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Journal articles on the topic 'Timetabling'

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Published: 4 June 2021

Last updated: 8 June 2024

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1

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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2

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

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

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3

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

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

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4

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

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

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5

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

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

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6

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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7

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

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

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8

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

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

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9

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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10

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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11

Ngo, Son Tung, Jafreezal B. Jaafar, Izzatdin Abdul Aziz, Giang Hoang Nguyen, and Anh Ngoc Bui. "Genetic Algorithm for Solving Multi-Objective Optimization in Examination Timetabling Problem." International Journal of Emerging Technologies in Learning (iJET) 16, no. 11 (June 4, 2021): 4. http://dx.doi.org/10.3991/ijet.v16i11.21017.

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Examination timetabling is one of 3 critical timetabling jobs besides enrollment timetabling and teaching assignment. After a semester, scheduling examinations is not always an easy job in education management, especially for many data. The timetabling problem is an optimization and Np-hard problem. In this study, we build a multi-objective optimizer to create exam schedules for more than 2500 students. Our model aims to optimize the material costs while ensuring the dignity of the exam and students' convenience while considering the rooms' design, the time requirement of each exam, which involves rules and policy constraints. We propose a programmatic compromise to approach the maximum tar-get optimization model and solve it using the Genetic Algorithm. The results show the effectiveness of the introduced algorithm.

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12

Eglese, R. W., and G. K. Rand. "Conference Seminar Timetabling." Journal of the Operational Research Society 38, no. 7 (July 1987): 591. http://dx.doi.org/10.2307/2582396.

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13

Johnson, David. "Timetabling University Examinations." Journal of the Operational Research Society 41, no. 1 (January 1990): 39. http://dx.doi.org/10.2307/2582937.

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14

Clark, Peter. "American Corporate Timetabling." Time & Society 6, no. 2-3 (July 1997): 261–85. http://dx.doi.org/10.1177/0961463x97006002008.

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Eglese, R. W., and G. K. Rand. "Conference Seminar Timetabling." Journal of the Operational Research Society 38, no. 7 (July 1987): 591–98. http://dx.doi.org/10.1057/jors.1987.102.

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16

Johnson, David. "Timetabling University Examinations." Journal of the Operational Research Society 41, no. 1 (January 1990): 39–47. http://dx.doi.org/10.1057/jors.1990.5.

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17

Ibarra-Rojas, Omar J., Fernando López-Irarragorri, and Yasmin A. Rios-Solis. "Multiperiod Bus Timetabling." Transportation Science 50, no. 3 (August 2016): 805–22. http://dx.doi.org/10.1287/trsc.2014.0578.

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18

Burke, Edmund, and Sanja Petrovic. "Timetabling and Rostering." European Journal of Operational Research 153, no. 1 (February 2004): 1–2. http://dx.doi.org/10.1016/s0377-2217(03)00106-1.

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19

Liebchen, Christian, and Sebastian Stiller. "Delay resistant timetabling." Public Transport 1, no. 1 (November 6, 2008): 55–72. http://dx.doi.org/10.1007/s12469-008-0004-3.

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20

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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21

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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22

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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24

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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Icasia, Gabriella, Raras Tyasnurita, and Etria Sepwardhani Purba. "Application of Heuristic Combinations in Hyper-Heuristic Framework for Exam Scheduling Problems." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 4 (August 17, 2020): 664–71. http://dx.doi.org/10.29207/resti.v4i4.2066.

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Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem. On a large scale of data, the examination timetabling problem becomes a complex problem and takes time if it solved manually. Therefore, heuristics exist to provide reasonable enough solutions and meet the constraints of the problem. In this study, a real-world dataset of Examination Timetabling (Toronto dataset) is solved using a Hill-Climbing and Tabu Search algorithm. Different from the approach in the literature, Tabu Search is a meta-heuristic method, but we implemented a Tabu Search within the hyper-heuristic framework. The main objective of this study is to provide a better understanding of the application of Hill-Climbing and Tabu Search in hyper-heuristics to solve timetabling problems. The results of the experiments show that Hill-Climbing and Tabu Search succeeded in automating the timetabling process by reducing the penalty 18-65% from the initial solution. Besides, we tested the algorithms within 10,000-100,000 iterations, and the results were compared with a previous study. Most of the solutions generated from this experiment are better compared to the previous study that also used Tabu Search algorithm.

