Non-linear great deluge with learning mechanism for solving the course timetabling problem

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Эволюционный и фрагментарный подходы к задаче о равномерной нагрузке

В работе представлены результаты исследования задачи о равномерной нагрузке. Подобная задача возникает, в частности, при моделировании структурных элементов учебного процесса в высшем учебном заведении. Проблема заключается в распределении учебных дисциплин во времени таким образом, чтобы максимум нагрузки на студента был минимален. Предложены эволюционный подход для поиска оптимального решения и фрагментарный подход для построения допустимого решения задачи о равномерной нагрузке. Предложенные методы протестированы на наборе случайных задач.У роботі представлені результати дослідження задачі про рівномірне навантаження. Подібна задача виникає, зокрема, при моделюванні структурних елементів навчального процесу у вищому навчальному закладі. Проблема полягає у розподілі навчальних дисциплін у часі в такий спосіб, щоб максимальне навантаження на студента було мінімальним. Запропоновано еволюційний підхід для пошуку оптимальних розв’язків та фрагментарний підхід до побудови допустимих розв’язків. Запропоновані методи протестовані на наборі випадкових індивідуальних задач.In the paper the results of a study of the uniform loading problem are presented. A similar problem arises, in particular, when modeling structural components of a learning process in a university. The problem consists of allocating of courses in time so that student’s maximal loading was minimized. An evolutionary approach for an optimal solution search and fragmentary approach for a feasible solution construction are proposed. The proposed methods are tested on instances generated at random

Auto-Time Table Automation Tool using Genetic Algorithm

Time Table Computerization Tool is utilized for all educational pitch. The process includes two client features, the institution head and staff individuals. Institution head has all the rights to control and change the given information. All office accessible in the institution will be kept up through division's module. Administrator can pick any staff individual from the necessary office and can allocateto a prescribed class. During allocation the procedure followed with respective subjects in the institution for first, second, third, final year and so. Staff must be browsed with the necessary division. Administrator picks relating staff for their respective subjects and ration them. After staff dispersal, their time table will be shaped by the administrator which can be perceived by the staff individuals. Staff will have separate login framework, where they can login and can perceive their time tables. For other years same criteria will be followed in order to distribute the subjects among staff evenly. The Auto timetable schedule feature automates class, exam, and course forecast process for students, teachers, and different classrooms by taking into consideration all the possible. Furthermore, the timetable software integrates user-centric and simple-to-use tools to for educators to view, organize, and generate master and individual timetables for each teacher/ class/grade, develop personalized timetables, create and pin to-do lists, schedule substitute replacements for absent staff, manage and organize events on calendar, and much more on smartphone, tablet and computer devices

A hybrid algorithm for university course timetabling problem

A hybrid algorithm combining the genetic algorithm with the iterated local search algorithm is developed for solving university course timetabling problem. This hybrid algorithm combines the merits of genetic algorithm and iterated local search algorithm for its convergence to global optima at the same time avoiding being get trapped into local optima. This leads to intensification of the involved search space for solutions. It is applied on a number of benchmark university course timetabling problem instances of various complexities. Keywords: timetabling, optimization, metaheuristics, genetic algorithm, iterative local searc

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

Genetic algorithms with guided and local search strategies for university course timetabling

This article is posted here with permission from the IEEE - Copyright @ 2011 IEEEThe university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1

An evolutionary non-Linear great deluge approach for solving course timetabling problems

The aim of this paper is to extend our non-linear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolutionary computation approaches has increased and become an important technique in solving complex combinatorial optimisation problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. The initialisation process is capable of producing feasible solutions even for large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conducted experiments to evaluate the performance of the proposed algorithm and in particular, the contribution of the evolutionary operators. The results showed the effectiveness of the hybridisation between non-linear great deluge and evolutionary operators in solving university course timetabling problems

An evolutionary non-Linear great deluge approach for solving course timetabling problems

The aim of this paper is to extend our non-linear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolutionary computation approaches has increased and become an important technique in solving complex combinatorial optimisation problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. The initialisation process is capable of producing feasible solutions even for large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conducted experiments to evaluate the performance of the proposed algorithm and in particular, the contribution of the evolutionary operators. The results showed the effectiveness of the hybridisation between non-linear great deluge and evolutionary operators in solving university course timetabling problems