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Journal articles on the topic 'Timetabling optimization problem'

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Published: 4 June 2021
Last updated: 6 February 2022

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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
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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 (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 suc
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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 (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 invo
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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 (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 c
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Lei, Yu, and Jiao Shi. "A NNIA Scheme for Timetabling Problems." Journal of Optimization 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/5723239.

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This paper presents a memetic multiobjective optimization algorithm based on NNIA for examination timetabling problems. In this paper, the examination timetabling problem is considered as a two-objective optimization problem while it is modeled as a single-objective optimization problem generally. Within the NNIA framework, the special crossover operator is utilized to search in the solution space; two local search techniques are employed to optimize these two objectives and a diversity-keeping strategy which consists of an elitism group operator and an extension optimization operator to ensur
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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 (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 (A
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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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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 (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 b
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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, timet
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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 (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. I
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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 (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. How
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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 (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-h
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13

Aidya Hanum Aizam, Nur, and Louis Caccetta. "Computational models for timetabling problem." Numerical Algebra, Control & Optimization 4, no. 3 (2014): 269–85. http://dx.doi.org/10.3934/naco.2014.4.269.

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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 (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–Me
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YAPICI, Mutlu, and Ömer Faruk BAY. "Genetic Algorithm Based Timetabling Program." Artificial Intelligence Studies 2, no. 2 (2020): 20–31. http://dx.doi.org/10.30855/ais.2019.02.02.01.

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Course Timetabling Problem is concerned with assigning a number of courses and instructors to classrooms by taking the constraints into consideration. Generally, this problem is typically resolved manually; and due to the large variety of constraints, resource limitations and complicated human factors involved, it takes a lot of time and manpower. It is considered as one of the most time-consuming problems faced by universities and colleges today. In this study, we aimed to develop a genetic algorithm-based timetabling software to bring a solution to course timetabling problem, which is a real
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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 (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.   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 th
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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 (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 L
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Christopher, Gilbert, and Arya Wicaksana. "Particle swarm optimization for solving thesis defense timetabling problem." TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no. 3 (2021): 762. http://dx.doi.org/10.12928/telkomnika.v19i3.18792.

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El-Dhshan, Abdalla, Hegazy Zaher, and Naglaa Ragaa. "An Intelligent Technique for Solving Timetable Problem." Journal of University of Shanghai for Science and Technology 23, no. 05 (2021): 1–11. http://dx.doi.org/10.51201/jusst/21/04251.

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Timetabling problem is complex combinatorial resources allocation problems. There are two hard and soft constraints to be satisfied. The timetable is feasible if all hard constraints are satisfied. Besides, satisfying more of the soft constraints produces a high-quality timetable. Crow Search Algorithm (CSA) as an intelligence technique presents for solving timetable problem. CSA like all meta-heuristic optimization techniques is a nature-inspire of intelligent behavior of crows. The proposed CSA tested using the well-known benchmark of hard timetabling datasets (hdtt). Taguchi’s method used t
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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 (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 devia
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LEE, HSIN-YUN. "A DECISION SUPPORT SYSTEM FOR EXPOSITION TIMETABLING USING ANT COLONY OPTIMIZATION." International Journal of Information Technology & Decision Making 11, no. 03 (2012): 609–26. http://dx.doi.org/10.1142/s0219622012500137.

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The exposition industry has developed rapidly in recent years. For exposition centers, exposition timetabling is a complicated problem. The timetables arranged by the administrators usually result in an imbalanced usage percentage between showgrounds and a low total usage percentage for all showgrounds. In this paper, we developed a decision support system that considers the characteristics of exposition timetabling. The proposed system applies ant colony optimization for determining the near-optimal timetable of expositions. The ants are grouped into teams representing multi-layer ant paths f
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Tassopoulos, Ioannis X., and Grigorios N. Beligiannis. "Solving effectively the school timetabling problem using particle swarm optimization." Expert Systems with Applications 39, no. 5 (2012): 6029–40. http://dx.doi.org/10.1016/j.eswa.2011.12.013.

