An Efficient Simulated Annealing Algorithm for Feasible Solutions of Course Timetabling

Frausto–Solís, Juan; Alonso-Pecina, Federico; Mora-Vargas, Jaime

doi:10.1007/978-3-540-88636-5_64

Cited by 15 publications

(6 citation statements)

References 5 publications

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“…En este trabajo se enfoca a generar soluciones aceptables al problema de calendarización de horarios, mediante el uso de algoritmos Metaheurísticos. Existen una diversa cantidad de enfoques que han sido utilizados para resolver el problema de calendarización de horarios como el coloreo de grafos [5], programación de Satisfacción de Restricciones(CSP) Métodos Basados[7], IP/LP (programación entera/programación lineal) [6], Algoritmos Genéticos [2, 8, 9], Algoritmos Meméticos [10, 11], Búsqueda Tabú [12, 13], Recosido Simulado [14], Búsqueda Local [15], Mejor-Peor sistema de Hormigas (BWAS) y las optimización por colonia de hormigas [16] y enfoque hiperheurístico [17].…”

Section: Introductionunclassified

Comparativa de algoritmos bioinspirados aplicados al problema de calendarización de horarios

Aguilar¹,

Carpío²,

Soberanes³

et al. 2015

RCS

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Resumen. El problema de calendarización de eventos está presente en diversas organizaciones como lo son escuelas, hospitales, centros de transporte, etc. La calendarización de actividades en una universidad tiene como propósito el garantizar que todos los estudiantes tomen sus asignaturas requeridas apegándose a los recursos que están disponibles. El conjunto de restricciones que debe contemplarse en el diseño de horarios involucra a los alumnos, maestros e infraestructura. En este trabajo se muestra que mediante la aplicación de algoritmos Genéticos, Memético y Sistema Inmune se generan soluciones aceptables, para el problema de calendarización de tareas, Los algoritmos son aplicados a instancias reales del Instituto Tecnológico de León y sus resultados son comparables con los de un experto humano.

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Section: Introductionunclassified

Comparativa de algoritmos bioinspirados aplicados al problema de calendarización de horarios

Aguilar¹,

Carpío²,

Soberanes³

et al. 2015

RCS

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show abstract

“…During the last three decades, various approaches have been intensively applied to solve course timetabling problem. Some of the common techniques are the Ant Colony Optimization [3], Tabu Search [4], Simulated Annealing [5], Evolutionary Algorithm [6], Hybrid Heuristic Algorithm [7] and Hyper Heuristic approach [8].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Ant Colony systems for course timetabling problems

Ayob

Jaradat

2009

2009 2nd Conference on Data Mining and Optimization

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The University Course Timetabling is a complex optimization Problem which is difficult to solve for optimality. It involves assigning lectures to a fixed number of timeslots and rooms; while satisfying some constraints. The goal is to construct a feasible timetable and satisfy soft constraints as much as possible. In this study, we apply two hybrids Ant Colony Systems, namely the Simulated Annealing with Ant Colony System (ACS-SA), and Tabu Search with Ant Colony System (ACS-TS) to solve the university course timetabling, a number of ants in the ACS construct a complete assignment of courses to timeslots. Based on a pre-ordered list of courses, the ants probabilistically choose the timeslot for the given course, guided by heuristic information and stigmergic information. We test both ACS algorithms over the Socha's benchmark course timetabling problem. We also compare our results with those obtained by other methodologies recent literature has illustrated. Experimental results showed that both ACS-SA and ACS-TS produces good quality solutions and outperforms previously applied Ant algorithms; they also outperform other methodologies tested on Socha's benchmark test instances, and approaches on some benchmark instances. We believe that these hybrid ACS algorithms are also valid for other types of combinational optimization problems.

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“…This set of instances has previously been used in the literature for testing algorithms' ability to find feasibility only [19,23,31,35,38,41,46] and they differ to the 24 competition instances in that they do not consider Constraints (8)- (10), and only some are known to feature perfect solutions (though they do all feature a feasible solution). 8 In our tests both algorithms were executed ten times on the forty largest instances from this set, of which 17 are known to feature perfect solutions.…”

Section: One-stage Versus Two-stage Approachesmentioning

confidence: 99%

Analysing the effects of solution space connectivity with an effective metaheuristic for the course timetabling problem

Lewis

Thompson

2015

European Journal of Operational Research

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This paper provides a mathematical treatment of the NP-hard post enrolment-based course timetabling problem and presents a powerful two-stage metaheuristic-based algorithm to approximately solve it. We focus particularly on the issue of solution space connectivity and demonstrate that when this is increased via specialised neighbourhood operators, the quality of solutions achieved is generally enhanced. Across a well-known suite of benchmark problem instances, our proposed algorithm is shown to produce results that are superior to all other methods appearing in the literature; however, we also make note of those instances where our algorithm struggles in comparison to others and offer evidence as to why.

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An Efficient Simulated Annealing Algorithm for Feasible Solutions of Course Timetabling

Cited by 15 publications

References 5 publications

Comparativa de algoritmos bioinspirados aplicados al problema de calendarización de horarios

Comparativa de algoritmos bioinspirados aplicados al problema de calendarización de horarios

Hybrid Ant Colony systems for course timetabling problems

Analysing the effects of solution space connectivity with an effective metaheuristic for the course timetabling problem

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