Estudio comparativo de estrategias heurísticas de generación de soluciones para el problema de asignación de exámenes

Timetabling se refiere a un conjunto de problemas de optimización combinatoria, que intentan asignar recursos, sean aulas, docentes o intervalos de tiempo para distintas necesidades de estudiantes, cursos y exámenes. En el presente trabajo se aborda una de las variantes de este problema que busca agendar exámenes a distintos intervalos de tiempo, cumpliendo con las restricciones de que ningún alumno debe asistir a más de un examen en el mismo momento y, en la medida de lo posible, que tenga el mayor tiempo libre entre las evaluaciones. La cantidad de combinaciones a considerar para una instancia tamaño moderado hacen inviable la búsqueda de la solución óptima, debido al tiempo que demandaría encontrarla. En consecuencia, en este artículo se utilizan distintas estrategias para combinar heurísticas que permiten obtener una buena solución al problema en un intervalo de tiempo reducido. Las heurísticas mencionadas fueron probadas sobre un conjunto de instancias estándar de manera individual así como también combinadas de manera secuencial yjerárquica. En las pruebas realizadas se obtuvieron mejores resultados mediante el método jerárquico. Debido a lo anterior es posible afirmar la superioridad de este último método sobre los demás utilizados en el presente trabajo

Evolutionary algorithms for timetable problems

The university course timetabling problem is hard and time-consuming to solve. Profits from full automatisation of this process can be invaluable. This paper describes architecture and operation of two automatic timetabling systems. Both are based on evolutionary algorithms, with specialised genetic operators and penalty-based evaluation function. The paper covers two problem variations (theorethical and real-world), with different sets of constraints and different representations. Moreover, specification of both solutions and a proposal of hybrid system architecture is included

Solving Examination Timetabling Problem using Partial Exam Assignment with Great Deluge Algorithm

Constructing a quality solution for the examination timetable problem is a difficult task. This paper presents a partial exam assignment approach with great deluge algorithm as the improvement mechanism in order to generate good quality timetable. In this approach, exams are ordered based on graph heuristics and only selected exams (partial exams) are scheduled first and then improved using great deluge algorithm. The entire process continues until all of the exams have been scheduled. We implement the proposed technique on the Toronto benchmark datasets. Experimental results indicate that in all problem instances, this proposed method outperforms traditional great deluge algorithm and when comparing with the state-of-the-art approaches, our approach produces competitive solution for all instances, with some cases outperform other reported result

Problemas de optimización combinatoria: una propuesta que combina algoritmos genéticos y metaheurísticas

Timetabling se refiere al conjunto de problemas de optimización combinatoria que intentan asignar recursos, sean aulas, docentes o intervalos de tiempo, para distintas necesidades de estudiantes, cursos y exámenes. El presente trabajo se ocupa de una de las variantes de este problema, que busca agendar exámenes a distintos intervalos de tiempo, cumpliendo con las restricciones de que ningún alumno debe asistir a más de un examen en el mismo momento y que, en la medida de lo posible, tenga el mayor tiempo libre entre las evaluaciones. Los intervalos de tiempo no tienen restricciones en cuanto a la cantidad de exámenes que puedan asignárseles. Como estrategia de resolución se utiliza un algoritmo genético, que combina diversas heurísticas para la construcción de soluciones factibles que conforman la población inicial con la que trabaja el algoritmo. Dichas heurísticas fueron seleccionadas priorizando la calidad de la solución construida. También se definieron operadores de cruzamiento y mutación particulares, con el objetivo de mejorar la calidad de la solución resultante del proceso genético o, al menos, evitar la generación de soluciones no factibles. Mediante el algoritmo propuesto se alcanzaron soluciones relativamente buenas con pocas evaluaciones de la función objetivo y en un tiempo de ejecución razonable

Evolutionary Ruin And Stochastic Recreate: A Case Study On The Exam Timetabling Problem

This paper presents a new class of intelligent systems, called Evolutionary Ruin and Stochastic Recreate, that can learn and adapt to the changing enviroment. It improves the original Ruin and Recreate principle’s performance by incorporating an Evolutionary Ruin step which implements evolution within a single solution. In the proposed approach, a cycle of Solution Decomposition, Evolutionary Ruin and Stochastic Recreate continues until stopping conditions are reached. The Solution Decomposition step first uses some domain knowledge to break a solution down into its components and assign a score to each. The Evolutionary Ruin step then applies two operators (namely Selection and Mutation) to destroy a certain fraction of the entire solution. After the above steps, an input solution becomes partial and thus the resulting partial solution needs to be repaired. The repair is carried out by using the Stochastic Recreate step to reintroduce the removed items in a specific way (somewhat stochastic in order to have a better chance to jump out of the local optima), and then ask the underlying improvement heuristic whether this move will be accepted. These three steps are executed in sequence until a specific stopping condition is reached. Therefore, optimisation is achieved by solution disruption, iterative improvement and a stochastic constructive repair process performed within. Encouraging experimental results on exam timetabling problems are reported

Artificial Immune Algorithm for exams timetable

The Artificial Immune System is a novel optimization algorithm designed on the resilient behavior of the immune system of vertebrates. In this paper, this algorithm is used to solve the constrained optimization problem of creating a university exam schedule and assigning students and examiners to each of the sessions. Penalties are imposed on the violation of the constraints. Abolition of the penalties on the hard constraints in the first stage leads to feasible solutions. In the second stage, the algorithm further refines the search in obtaining optimal solutions, where the exam schedule matches the preferences of the examiners

A New Initialisation Method for Examination Timetabling Heuristics

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Timetabling problems are widespread, but are particularly prevalent in the educational domain. When sufficiently large, these are often only effectively tackled by timetabling meta-heuristics. The effectiveness of these in turn are often largely dependant on their initialisation protocols. There are a number of different initialisation approaches used in the literature for starting examination timetabling heuristics. We present a new iterative initialisation algorithm here --- which attempts to generate high-quality and legal solutions, to feed into a heuristic optimiser. The proposed approach is empirically verified on the ITC 2007 and Yeditepe benchmark sets. It is compared to popular initialisation approaches commonly employed in exam timetabling heuristics: the largest degree, largest weighted degree, largest enrollment, and saturation degree graph-colouring approaches, and random schedule allocation. The effectiveness of these approaches are also compared via incorporation in an exemplar evolutionary algorithm. The results show that the proposed method is capable of producing feasible solutions for all instances, with better quality and diversity compared to the alternative methods. It also leads to improved optimiser performance.Saudi Arabia Cultural Burea

Solving the Examination Timetabling Problem Using a Two-Phase Heuristic: The case of Sokoine University of Agriculture

Examination timetabling is an important operational problem in any academic institution. The problem involves assigning examinations and candidates to time periods and examination rooms while satisfying a set of specific constraints. An increased number of student enrollments, a wider variety of courses, and the growing flexibility of students' curricula have contributed to the growing challenge in preparing examination timetables. Since examination timetabling problems differ from one institution to another, in this paper we develop and investigate the impact of a two-phase heuristic that combines Graph-Colouring and Simulated Annealing at Sokoine University of Agriculture (SUA) in Tanzania. Computational results are presented which shows great improvement over the previous work on the same problem