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Shen, Yindong, Wenliang Xie, and Jingpeng Li. "A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty." Journal of Advanced Transportation 2021 (August 4, 2021): 1–16. http://dx.doi.org/10.1155/2021/3529984.

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The timetabling problem (TTP) and vehicle scheduling problem (VSP) are two indispensable problems in public transit planning process. They used to be solved in sequence; hence, optimality of resulting solutions is compromised. To get better results, some integrated approaches emerge to solve the TTP and VSP as an integrated problem. In the existing integrated approaches, the passenger comfort on bus and the uncertainty in the real world are rarely considered. To provide better service for passengers and enhance the robustness of the schedule to be compiled, we study the integrated optimization
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Schiewe, Philine, and Anita Schöbel. "Periodic Timetabling with Integrated Routing: Toward Applicable Approaches." Transportation Science 54, no. 6 (2020): 1714–31. http://dx.doi.org/10.1287/trsc.2019.0965.

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Periodic timetabling is an important, yet computationally challenging, problem in public transportation planning. The usual objective when designing a timetable is to minimize passenger travel time. However, in most approaches, it is ignored that the routes of the passengers depend on the timetable, so handling their routing separately leads to timetables that are suboptimal for the passengers. This has recently been recognized, but integrating the passenger routing in the optimization is computationally even harder than solving the classic periodic timetabling problem. In our paper, we develo
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Afolabi LO, Ibrahim MA, and Kehinde OO. "Developing an efficient optimization of course-lecturer distribution timetabling using transportation algorithm: A case study of Federal Polytechnic Offa." Global Journal of Engineering and Technology Advances 7, no. 3 (2021): 179–89. http://dx.doi.org/10.30574/gjeta.2021.7.3.0092.

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The institution course timetabling problem (ICTP) is a multidimensional assignment-problem that varies from course timetabling, class-teacher timetabling, student scheduling, teacher assignment, and classroom assignment. Many researchers have attempted to solve problems as related to timeslot but neglecting areas of course allocation to lecturers. The paper presented a course allocation and distribution model for lecturers based on their fields of interest and qualification to a transportation algorithm which was aimed at optimising the performance of lecturers in each course. It also evaluate
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Nothegger, Clemens, Alfred Mayer, Andreas Chwatal, and Günther R. Raidl. "Solving the post enrolment course timetabling problem by ant colony optimization." Annals of Operations Research 194, no. 1 (2012): 325–39. http://dx.doi.org/10.1007/s10479-012-1078-5.

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Tassopoulos, Ioannis X., and Grigorios N. Beligiannis. "Using particle swarm optimization to solve effectively the school timetabling problem." Soft Computing 16, no. 7 (2012): 1229–52. http://dx.doi.org/10.1007/s00500-012-0809-5.

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Van Bulck, David, Dries Goossens, Jo¨rn Scho¨nberger, and Mario Guajardo. "An Instance Data Repository for the Round-robin Sports Timetabling Problem." Management and Labour Studies 45, no. 2 (2020): 184–200. http://dx.doi.org/10.1177/0258042x20912108.

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The sports timetabling problem is a combinatorial optimization problem that consists of creating a timetable that defines against whom, when and where teams play games. This is a complex matter, since real-life sports timetabling applications are typically highly constrained. The vast amount and variety of constraints and the lack of generally accepted benchmark problem instances make that timetable algorithms proposed in the literature are often tested on just one or two specific seasons of the competition under consideration. This is problematic since only a few algorithmic insights are gain
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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 (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 meny
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TESFALDET, BEREKET T. "AUTOMATED LECTURE TIMETABLING USING A MEMETIC ALGORITHM." Asia-Pacific Journal of Operational Research 25, no. 04 (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
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Li, Xiao Feng, An Shi, and Jian Guo Luo. "Curriculum Schedule Arrangement Solved by an Improved Immune Optimization Algorithm." Advanced Materials Research 756-759 (September 2013): 3556–61. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3556.

