A time-dependent metaheuristic algorithm for post enrolment-based course timetabling

A metaheuristic-based algorithm is presented for the post enrolment-based course timetabling problem used in track-2 of the Second International Timetabling Competition (ITC2007). The featured algorithm operates in three distinct stages—a constructive phase followed by two separate phases of simulated annealing—and is time dependent, due to the fact that various run-time parameters are calculated automatically according to the amount of computation time available. Overall, the method produces results in line with the official finalists to the timetabling competition, though experiments show that this algorithm also seems to find certain instances more difficult to solve than others. A number of reasons for this latter feature are discussed

Design, Engineering, and Experimental Analysis of a Simulated Annealing Approach to the Post-Enrolment Course Timetabling Problem

The post-enrolment course timetabling (PE-CTT) is one of the most studied timetabling problems, for which many instances and results are available. In this work we design a metaheuristic approach based on Simulated Annealing to solve the PE-CTT. We consider all the different variants of the problem that have been proposed in the literature and we perform a comprehensive experimental analysis on all the public instances available. The outcome is that our solver, properly engineered and tuned, performs very well on all cases, providing the new best known results on many instances and state-of-the-art values for the others

A methodology for determining an effective subset of heuristics in selection hyper-heuristics

We address the important step of determining an effective subset of heuristics in selection hyper-heuristics. Little attention has been devoted to this in the literature, and the decision is left at the discretion of the investigator. The performance of a hyper-heuristic depends on the quality and size of the heuristic pool. Using more than one heuristic is generally advantageous, however, an unnecessary large pool can decrease the performance of adaptive approaches. Our goal is to bring methodological rigour to this step. The proposed methodology uses non-parametric statistics and fitness landscape measurements from an available set of heuristics and benchmark instances, in order to produce a compact subset of effective heuristics for the underlying problem. We also propose a new iterated local search hyper-heuristic usingmulti-armed banditscoupled with a change detection mechanism. The methodology is tested on two real-world optimisation problems: course timetabling and vehicle routing. The proposed hyper-heuristic with a compact heuristic pool, outperforms state-of-the-art hyper-heuristics and competes with problem-specific methods in course timetabling, even producing new best-known solutions in 5 out of the 24 studied instances

Modelos de Programación Entera para el Problema de Aignación de Horarios en Cursos Universitarios

Timetabling scheduling is classified as a combinatorial problem for which there are multiple solution alternatives, including the integer programming. However, the modeling of operational rules and the size of real problems makes its use not common compared to other techniques. This article proposes an integer programming model (IP) model, which addresses the scheduling problem made up of variables associated with time slots, subject and assigned teacher. Parameters such as the number of minimum and maximum time slots to be taught by the teacher, time slot time, teacher availability, available classrooms and estimated cost of dissatisfaction generated by the assigned schedule are also included. Seven hard and seventeen soft constraints are integrated into the model, which provide higher quality to the final schedule solution. The IP model is validated with a global objective function, in which experiments, and results obtained in real instances of the Universidad de la Salle (ULS) are reported. The new solution approach offers improvements in the final schedules, as well as the interaction with the users during its construction. Finally, in the conclusions of the work, the design and development of a system that provides support for decisions is discussed, referencing suggestions for future developments.La programación de horarios se clasifica como un problema combinatorio para el que existen múltiples alternativas de solución incluyendo entre ellas la programación entera. Sin embargo, el modelado de reglas operativas y el tamaño de problemas reales hace que su uso no sea común comparado con otras técnicas. El presente artículo propone un modelo de programación entera (IP), que aborda el problema de programación de horarios conformado por variables asociadas con franjas horarias, asignatura y docente asignado. También se incluyen parámetros como número de franjas horarias mínimas y máximas a impartir por el docente, tiempo de franja horaria, disponibilidad de docente, salones disponibles y costo estimado de insatisfacción generado por el horario asignado. En el modelo se integran siete restricciones duras y diecisiete blandas que proporcionan mayor calidad a la solución final de horarios. Se valida el modelo IP con una función objetivo global, en el que se reportan experimentos y resultados obtenidos en instancias reales de la Universidad de la Salle (ULS). El nuevo enfoque de solución ofrece mejoras en los horarios finales, así como la interacción con los usuarios durante su construcción. Finalmente, en las conclusiones del trabajo se discute el diseño y desarrollo de un sistema que brinda soporte a las decisiones, referenciando sugerencias para futuros desarrollos

Analysing the effects of solution space connectivity with an effective metaheuristic for the course timetabling problem

This paper provides a mathematical treatment of the NP-hard post enrolment-based course timetabling problem and presents a powerful two-stage metaheuristic-based algorithm to approximately solve it. We focus particularly on the issue of solution space connectivity and demonstrate that when this is increased via specialised neighbourhood operators, the quality of solutions achieved is generally enhanced. Across a well-known suite of benchmark problem instances, our proposed algorithm is shown to produce results that are superior to all other methods appearing in the literature; however, we also make note of those instances where our algorithm struggles in comparison to others and offer evidence as to why

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