A modified migrating bird optimization for university course timetabling problem

University course timetabling problem is a dilemma which educational institutions are facing due to various demands to be achieved in limited resources. Migrating bird optimization (MBO) algorithm is a new meta-heuristic algorithm which is inspired by flying formation of migrating birds. It has been applied successfully in tackling quadratic assignment problem and credit cards fraud detection problem. However, it was reported that MBO will get stuck in local optima easily. Therefore, a modified migrating bird optimization algorithm is proposed to solve post enrolment-based course timetabling. An improved neighbourhood sharing mechanism is used with the aim of escaping from local optima. Besides that, iterated local search is selected to be hybridized with the migrating bird optimization in order to further enhance its exploitation ability. The proposed method was tested using Socha’s benchmark datasets. The experimental results show that the proposed method outperformed the basic MBO and it is capable of producing comparable results as compared with existing methods that have been presented in literature. Indeed, the proposed method is capable of addressing university course timetabling problem and promising results were obtained

An Assignment Problem and Its Application in Education Domain: A Review and Potential Path

This paper presents a review pertaining to assignment problem within the education domain, besides looking into the applications of the present research trend, developments, and publications. Assignment problem arises in diverse situations, where one needs to determine an optimal way to assign n subjects to m subjects in the best possible way.With that, this paper classified assignment problems into two, which are timetabling problem and allocation problem. The timetabling problem is further classified into examination, course, and school timetabling problems, while the allocation problem is divided into student-project allocation, new student allocation, and space allocation problems. Furthermore, the constraints, which are of hard and soft constraints, involved in the said problems are briefly elaborated.In addition, this paper presents various approaches to address various types of assignment problem. Moreover, direction and potential paths of problem solving based on the latest trend of approaches are also highlighted.As such, this review summarizes and records a comprehensive survey regarding assignment problem within education domain, which enhances one's understanding concerning the varied types of assignment problems, along with various approaches that serve as solution

Penyelesaian Penjadwalan Mata Kuliah Menggunakan Metode Hiperheuristik Dengan Hibridisasi Algoritma Tabu Search, Simulated Annealing, Dan Self-Adaptive Pada Lintas Domain Permasalahan

Penjadwalan diperlukan sebagai pengalokasian sumber daya untuk menyelesaikan sebuah pekerjaan dengan batasan-batasan yang telah didefinisikan sehingga dapat memaksimalkan kemungkinan alokasi atau meminimalisir pelanggaran batasan. Salah satu jenis penjadwalan pada bidang pendidikan yaitu Post-Enrollment Course Timetabling (PE-CTT). Tantangan yang dihadapi pada PE-CTT yaitu perbedaan permasalahan, sejumlah batasan, dan persyaratan berbeda pada satu universitas dengan universitas lainnya sehingga sulit untuk menemukan solusi yang umum dan efektif. Salah satu solusi yang dapat mengembangkan sistem yang lebih general dengan menggunakan metode yang lebih murah dan tetap dapat menyelesaikan masalah adalah dengan menggunakan pendekatan Hyper-Heuristic. Pengujian akan dilakukan pada lintas domain yaitu dataset Socha dan dataset ITC-2007. Strategi Self-Adaptive digunakan sebagai strategi untuk memilih Low-Level-Heuristic (LLH) dan Simulated Annealing dan Tabu Search sebagai strategi Move Acceptance (MA) untuk menyelesaikan permasalahan penjadwalan mata kuliah tersebut. Hasil yang didapatkan pada dataset Socha, algoritma SATSSA menghasilkan nilai yg lebih baik dibandingkan dengan algoritma lain pada 2 instance. Algoritma SATSSA mampu mencapai nilai yang paling optimum pada 5 instance dataset Socha. Algoritma SATSSA menghasilkan nilai yang lebih optimum dibandingkan dengan algoritma lain pada 5 instance dataset ITC2007 yaitu instance early1, early2, hidden20, hidden21, dan hidden23. ================================================================================================================================== Timetabling is needed to complete a job with allocating resources and defined boundaries to maximize the possibility of allocation or minimize the violation of boundaries. One type of timetabling in the education field is Post-Enrollment Course Timetabling (PE-CTT). The challenges faced in the PE-CTT are differences in problems, a number of limitations, and requirements that differ from one university to another so that it is difficult to find common and effective solutions. One solution that can develop more general systems by using cheaper methods and still being able to solve problems is the Hyper-Heuristic approach. Testing will be carried out on cross domains namely the Socha dataset and the ITC-2007 dataset. The Self-Adaptive Strategy is used as a strategy for selecting Low-Level-Heuristics (LLH) and Simulated Annealing and Taboo Search as a Move Acceptance (MA) strategy to solve the course timetabling problems. The results obtained in the Socha dataset, SATSSA algorithm produces better values compared to other algorithms in 2 instances. SATSSA algorithm is able to achieve the most optimum value on 5 Socha dataset instances. SATSSA algorithm produces more optimum values compared to other algorithms on 5 ITC2007 dataset instances, namely early1, early2, hidden20, hidden21, and hidden23 instances. Key words : Post Enrollment Course Timetabling, Simulated Annealing, Tabu Search, Self-Adaptive, Socha Dataset, ITC-2007 Datase