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To solve the university timetabling problem (UTP) effectively, a immune algorithm-based solution for UTP was proposed. The mathematical model of UTP was expounded, a framework of immune algorithm was given, and simulation experiments were done to validate algorithm. Experimental result shows that proposed algorithm can solve the UTP effectively, and has the advantage of good application value.
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Palma, Cristian D., and Patrick Bornhardt. "Considering Section Balance in an Integer Optimization Model for the Curriculum-Based Course Timetabling Problem." Mathematics 8, no. 10 (2020): 1763. http://dx.doi.org/10.3390/math8101763.

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University course timetabling is a complex and time-consuming duty that every educational institution faces regularly. It consists of scheduling a set of lectures in predefined time slots so as to avoid student conflicts, meet teacher and room availability, and manage several institution-specific operational rules. In this paper, we schedule courses based on a curriculum, that is, before the students’ registration. Unlike other curriculum-based models, the proposed model considers two practical aspects when managing the conflicts between lectures: (i) it schedules sections of subjects so that
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Sakaliuk, O., and F. Trishyn. "USING A GENETIC ALGORITHM TO SOLVE THE COURSES TIMETABLING CREATION PROBLEM." Automation of technological and business processes 13, no. 2 (2021): 22–28. http://dx.doi.org/10.15673/atbp.v13i2.2053.

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Creating of courses timetable is an extremely difficult, time-consuming task and usually takes a long time. In many educational institutions, the courses schedule is developed manually. Schedule theory includes problems that are actually less complex than problems in practice, but theoretical analysis provides a fundamental understanding of the complexity of the schedule. The logical result is that the schedule is very difficult to build in practice due to many constraints [1].
 Scheduling courses is a planning problem. In 1996, the problem of scheduling was described as the allocation of
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Alghamdi, H., T. Alsubait, H. Alhakami, and A. Baz. "A Review of Optimization Algorithms for University Timetable Scheduling." Engineering, Technology & Applied Science Research 10, no. 6 (2020): 6410–17. http://dx.doi.org/10.48084/etasr.3832.

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The university course timetabling problem looks for the best schedule, to satisfy given criteria as a set of given resources, which may contain lecturers, groups of students, classrooms, or laboratories. Developing a timetable is a fundamental requirement for the healthy functioning of all educational and administrative parts of an academic institution. However, factors such as the availability of hours, the number of subjects, and the allocation of teachers make the timetable problem very complex. This study intends to review several optimization algorithms that could be applied as possible s
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Van Lieshout, Rolf N. "Integrated Periodic Timetabling and Vehicle Circulation Scheduling." Transportation Science 55, no. 3 (2021): 768–90. http://dx.doi.org/10.1287/trsc.2020.1024.

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Periodic timetabling is one of the most well-researched problems in the public transport optimization literature. However, the impact that timetabling has on the number of required vehicles, which directly translates to operator costs, is rarely considered. Therefore, in this paper, we consider the problem of jointly optimizing the timetable and the vehicle circulation schedule, which specifies the cyclic sequences of trips that vehicles perform. In order to obtain high-quality solutions to realistic instances, we improve an earlier proposed formulation by contraction techniques, three new val
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Kristiansen, Simon, Matias Sørensen, Michael B. Herold, and Thomas R. Stidsen. "The consultation timetabling problem at Danish high schools." Journal of Heuristics 19, no. 3 (2013): 465–95. http://dx.doi.org/10.1007/s10732-013-9219-9.

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Burke, Edmund, Yuri Bykov, James Newall, and Sanja Petrovic. "A time-predefined approach to course timetabling." Yugoslav Journal of Operations Research 13, no. 2 (2003): 139–51. http://dx.doi.org/10.2298/yjor0302139b.