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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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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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Imah, Elly Matul, A. R. Hutomo, A. Fitrananda, A. Marshadiany, and G. P. Prikarti. "IMPLEMENTASI ALGORITMA INTEGER LINEAR PROGRAMMING UNTUK SISTEM INFORMASI PENJADWALAN RUANGAN DI FAKULTAS ILMU KOMPUTER UNIVERSITAS INDONESIA." Jurnal Sistem Informasi 7, no. 1 (July 15, 2012): 25. http://dx.doi.org/10.21609/jsi.v7i1.291.

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Permasalahan konflik penjadwalan ruangan (timetabling) sering dihadapi hampir sebagian besar institusi akademis di Indonesia, salah satunya di Fakultas Ilmu Komputer Universitas Indonesia (Fasilkom UI). Peningkatan jumlah mahasiswa setiap tahun yang tidak diikuti oleh peningkatan jumlah dan kapasitas kelas menjadi faktor penyebab utama. Selama ini sistem penjadwalan masih dilakukan secara manual, sehingga membutuhkan waktu yang relatif lama dan menyebabkan optimasi pengalokasian kebutuhan ruangan menjadi kurang efisien. Penelitian ini bertujuan untuk menemukan pendekatan yang sesuai dalam menyelesaikan masalah timetabling tersebut. Beberapa pendekatan yang dapat digunakan untuk menyelesaikan masalah ini antara lain algoritma Tabu Search, Simmulated Annealing, Graph Coloring, dan Integer Linear Programming (ILP). Dalam penelitian ini, peneliti menggunakan algoritma ILP karena ILP merupakan model yang paling tepat untuk menyelesaikan masalah timetabling di Fasilkom UI. Algoritma ini dapat meminimalkan waktu yang diperlukan untuk melakukan penjadwalan dari sebulan menjadi hitungan menit. Room scheduling conflict issues (timetabling) are facing most of the academic institutions in Indonesia, one is in the Faculty of Computer Science (Fasilkom) Universitas Indonesia (UI). In the number of students each year followed by no increase in the number and capacity of the class became the main factor. During this scheduling system is still done manually so it takes a relatively long time so that the optimization is less efficient allocation of space requirements. This study aims to find an appropriate approach in solving the timetabling problem. Several approaches can be used to solve these problems include Tabu Search algorithm, Simmulated Annealing, Graph Coloring, and Integer Linear Programming (ILP). In this study we used the ILP algorithm for ILP is the most appropriate model to solve the timetabling problem in Fasilkom UI. This algorithm can minimize the time required to perform the scheduling of a month becomes a matter of minutes.

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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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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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31

Ma, Xin Min, and Lin Li Wu. "Research on Timetabling Problems Based on Particle Swarm Optimization Algorithm." Advanced Engineering Forum 6-7 (September 2012): 736–41. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.736.

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A new algorithm for timetabling based on particle swarm optimization algorithm was proposed, and the key problems such as particle coding, fitness function fabricating, particle swarm initialization and crossover operation were settled. The fitness value declines when the evolution generation increases. The results showed that it was a good solution for course timetabling problem in the educational system.

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Bolaji, Asaju La’aro, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, and Mohammed A. Awadallah. "Artificial Bee Colony Algorithm for Solving Educational Timetabling Problems." International Journal of Natural Computing Research 3, no. 2 (April 2012): 1–21. http://dx.doi.org/10.4018/jncr.2012040101.

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This paper presents an artificial bee colony algorithm (ABC) for Education Timetabling Problem (ETP). It is aimed at developing a good-quality solution for the problem. The initial population of solutions was generated using Saturation Degree (SD) and Backtracking Algorithm (BA) to ensure the feasibility of the solutions. At the improvement stage in the solution method, ABC uses neighbourhood structures iteratively within the employed and onlooker bee operators, in order to rigorously navigate the UTP search space. The technique was evaluated using curriculum-based course timetabling (CB-CTT) and Uncapacitated Examination Timetabling Problem (UETP) problem instances. The experimental results on UETP showed that the technique is comparable with other state-of-the-art techniques and provides encouraging results on CB-CTT.

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33

Kusuma, Purba Daru, and Ratna Astuti Nugrahaeni. "Coordinated COVID-19 vaccination scheduling model by using nearest distance-single course timetabling method." Bulletin of Electrical Engineering and Informatics 11, no. 2 (April 1, 2022): 1007–17. http://dx.doi.org/10.11591/eei.v11i2.3384.