Scheduling Problems

Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems

Genetic based discrete particle swarm optimization for elderly day care center timetabling

The timetabling problem of local Elderly Day Care Centers (EDCCs) is formulated into a weighted maximum constraint satisfaction problem (Max-CSP) in this study. The EDCC timetabling problem is a multi-dimensional assignment problem, where users (elderly) are required to perform activities that require different venues and timeslots, depending on operational constraints. These constraints are categorized into two: hard constraints, which must be fulfilled strictly, and soft constraints, which may be violated but with a penalty. Numerous methods have been successfully applied to the weighted Max-CSP; these methods include exact algorithms based on branch and bound techniques, and approximation methods based on repair heuristics, such as the min-conflict heuristic. This study aims to explore the potential of evolutionary algorithms by proposing a genetic-based discrete particle swarm optimization (GDPSO) to solve the EDCC timetabling problem. The proposed method is compared with the min-conflict random-walk algorithm (MCRW), Tabu search (TS), standard particle swarm optimization (SPSO), and a guided genetic algorithm (GGA). Computational evidence shows that GDPSO significantly outperforms the other algorithms in terms of solution quality and efficiency

Recommendation System for Collegian Student's Weekly Course Schedule

Existing research on course scheduling was conducted only from the institutional side.  However, students usually have other considerations, such as routine activities outside of class, course time, holidays in a week of study, and lead time between courses. These conditions had never been taken into consideration in existing research.  In this paper, a recommendation system was proposed using Depth First Search and Simple Multi Attribute Ranking Technique methods.  Depth First Search method was used to find all possible alternative schedules. All the possible alternative schedules were used to determine the schedule that best suits student preferences using Simple Multi Attribute Ranking Technique method. The system performance was measured through simulation to get course schedule recommendations for 28 students.  The simulation results were compared with the ideal schedule desired by the students and the real course schedule for those students. The accuracy of the recommended schedule against the ideal schedule desired by students was 70.8% with an average processing time of 1.05 seconds. The accuracy of the recommended schedule increased to about 91% when compared to the actual student courses schedule.  So it can be concluded that the research can help to recommend students' weekly class schedules in real terms.Selama ini, penelitian terkait dengan penjawalan mata kuliah hanya  dilakukan dengan mempertimbangkan sisi institusi.  Namun, biasanya mahasiswa memiliki pertimbangan lain, seperti kegiatan rutin di luar kuliah, waktu perkuliahan, hari libur kuliah, dan jeda waktu tunggu antar mata kuliah.  Kondisi ini tidak pernah dipertimbangkan dalam penelitian yang ada. Penelitian yang dilakukan bertujuan untuk mengembangkan sebuah sistem rekomendasi dengan menggunakan gabungan antara metode Depth First Search dan Simple Multi Attribute Ranking Technique. Metode Depth First Search digunakan untuk mencari semua kemungkinan alternatif jadwal. Semua alternatif jadwal yang didapatkan akan digunakan untuk menentukan jadwal yang paling sesuai dengan preferensi mahasiswa menggunakan metode Simple Multi Attribute Ranking Technique. Kinerja dari sistem dievaluasi melalui simulasi untuk mendapatkan rekomendasi jadwal mata kuliah bagi 28 mahasiswa. Hasil simulasi kemudian dibandingkan dengan jadwal ideal yang diinginkan oleh mahasiswa dan jadwal mata kuliah yang riil dijalani mahasiswa.  Akurasi dari jadwal yang direkomendasikan terhadap jadwal ideal yang diinginkan mahasiswa mencapai 70,8% dengan rata-rata waktu untuk menghasilkan jadwal yang direkomendasikan adalah 1,05 detik. Akurasi jadwal yang direkomendasikan meningkat menjadi sekitar 91% jika dibandingkan dengan jadwal mata kuliah yang riil dijalani oleh mahasiswa yang bersangkutan.  Jadi dapat disimpulkan bahwa penelitian yang dilakukan dapat membantu merekomendasikan jadwal kuliah mingguan mahasiswa secara rii

