Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach

The university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed approach generates solutions into two phases (initial and improvement). A new LLH named “least possible rooms left” has been developed and proposed to schedule events. Both datasets of international timetabling competition (ITC) i.e., ITC 2002 and ITC 2007 are used to evaluate the proposed method. Experimental results indicate that the proposed low-level heuristic helps to schedule events at the initial stage. When compared with other LLH’s, the proposed LLH schedule more events for 14 and 15 data instances out of 24 and 20 data instances of ITC 2002 and ITC 2007, respectively. The experimental study shows that HH PSO gets a lower soft constraint violation rate on seven and six data instances of ITC 2007 and ITC 2002, respectively. This research has concluded the proposed LLH can get a feasible solution if prioritized.

Model Paralelisasi Algoritma Genetika Terpandu pada Sistem Penjadwalan Kuliah Universitas dengan Alokasi Waktu Dinamis

One of the many techniques used to solve the University Course Timetable Problem (UCTP) is Genetic Algorithm (GA) which is a technique in the field of Evolutionary Computation. However, GA has high computational complexity due to the large number of evolutionary operators that must be performed during the evolutionary process, so it takes a long time to produce an optimal timetable. The computation time will also increase when the number of optimized variables is very large, such as in UCTP. Of course, this makes the application less reliable by users. Therefore, this article proposes a parallelization model for GA to reduce computation time in solving UCTP problems. The proposed AG is designed with a multithreading CPU scheme and implements a guided creep mutation mechanism and eliminates the recombination mechanism to reduce more computation time. The proposed system was tested and evaluated using two different UCTP datasets from the University of Technology Yogyakarta which contained 878 and 1140 lecture meetings in even and odd semesters. Unlike the previous ones, this study discusses UCTP with dynamic time slots where the duration of the lecture depends on the course credits. From the tests that have been done, it is found that the GA that was built is able to generate optimal course timetable without any clashes in a relatively fast time, that is less than 60 minutes for 1140 lecture meetings and less than 20 minutes for 878 lecture meetings. The use of the multithreading CPU model has succeeded in reducing computation time by 62% when compared to the conventional model which only uses one thread.

An Intelligent Technique for Solving Timetable Problem

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 to tune the best parameter combinations for the factors and levels. The tuned parameters of CSA are applied on datasets in separate experiment. The results show that the proposed CSA is superior to generate solutions in reasonable CPU time when compared with other literature techniques.

Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.

Timetable Generation

Evidence based on ongoing published research shows that timetabling has been a challenge for over two decades. There is a growing need in higher education for a learner-centered solution focused on individual preferences. In the authors' earlier published work, students' group assessment information was mined to determine individualized achievements and predict future performance. In this paper, they extend the work to present a solution that uses students' individualized achievements, expected future performance, and historical registration records to discover students' registration timing patterns, as well as the most appropriate courses for registration. Such information is then processed to build the most suitable timetable for each student in the following semester. Faculty members' time preferences are also predicted based on historical teaching time patterns and course teaching preferences. The authors propose a modified frequent pattern (FP)-tree algorithm to process the predicted information. This results in clustering students to solve the timetable problem based on the predicted courses for registration. Then, it divides the timetable problem into subproblems for resolution. This ensures that time will not conflict within the generated timetables while satisfying both the hard and soft constraints. Both students' and faculty members timetabling preferences are met (88.8% and 85%).

A Review of Optimization Algorithms for University Timetable Scheduling

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 solutions for the university student course timetable problem. The reviewed algorithms take into account the demands of institutional constraints for course timetable management.

New hybrid flower pollination algorithm with dragonfly algorithm and jaccard index to enhance solving university course timetable problem

University course timetable problem (UCTP) is one of the problems on which many researches have been conducted over the years because of its importance in academic institutions. A nature-inspired metaheuristic optimization algorithm, Flower Pollination Algorithm (FPA) has been adapted, so-called Adapted FPA (AFPA), to cope with UCTP in the previous work. However, AFPA suffers from the stagnation problem because of the non-diversity in the population. To improve the diversity of the population, this work introduces new Hybrid FPA with two variants: JFPA provided the Jaccard index to determine similarities among categorical data and the greedy selection mechanism to improve the selection of the random solution, and DFPA applied the navigational characteristics of the Dragonfly Algorithm (DA) to help in the neighborhood relationship. The results in this study indicate that the proposed algorithms have better exploration ability and fast convergence rate in comparison; JFPA outperforms AFPA in 3 out of 4 datasets for both small and large datasets, and DFPA outperforms AFPA and GA in all datasets while it outperforms PSO in 3 out of 4 small datasets and 2 complex large datasets.

Kajian Algoritma Optimasi Penjadwalan Mata Kuliah

Penjadwalan mata kuliah merupakan hal penting yang dilakukan pada awal semester akademik. Proses penyusunan jadwal kuliah secara manual seringkali mengalami kesulitan karena terdapat beberapa konstrain sehingga membutuhkan waktu yang lama. Penelitian ini bertujuan mengkaji algoritma-algoritma yang sesuai dengan masalah penjadwalan mata kuliah. Pencarian dan analisis dilakukan terhadap literatur yang berkaitan dengan optimasi penjadwalan. Proses pencarian literatur dilakukan pada Google Scholar dan Science Direct dengan memasukkan kata kunci utama “course timetable”, “university timetable problem”, “school scheduling”, dan “algoritma penjadwalan”. Hasil analisis literatur meliputi sebaran domain, analisis algoritma serta gap dari penelitian sebelumnya. Pada penelitian sebelumnya terdapat kekurangan seperti algoritma yang tidak dapat menghasilkan solusi optimal. Hasil sebaran domain yang diperoleh ialah universitas dan sekolah dengan persentase 88% dan 12% dari keseluruhan makalah. Adanya temuan 14 sebaran algoritma dapat diklasifikasikan menjadi 3 metode, yaitu heuristic, metaheuristic, dan hyper-heuristic. Berdasarkan hasil analisis, dapat diberikan beberapa rekomendasi. Untuk optimasi yang cepat, Simulated Annealing (SA) dapat menjadi solusi karena mampu menghasilkan solusi dengan waktu 0.481-10.102s. Untuk solusi waktu dan nilai fitness terbaik, Genetic Algorithm (GA) dapat menjadi solusi karena mampu menghasilkan solusi dengan waktu 0.964-73.461s dan nilai fitness 1.