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This work proposes a new coordinated vaccine scheduling model suitable for the city size COVID-19 vaccination program. It is different from the existing COVID-19 vaccination scheduling mechanism where there is no coordination among endpoint providers. On the other side, the vaccine stock in every provider is limited, so that this mismatch creates many unserved participants. Moreover, studies on the COVID-19 vaccination scheduling problem are hard to find. This work aims to solve this mismatch problem. It is developed by combining the nearest distance and the single course timetabling. It is then optimized by using a cloud theory based-simulated annealing algorithm. The simulation result shows that the proposed model outperforms both the uncoordinated and basic course timetabling models. It can minimize the number of unserved participants, total travel distance, and the number of participants with missed timeslot. It produces zero unserved participants if the total vaccine quantity is at least equal to the total number of participants. The proposed model creates lower total travel distance than the uncoordinated or basic course timetabling adopted model. It is also better than the basic course timetabling model in creating a low number of participants with missed timeslot.

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34

Wood, J., and D. Whitaker. "Student Centred School Timetabling." Journal of the Operational Research Society 49, no. 11 (November 1998): 1146. http://dx.doi.org/10.2307/3010095.

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Ayob, Masri, Abdul Razak Hamdan, Salwani Abdullah, Zalinda Othman, Mohd Zakree Ahmad Nazri, Kiftiah Abd Razak, Rohana Tan, et al. "Intelligent Examination Timetabling Software." Procedia - Social and Behavioral Sciences 18 (2011): 600–608. http://dx.doi.org/10.1016/j.sbspro.2011.05.087.

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Wood, J., and D. Whitaker. "Student centred school timetabling." Journal of the Operational Research Society 49, no. 11 (November 1998): 1146–52. http://dx.doi.org/10.1057/palgrave.jors.2600628.

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Ibarra-Rojas, Omar J., and Yasmin A. Rios-Solis. "Synchronization of bus timetabling." Transportation Research Part B: Methodological 46, no. 5 (June 2012): 599–614. http://dx.doi.org/10.1016/j.trb.2012.01.006.

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38

de Werra, D. "The combinatorics of timetabling." European Journal of Operational Research 96, no. 3 (February 1997): 504–13. http://dx.doi.org/10.1016/s0377-2217(96)00111-7.

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Rudová, Hana, Tomáš Müller, and Keith Murray. "Complex university course timetabling." Journal of Scheduling 14, no. 2 (May 1, 2010): 187–207. http://dx.doi.org/10.1007/s10951-010-0171-3.

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Müller, Tomáš. "Real-life examination timetabling." Journal of Scheduling 19, no. 3 (August 9, 2014): 257–70. http://dx.doi.org/10.1007/s10951-014-0391-z.

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Schmidt, Marie, and Anita Schöbel. "Timetabling with passenger routing." OR Spectrum 37, no. 1 (January 23, 2014): 75–97. http://dx.doi.org/10.1007/s00291-014-0360-0.

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42

White, George M., and Simon K. S. Wong. "Interactive timetabling in universities." Computers & Education 12, no. 4 (1988): 521–29. http://dx.doi.org/10.1016/0360-1315(88)90025-5.

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Palgunadi, Sarngadi. "Timetabling Construction Problem (TCP)." Jurnal Teknologi & Informasi ITSmart 1, no. 1 (September 3, 2016): 44. http://dx.doi.org/10.20961/its.v1i1.586.

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It will be described the TCP in general and proposed polinomial algorithms to solve TCP in general and different classsizes in TCP. A timetabling is a tabling of conducting lessons by teachers arranged according to the room-time when they take place that must meet a number of requirements and the timing of teaching processes must quarantee that no student and no teacher has more than one lesson at the same time. Let k, m, n be the number of lessons, teachers and rooms respectively, the general TCP, as well as 2-classsizes TCP, can be solved in O(k m n)

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Wood, J., and D. Whitaker. "Student centred school timetabling." Journal of the Operational Research Society 49, no. 11 (1998): 1146–52. http://dx.doi.org/10.1038/sj.jors.2600628.

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de Werra, D. "An introduction to timetabling." European Journal of Operational Research 19, no. 2 (February 1985): 151–62. http://dx.doi.org/10.1016/0377-2217(85)90167-5.

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Hamzaoui, Salem, and Omar Ben-Ayed. "Parcel distribution timetabling problem." Operations Management Research 4, no. 3-4 (September 29, 2011): 138–49. http://dx.doi.org/10.1007/s12063-011-0056-4.