DEM Timetabling Project ? Development/implementation of an algorithm to support the creation of timetables

This work presents the development of an algorithm to support the process of creating academic timetables, specifically aimed at solving the University Course Timetabling Problem. To date, this problem is solved manually in Instituto Superior de Engenharia do Porto, where professors and engineers face the complex task of creating timetables based on schedules from previous years. The proposed solution aimed to support the process of creating timetables at ISEP, reducing the time and human resources required for this task. The developed algorithm uses an integer programming approach and can consider a variety of constraints and preferences of both faculty and students. It was designed to adapt and optimize the timetable creation process as needs evolve, ensuring future demands can be easily accommodated. The algorithm implementation was based on the Python programming language and the Pyomo library, offering a flexible and efficient approach to optimizing resource allocation. Additionally, the system is designed to import data from real-world sources, simplifying the integration of crucial information. The result assigned all the 128 one-hour classes among the week, presenting the faculty member, the classroom assigned and the type of class according to each course. This research presents feasible solutions that need improvement on the demanding conditions and restrictions imposed by ISEP. The computational results obtained offered a significantly decrease in the time resource used, compared to the manual work previously done

An investigation of Monte Carlo tree search and local search for course timetabling problems

The work presented in this thesis focuses on solving course timetabling problems, a variant of education timetabling. Automated timetabling is a popular topic among researchers and practitioners because manual timetable construction is impractical, if not impossible, as it is known to be NP-hard. A two-stage approach is investigated. The first stage involves finding feasible solutions. Monte Carlo Tree Search (MCTS) is utilized in this stage. As far as we are aware, it is used for the first time in addressing the timetabling problem. It is a relatively new search method and has achieved breakthrough in the domain of games particularly Go. Several enhancements are attempted on MCTS such as heuristic based simulations and pruning. We also compare the effectiveness of MCTS with Graph Coloring Heuristic (GCH) and Tabu Search (TS) based methods. Initial findings show that a TS based method is more promising, so we focus on improving TS. We propose an algorithm called Tabu Search with Sampling and Perturbation (TSSP). Among the enhancements that we introduced are event sampling, a novel cost function and perturbation. Furthermore, we hybridize TSSP with Iterated Local Search (ILS). The second stage focuses on improving the quality of feasible solutions. We propose a variant of Simulated Annealing called Simulated Annealing with Reheating (SAR). SAR has three features: a novel neighborhood examination scheme, a new way of estimating local optima and a reheating scheme. The rigorous setting of initial and end temperature in conventional SA is bypassed in SAR. Precisely, reheating and cooling were applied at the right time and level, thus saving time allowing the search to be performed efficiently. One drawback of SAR is having to preset the composition of neighborhood structures for the datasets. We present an enhanced variant of the SAR algorithm called Simulated Annealing with Improved Reheating and Learning (SAIRL). We propose a reinforcement learning based method to obtain a suitable neighborhood structure composition for the search to operate effectively. We also propose to incorporate the average cost changes into the reheated temperature function. SAIRL eliminates the need for tuning parameters in conventional SA as well as neighborhood structures composition in SAR. Experiments were tested on four publicly available datasets namely Socha, International Timetabling Competition 2002 (ITC02), International Timetabling Competition 2007 (ITC07) and Hard. Our results are better or competitive when compared with other state of the art methods where new best results are obtained for many instances