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A common weakness of local search metaheuristics, such as Simulated Annealing, in solving combinatorial optimization problems, is the necessity of setting a certain number of parameters. This tends to generate a significant increase in the total amount of time required to solve the problem and often requires a high level of experience from the user. This paper is motivated by the goal of overcoming this drawback by employing "parameter-free" techniques in the context of automatically solving course timetabling problems. We employ local search techniques with "straightforward" parameters, i.e.
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Khan, M. B., and Xuesong Zhou. "Stochastic Optimization Model and Solution Algorithm for Robust Double-Track Train-Timetabling Problem." IEEE Transactions on Intelligent Transportation Systems 11, no. 1 (2010): 81–89. http://dx.doi.org/10.1109/tits.2009.2030588.

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Larabi Marie-Sainte, Souad. "A survey of Particle Swarm Optimization techniques for solving university Examination Timetabling Problem." Artificial Intelligence Review 44, no. 4 (2015): 537–46. http://dx.doi.org/10.1007/s10462-015-9437-7.

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Bolaji, Asaju La’aro, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, and Mohammed A. Awadallah. "A Hybrid Nature-Inspired Artificial Bee Colony Algorithm for Uncapacitated Examination Timetabling Problems." Journal of Intelligent Systems 24, no. 1 (2015): 37–54. http://dx.doi.org/10.1515/jisys-2014-0002.

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AbstractThis article presents a Hybrid Artificial Bee Colony (HABC) for uncapacitated examination timetabling. The ABC algorithm is a recent metaheuristic population-based algorithm that belongs to the Swarm Intelligence technique. Examination timetabling is a hard combinatorial optimization problem of assigning examinations to timeslots based on the given hard and soft constraints. The proposed hybridization comes in two phases: the first phase hybridized a simple local search technique as a local refinement process within the employed bee operator of the original ABC, while the second phase
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41

AlHadid, Issam, Khalid Kaabneh, and Hassan Tarawneh. "Hybrid Simulated Annealing with Meta-Heuristic Methods to Solve UCT Problem." Modern Applied Science 12, no. 11 (2018): 366. http://dx.doi.org/10.5539/mas.v12n11p366.

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Simulated Annealing (SA) is a common meta-heuristic algorithm that has been widely used to solve complex optimization problems. This work proposes a hybrid SA with EMC to divert the search effectively to another promising region. Moreover, a Tabu list memory applied to avoid cycling. Experimental results showed that the solution quality has enhanced using SA-EMCQ by escaping the search space from local optimum to another promising region space. In addition, the results showed that our proposed technique has outperformed the standard SA and gave comparable results to other approaches in the lit
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42

AlHadid, Issam, Khalid Kaabneh, and Hassan Tarawneh. "Hybrid Simulated Annealing with Meta-Heuristic Methods to Solve UCT Problem." Modern Applied Science 12, no. 11 (2018): 385. http://dx.doi.org/10.5539/mas.v12n11p385.

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Simulated Annealing (SA) is a common meta-heuristic algorithm that has been widely used to solve complex optimization problems. This work proposes a hybrid SA with EMC to divert the search effectively to another promising region. Moreover, a Tabu list memory applied to avoid cycling. Experimental results showed that the solution quality has enhanced using SA-EMCQ by escaping the search space from local optimum to another promising region space. In addition, the results showed that our proposed technique has outperformed the standard SA and gave comparable results to other approaches in the lit
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43

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 (2020): 67–87. http://dx.doi.org/10.29015/cerem.870.

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 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 inadeq
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44

He, Bisheng, Rui Song, Shiwei He, and Yue Xu. "High-Speed Rail Train Timetabling Problem: A Time-Space Network Based Method with an Improved Branch-and-Price Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/641562.

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A time-space network based optimization method is designed for high-speed rail train timetabling problem to improve the service level of the high-speed rail. The general time-space path cost is presented which considers both the train travel time and the high-speed rail operation requirements: (1) service frequency requirement; (2) stopping plan adjustment; and (3) priority of train types. Train timetabling problem based on time-space path aims to minimize the total general time-space path cost of all trains. An improved branch-and-price algorithm is applied to solve the large scale integer pr
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Hartmann Tolić, Ivana, Emmanuel Karlo Nyarko, and Avishai (Avi) Ceder. "Optimization of Public Transport Services to Minimize Passengers’ Waiting Times and Maximize Vehicles’ Occupancy Ratios." Electronics 9, no. 2 (2020): 360. http://dx.doi.org/10.3390/electronics9020360.