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BANBARA, MUTSUNORI, TAKEHIDE SOH, NAOYUKI TAMURA, KATSUMI INOUE, and TORSTEN SCHAUB. "Answer set programming as a modeling language for course timetabling." Theory and Practice of Logic Programming 13, no. 4-5 (July 2013): 783–98. http://dx.doi.org/10.1017/s1471068413000495.

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AbstractThe course timetabling problem can be generally defined as the task of assigning a number of lectures to a limited set of timeslots and rooms, subject to a given set of hard and soft constraints. The modeling language for course timetabling is required to be expressive enough to specify a wide variety of soft constraints and objective functions. Furthermore, the resulting encoding is required to be extensible for capturing new constraints and for switching them between hard and soft, and to be flexible enough to deal with different formulations. In this paper, we propose to make effective use of ASP as a modeling language for course timetabling. We show that our ASP-based approach can naturally satisfy the above requirements, through an ASP encoding of the curriculum-based course timetabling problem proposed in the third track of the second international timetabling competition (ITC-2007). Our encoding is compact and human-readable, since each constraint is individually expressed by either one or two rules. Each hard constraint is expressed by using integrity constraints and aggregates of ASP. Each soft constraint S is expressed by rules in which the head is the form of penalty(S,V,C), and a violation V and its penalty cost C are detected and calculated respectively in the body. We carried out experiments on four different benchmark sets with five different formulations. We succeeded either in improving the bounds or producing the same bounds for many combinations of problem instances and formulations, compared with the previous best known bounds.

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McCollum, Barry, Andrea Schaerf, Ben Paechter, Paul McMullan, Rhyd Lewis, Andrew J. Parkes, Luca Di Gaspero, Rong Qu, and Edmund K. Burke. "Setting the Research Agenda in Automated Timetabling: The Second International Timetabling Competition." INFORMS Journal on Computing 22, no. 1 (February 2010): 120–30. http://dx.doi.org/10.1287/ijoc.1090.0320.

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Kheiri, Ahmed, and Ed Keedwell. "A Hidden Markov Model Approach to the Problem of Heuristic Selection in Hyper-Heuristics with a Case Study in High School Timetabling Problems." Evolutionary Computation 25, no. 3 (September 2017): 473–501. http://dx.doi.org/10.1162/evco_a_00186.

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Operations research is a well-established field that uses computational systems to support decisions in business and public life. Good solutions to operations research problems can make a large difference to the efficient running of businesses and organisations and so the field often searches for new methods to improve these solutions. The high school timetabling problem is an example of an operations research problem and is a challenging task which requires assigning events and resources to time slots subject to a set of constraints. In this article, a new sequence-based selection hyper-heuristic is presented that produces excellent results on a suite of high school timetabling problems. In this study, we present an easy-to-implement, easy-to-maintain, and effective sequence-based selection hyper-heuristic to solve high school timetabling problems using a benchmark of unified real-world instances collected from different countries. We show that with sequence-based methods, it is possible to discover new best known solutions for a number of the problems in the timetabling domain. Through this investigation, the usefulness of sequence-based selection hyper-heuristics has been demonstrated and the capability of these methods has been shown to exceed the state of the art.

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Jiang, Feng, Da-ben Yu, and Shao-quan Ni. "An Objective Train Timetabling Quality Evaluation Method." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/3047963.

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The train timetable dominates the rail traffic organization. The timetabling quality should be evaluated to check the work skill of train timetable managers. The values of existing timetable evaluation indexes vary with infrastructure features and traffic flow; therefore, they are not comparable in fact. Furthermore, subjective inputs like expert scores are involved in evaluation; this will lead to unreliable results because the experts may have different opinions. To overcome these shortages, we propose a relative train path efficiency index by taking the train paths as production units. Each unit consumes some transport resources and produces some feedback outputs. A DEA model is applied to compute the train path efficiency. Two statistical functions of train path efficiency are used to evaluate the timetabling quality. We verify our method with real-world timetables. First, we use the Shibantan-to-Xinqiao line timetable to test the relative feature of the index proposed, and the results show that the train path efficiency value is relative and can reflect whether the stops are evenly distributed or not. Second, we evaluate the timetabling quality of another two timetables of the Qingdao-to-Jinan line with different traffic flows, and the results show that, compared with the 2012 timetable, the timetabling quality decreased in 2013.

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