Solving examination timetabling problem within a hyper-heuristic framework

Scheduling exams in colleges are a complicated job that is difficult to solve conventionally. Exam timetabling is one of the combinatorial optimization problems where there is no exact algorithm that can answer the problem with the optimum solution and minimum time possible. This study investigated the University of Toronto benchmark dataset, which provides 13 real instances regarding the scheduling of course exams from various institutions. The hard constraints for not violate the number of time slots must be fulfilled while paying attention to fitness and running time. Algorithm of largest degree, hill climbing, and tabu search within a hyper-heuristic framework is investigated with regards to each performance. This study shows that the Tabu search algorithm produces much lower penalty value for all datasets by reducing 18-58% from the initial solution.

An integer mathematical model for the problem of timetabling university exams

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.

Genetic Algorithm for Exam Timetabling Problem - A Specific Case for Japanese University Final Presentation Timetabling

This paper presents a Genetic Algorithm approach to solve a specific examination timetabling problem which is common in Japanese Universities. The model is programmed in Excel VBA programming language, which can be run on the Microsoft Office Excel worksheets directly. The model uses direct chromosome representation. To satisfy hard and soft constraints, constraint-based initialization operation, constraint-based crossover operation and penalty points system are implemented. To further improve the result quality of the algorithm, this paper designed an improvement called initial population pre-training. The proposed model was tested by the real data from Sophia University, Tokyo, Japan. The model shows acceptable results, and the comparison of results proves that the initial population pre-training approach can improve the result quality.

A New Optimization on Harmony Search Algorithm for Exam Timetabling System

Preparing an optimal exam timetable in universities is challenging for head of departments, especially for colleges with multiple number of departments, courses, and students. Harmony search algorithm is used by many researchers to solve this problem but none of them could get an optimal solution. In this paper, a new algorithm which is called optimised harmony search algorithm with distributed selections is proposed by optimising the harmony search algorithm and the genetic algorithm. The new algorithm could satisfy hard, soft, and general constraints and generate an optimal exam timetable for a huge number of courses and students. The proposed algorithm is implemented and applied on Jadara University, the algorithm uses an upper triangular matrix to reduce relationships and memory usage, a three-dimensional matrix to ease the exams timetable management a deterministic number generators to eliminate timeslots conflicts, and backtracking algorithm to enhance the population selections. Lecturers and students’ feedback showed a good satisfaction as well the system results.

A study on university course and exam timetabling problems and methods: an optimization survey

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.

Solving examination timetabling problem in UniSZA using ant colony optimization

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 be used to eliminate various types of problems for the purpose of optimising the scheduling management system and minimising the number of conflicts. The key of this feature is to simplify and find shorter paths based on index pheromone updating (occurrence matrix). With appropriate algorithm and using efficient techniques, the schedule and assignation allocation can be improved. The approach is applied according to the data set instance that has been gathered. Therefore, performance evaluation and result are used to formulate the proposed approach. This is to determine whether it is reliable and efficient in managing feasible final exam timetables for further use.

A Multi-Phase Hybrid Metaheuristics Approach for the Exam Timetabling

We propose a Multi-Phase Hybrid Metaheuristics approach for solving the Exam Timetabling Problem (ETP). This approach is defined with three phases: pre-processing phase, construction phase and enhancement phase. The pre-processing phase relies on our variable ordering heuristic as well as a form of transitive closure for discovering implicit constraints. The construction phase uses a variant of the Tabu Search with conflicts dictionary. The enhancement phase includes Hill Climbing (HC), Simulated Annealing (SA) and our updated version of the extended “Great Deluge” algorithm. In order to evaluate the performance of the different phases of our proposed approach, we conducted several experiments on instances taken from ITC 2007 benchmarking datasets. The results are very promising and competitive with the well known ETP solvers.