Dual Sequence Simulated Annealing with Round-Robin Approach for University Course Timetabling

Abdullah, Salwani; Shaker, Khalid; McCollum, Barry; McMullan, Paul

doi:10.1007/978-3-642-12139-5_1

Cited by 21 publications

(9 citation statements)

References 12 publications

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“…Difficult events are those events that have fewer available rooms/time slots that they can be assigned to. In some cases, a feasible solution is not be found by the construction heuristics in a single run [15], the solution can be repaired by neighbourhood search [9].…”

Section: Construction Phasementioning

confidence: 99%

“…Evolutionary based local search techniques like ants colony [5], genetic [6] and swarm intelligence [7] algorithms can be used to efficiently find a feasible timetable solution that would not need to be repaired, and can produce different solutions for a given problem as feasible solutions are not constructed in a deterministic fashion. Evolutionary strategies can also select sequence or combination of moves adaptively which in turn may improve the quality of the resulting timetable [8,9]. This paper describes a greedy ants' colony optimization strategy for solving the university course timetabling problem.…”

Section: Introductionmentioning

confidence: 99%

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Greedy Ants Colony Optimization Strategy for Solving the Curriculum Based University Course Timetabling Problem

Kenekayoro

Zipamone

2016

BJMCS

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Timetabling is a problem faced in all higher education institutions. The International Timetabling Competition (ITC) has published a dataset that can be used to test the quality of methods used to solve this problem. A number of meta-heuristic approaches have obtained good results when tested on the ITC dataset, however few have used the ant colony optimization technique, particularly on the ITC 2007 curriculum based university course timetabling problem. This study describes an ant system that solves the curriculum based university course timetabling problem and the quality of the algorithm is tested on the ITC 2007 dataset. The ant system was able to find feasible solutions in all instances of the dataset and close to optimal solutions in some instances. The ant system performs better than some published approaches, however results obtained are not as good as those obtained by the best published approaches. This study may be used as a benchmark for ant based algorithms that solve the curriculum based university course timetabling problem.

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Section: Construction Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Greedy Ants Colony Optimization Strategy for Solving the Curriculum Based University Course Timetabling Problem

Kenekayoro

Zipamone

2016

BJMCS

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“…In recent time, various heuristical approaches have been developed. Most of them come from the fields of operation research for example graph coloring [9], case-based reasoning [10] and artificial intelligence like simulated annealing [11], tabu search [12], local search [13], genetic algorithm [14][15] and ant colony optimization [16][17][18]. The pur-pose of this paper is to study on the Optimization Methods by categorizing between the classification of Optimization Methods and the classification of Soft Constraints and Hard Constraints.…”

Section: Introductionmentioning

confidence: 99%

A study on optimization methods for solving course timetabling problem in university

Mazlan¹,

Makhtar²,

Mohamed³

et al. 2018

IJET

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Course timetabling is one of the most important processes faced by any educational institution. However, the course timetabling process is time consuming and tiresome as it needs to be done for each regular semester. This paper aims to study on the Optimization methods to solve the course timetabling problem. The study is obtained and discussed by categorizing between the classification of Hard Constraint and Soft Constraint and the classification of Optimization Methods. From the study, it shows that Meta-Heuristics are the mostly method used in solving the course timetabling problem. It is concluded that this method is suitable for future used compared to other techniques studied. An analysis and observation will be carried out for the research future.

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“…Some of these approaches are great deluge [3], simulated annealing [4], tabu search [5], randomized descent method [6] and many other approaches [23,24,25].…”

Section: Introductionmentioning

confidence: 99%

MPCA-ARDA for solving course timetabling problems

Abuhamdah

Ayob

2011

2011 3rd Conference on Data Mining and Optimization (DMO)

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This work presents a hybridization between Multi-Neighborhood Particle Collision Algorithm (MPCA) and Adaptive Randomized Descent Algorithm (ARDA) acceptance criterion to solve university course timetabling problems. The aim of this work is to produce an effective algorithm for assigning a set of courses, lecturers and students to a specific number of rooms and timeslots, subject to a set of constraints. The structure of the MPCA-ARDA resembles a Hybrid Particle Collision Algorithm (HPCA) structure. The basic difference is that MPCA-ARDA hybridize MPCA and ARDA acceptance criterion, whilst HPCA, hybridize MPCA and great deluge acceptance criterion. In other words, MPCA-ARDA employ adaptive acceptance criterion, whilst HPCA, employ deterministic acceptance criterion. Therefore, MPCA-ARDA has better capability of escaping from local optima compared to HPCA and MPCA. MPCA-ARDA attempts to enhance the trial solution by exploring different neighborhood structures to overcome the limitation in HPCA and MPCA. Results tested on Socha benchmark datasets show that, MPCA-ARDA is able to produce significantly good quality solutions within a reasonable time and outperformed some other approaches in some instances.

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Dual Sequence Simulated Annealing with Round-Robin Approach for University Course Timetabling

Cited by 21 publications

References 12 publications

Greedy Ants Colony Optimization Strategy for Solving the Curriculum Based University Course Timetabling Problem

Greedy Ants Colony Optimization Strategy for Solving the Curriculum Based University Course Timetabling Problem

A study on optimization methods for solving course timetabling problem in university

MPCA-ARDA for solving course timetabling problems

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