithm the problem of university class scheduling is solved through a new chromosome structure and modifying the normal genetic methods which really improves the solution in this case. We include lecturer, class and course information in presented algorithm, with all their Constraints, and it creates optimized scheduling table for weekly program of university after creating primary population of chromosomes and running genetic operators. In the final part of this paper we conclude from the results of input data analysis that the results have high efficiency compared with other algorithms considering maximum Constraints.

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Wu, Liping. "The application of Coarse-Grained Parallel Genetic Algorithm with Hadoop in University Intelligent Course-Timetabling System." International Journal of Emerging Technologies in Learning (iJET) 10, no. 8 (December 14, 2015): 11. http://dx.doi.org/10.3991/ijet.v10i8.5206.

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The university course-timetabling problem is a NP-C problem. The traditional method of arranging course is inefficient, causes a high conflict rate of teacher resource or classroom resource, and is poor satisfaction in students. So it does not meet the requirements of modern university educational administration management. However, parallel genetic algorithm (PGA) not only have the advantages of the traditional genetic algorithm(GA), but also take full advantage of the computing power of parallel computing. It can improve the quality and speed of solving effectively, and have a broad application prospect in solving the problem of university course-timetabling problem. In this paper, based on the cloud computing platform of Hadoop, an improved method of fusing coarse-grained parallel genetic algorithm (CGPGA) and Map/Reduce programming model is deeply researched, and which is used to solve the problem of university intelligent courses arrangement. The simulation experiment results show that, compared with the traditional genetic algorithm, the coarse-grained parallel genetic algorithm not only improves the efficiency of the course arrangement and the success rate of the course, but also reduces the conflict rate of the course. At the same time, this research makes full use of the high parallelism of Map/Reduce to improve the efficiency of the algorithm, and also solves the problem of university scheduling problem more effectively.

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BAYAR, Mustafa Mehmet, and Irmak UZUN BAYAR. "Conflict Management in University Examination Timetabling Problem: A case study of summer school mid-terms." Central European Review of Economics and Management 4, no. 3 (September 20, 2020): 67–87. http://dx.doi.org/10.29015/cerem.870.

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Abstract: Aim: This study is on tackling Examination Timetabling Problem (ETP) of the Faculty of Economics And Administrative Sciences (FEAS) of the Ankara HBV University summer school, where the courses of fall and spring semesters are offered simultaneously and regulations on restricting enrollments in inter-department electives or in-department courses of distinct years are relaxed. Thus, the complexity of the nature of the ETP problem is exacerbated. The direct heuristics based on successive assignments that the university normally adopts was proven inadequate for assuming standard regulations hence, another approach we explain in this paper was needed. Design / Research methods: The ETP was formulated as a Linear Mixed-Integer Program (LMIP) and decomposed into three stages; timetabling exams, room assignment, student allocation. To manage the conflict between the stakeholders of the examination procedure, a lexicographic optimization process based on the priority of the parties was undertaken. Conclusions / findings: After a recursive timetabling process based on a trial-and-error method a clash-free timetable was generated and, a room assignment plan that minimizes the total number of proctoring duties, usage of higher floor rooms and total crowdedness of rooms respectively was put into action. Therefore no student group experienced any clashing exams, the faculty members saved time that can be spent on research instead, since the room usage was better planned the costs (elevator usage, lighting, air conditioning, the labor of the janitors) were assumed to be decreased. Originality / value of the article: Each examination period bares a different ETP due to its problem-specific nature (number of courses offered, structure of student enrollments, availability of rooms, etc.). Summer schools provide a more irregular structure that demands special attention, a trial-and-error reformulation of the ETP in our case. In addition, the traditional formulations of the ETP, to the extent we have been able to scan, do not include the minimization of the crowdedness of the rooms. Thus, in creating a more comfortable environment, easier to monitor exams and, ability in handling unexpected dysfunctionalities (broken classroom equipment, etc.) this study is novel. Limitations of the research: The algorithms to solve an ETP formulated as an LMIP are of high complexity therefore, we are not able to assert the optimality of our suggested solutions acquired within time limitations. Keywords: examination timetabling, group decision making, lexicographic optimization, linear mixed-integer programming JEL: C44, C61, M12

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

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Course Timetabling is a complex problem that cannot be dealt with by using only a few general principles. The various actors (the administrator, the chairman, the instructor and the student) have their own objectives, and these objectives usually conflict. The complexity of the relationships among time slots, classes, classrooms, and instructors makes it difficult to achieve a feasible solution. In this article, we propose an artificial intelligence approach that integrates expert systems and constraint programming to implement a course timetabling system. Expert systems are utilized to incorporate knowledge into the timetabling system and to provide a reasoning capability for knowledge deduction. Separating out the knowledge base, the facts, and the inference engine in expert systems provides greater flexibility in supporting changes. The constraint hierarchy and the constraint network are utilized to capture hard and soft constraints and to reason about constraints by using constraint satisfaction and relaxation techniques. In addition, object-oriented software engineering is applied to improve the development and maintenance of the course timetabling system. A course timetabling system in the Department of Computer Science and Information Engineering at the National Changhua University of Education (NCUE) is used as an illustrative example of the proposed approach.

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Journal articles on the topic 'University timetabling problem'

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