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Determining the best timetable for vehicles in a public transportation (PT) network is a complex problem, especially because it is just necessary to consider the requirements and satisfaction of passengers as the requirements of transportation companies. In this paper, a model of the PT timetabling problem which takes into consideration the passenger waiting time (PWT) at a station and the vehicle occupancy ratio (VOR) is proposed. The solution aims to minimize PWT and maximize VOR. Due to the large search space of the problem, we use a multiobjective particle swarm optimization (MOPSO) algori
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TEOH, CHONG KEAT, ANTONI WIBOWO, and MOHD SALIHIN NGADIMAN. "AN ADAPTED CUCKOO OPTIMIZATION ALGORITHM AND GENETIC ALGORITHM APPROACH TO THE UNIVERSITY COURSE TIMETABLING PROBLEM." International Journal of Computational Intelligence and Applications 13, no. 01 (2014): 1450002. http://dx.doi.org/10.1142/s1469026814500023.

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The university course timetabling problem is an NP-hard and NP-complete problem concerned with assigning a specific set of events and resources to timeslots under a highly-constrained search space. This paper presents a novel metaheuristic algorithm entitled adapted cuckoo optimization algorithm which is derived from the cuckoo optimization algorithm and cuckoo search algorithm. This algorithm includes features such as local random walk on discrete data which mimics the behavior of Lévy flights and an Elitism-based mechanism which echoes back the best candidate solutions and prevents the algor
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Yusoff, Marina, and Anis Amalina Othman. "Genetic Algorithm with Elitist-Tournament for Clashes-Free Slots of Lecturer Timetabling Problem." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (2018): 303. http://dx.doi.org/10.11591/ijeecs.v12.i1.pp303-309.

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<span>Genetic algorithm (GA) approach is one of an evolutionary optimization technique relies on natural selection. The employment of GA still popular and it was applied to many real-world problems, especially in many combinatorial optimization solutions. Lecturer Timetabling Problem (LTP) has been researched for a few decades and produced good solutions. Although, some of LTP offers good results, the criteria and constraints of each LTP however are different from other universities. The LTP appears to be a tiresome job to the scheduler that involves scheduling of students, classes, lect
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Patrick, Kenekayoro, and Zipamone Godswill. "Greedy Ants Colony Optimization Strategy for Solving the Curriculum Based University Course Timetabling Problem." British Journal of Mathematics & Computer Science 14, no. 2 (2016): 1–10. http://dx.doi.org/10.9734/bjmcs/2016/23143.

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Skoullis, Vassilios I., Ioannis X. Tassopoulos, and Grigorios N. Beligiannis. "Solving the high school timetabling problem using a hybrid cat swarm optimization based algorithm." Applied Soft Computing 52 (March 2017): 277–89. http://dx.doi.org/10.1016/j.asoc.2016.10.038.

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Luo, Ziyan, Xiaoyu Li, and Naihua Xiu. "A Sparse Optimization Approach for Energy-Efficient Timetabling in Metro Railway Systems." Journal of Advanced Transportation 2018 (November 1, 2018): 1–19. http://dx.doi.org/10.1155/2018/1784789.

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In this paper, we propose a sparse optimization approach to maximize the utilization of regenerative energy produced by braking trains for energy-efficient timetabling in metro railway systems. By introducing the cardinality function and the square of the Euclidean norm function as the objective function, the resulting sparse optimization model can characterize the utilization of the regenerative energy appropriately. A two-stage alternating direction method of multipliers is designed to efficiently solve the convex relaxation counterpart of the original NP-hard problem and then to produce an
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