Towards constraint-based school timetabling

Marte, Michael

doi:10.1007/s10479-007-0218-9

Cited by 6 publications

(3 citation statements)

References 23 publications

(30 reference statements)

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“…There is a variety of real world high school timetabling problems exhibiting various characteristics from different countries and many different approaches have been proposed for a particular problem in hand, including a tiling algorithm (Kingston, 2005), constraint programming approach (Valouxis and Housos, 2003;Marte, 2007), Hopfield neural networks (Smith et al, 2003) and integer programming (Birbas et al, 2009). The tabu search or simulated annealing metaheuristics are frequently preferred as the single-point-based solution methods for high school timetabling.…”

Section: High School Timetabling Problemmentioning

confidence: 99%

A stochastic local search algorithm with adaptive acceptance for high-school timetabling

2014

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Automated high school timetabling is a challenging task. This problem is a well known hard computational problem which has been of interest to practitioners as well as researchers. High schools need to timetable their regular activities once per year, or even more frequently. The exact solvers may fail to find a solution for a given instance of the problem. A selection hyper-heuristic can be defined as an easy-to-implement, easy-to-maintain and effective 'heuristic to choose heuristics' to solve such computationally hard problems. This paper describes the approach of the team HySST (Hyper-heuristic Search Strategies and Timetabling) to high school timetabling which competed at the three rounds of the Third International Timetabling Competition. Despite this being the first attempt in high school timetabling, HySST generated the best new solutions for three given instances in Round 1 and gained the second place in Rounds 2 and 3 with a fairly standard stochastic search method but significantly enhanced by a selection hyper-heuristic with an adaptive acceptance mechanism.

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Section: High School Timetabling Problemmentioning

confidence: 99%

A stochastic local search algorithm with adaptive acceptance for high-school timetabling

2014

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“…Abramson, Dang and Krisnamoorthy (1999) [18] compared different cooling schedules used in simulated annealing for course timetabling. Marte (2004) [19] utilised a constraint programming approach for solving a timetabling problem at German secondary schools of Gymnasium type. Jakobsen, Bortfeld and Gehring (2006) [20] compared their tabu search algorithm to the results obtained during this previous study.…”

Section: High-school Course Timetabling and Metaheuristic Solutionsmentioning

confidence: 99%

The Interleaved Constructive Memetic Algorithm and its application to timetabling

Özcan

Parkes

Alkan

2012

Computers & Operations Research

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Timetabling problems are well known NP-hard constraint satisfaction, and real-world cases often have complicated and challenging structures. For such problems, we present a new hybrid method, "Interleaved Constructive Memetic Algorithm" (ICMA) that interleaves memetic algorithms with constructive methods. ICMA works using an active subset of all the events. Starting with a few events, in multiple construction stages ICMA increases the active set to eventually include all of them. At each stage, a memetic algorithm (MA) is applied to improve the current partial solution before the next construction step. We also describe a real-world course timetabling problem, the "Preparation School Timetabling Problem" (PSTP), which is of particular interest because it has a highly hierarchical structure arising from various organisational requirements. An important advantage of ICMA is that both the constructive heuristics and MA can be tailored to exploit such hierarchical structures. We give empirical results showing that ICMA performs better than the corresponding conventional MA on the PSTP.

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“…Traditionally, this problem has been considered in the Operations Research field and solved based on techniques including Graph Coloring (GC) Lara-Velázquez et al 2011), Integer/Linear Programming (IP/LP) Method (Schimmelpfeng, 2007;Rudová et al 2011;Sánchez-Partida et al 2014;Kassa, 2015), Constraint Satisfaction(s) Programming (CSPs) (Marte, 2007) and Benders' Partitioning (Sarin et al 2010). …”

Section: Introductionmentioning

confidence: 99%

Estudio de caso: Recocido simulado para mejorar el calendario educativo

Sánchez‐Partida

Baquela²,

Mora-Vargas

et al. 2016

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Introducción: En ocasiones, los problemas de Optimización Combinatoria (COP), tal como el Problema de Calendarización de horarios (CTTP), se puede resolver utilizando técnicas Investigación Operativa (IO); sin embargo, cuando el problema aumenta de tamaño, la búsqueda de una solución se vuelve más compleja. Este tipo de problema es NP-duro por lo que requiere de procedimientos como métodos metaheurísticos con el fin de resolver el problema. Este trabajo aborda un Problema real en una Institución de Educación Superior Mexicana acerca de la Calendarización de horarios basada en la Curricula (CB-CTT). Cada institución tiene sus propias reglas operacionales, por lo tanto la modelación del problema es único ya que conserva sus propias características. Primero como parte de la aportación a la solución del problema, se elaboró un Software de Mediación (MS) con el fin de organizar los datos en bruto y eliminar la restricción dura en relación con los planes de estudio. Posteriormente, el problema se dividió en cinco instancias, de acuerdo a los cursos que comparten el mismo espacio físico, los cuales fueron resueltos mediante el algoritmo tradicional de Recocido Simulado (SA).El problema fue resuelto de manera satisfactoria, obteniendo la asignación de 9620 sesiones en 174.5 horas aproximadamente, aportando una solución sin particionar el problema en dos subproblemas, impactando positivamente la reducción del tiempo de elaboración de los horarios, proporcionando un horario factible y sin errores para toda la universidad.Método: El Algoritmo de Recocido Simulado, cristalización simulada o enfriamiento simulado, es un algoritmo de búsqueda metaheurística para problemas de optimización; el objetivo general de este tipo de algoritmos es encontrar una buena aproximación al valor óptimo de una función en un espacio de búsqueda grande. A este valor se lo denomina "óptimo local u óptimo global".El nombre e inspiración viene del proceso de recocido del acero y cerámicas, una técnica que consiste en calentar y luego enfriar lentamente el material para variar sus propiedades físicas. El calor causa que los átomos aumenten su energía y que puedan así desplazarse de sus posiciones iniciales (un mínimo local de energía); el enfriamiento lento les da mayores probabilidades de recristalizar en configuraciones con menor energía que la inicial (mínimo global). El método fue descrito independientemente por Scott Kirkpatrick, C. Daniel Gelatt y Mario P. Vecchi en 1983.

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Towards constraint-based school timetabling

Cited by 6 publications

References 23 publications

A stochastic local search algorithm with adaptive acceptance for high-school timetabling

A stochastic local search algorithm with adaptive acceptance for high-school timetabling

The Interleaved Constructive Memetic Algorithm and its application to timetabling

Estudio de caso: Recocido simulado para mejorar el calendario educativo

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