Simulated annealing for the uncapacitated exam scheduling problem

Cheraitia, Meryem; Haddadi, Salim

doi:10.1504/ijmheur.2016.080266

Cited by 11 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…-Kempe chain: two sets of exams that do not conflict with each other, the timeslots are swapped. In a study conducted by [20], concluded that Kempe Chain is the right choice to optimize the solution of the examination timetabling problem.…”

Section: Low Level Heuristicmentioning

confidence: 99%

“…SA is a meta-heuristic search technique inspired by the process of heating a solid to its melting point and then cooling it [21]. The purpose of this method is to efficiently find the optimum solution without spending excessive time, and its ability to avoid local optimality is considered an advantage [20], [22]. In this study, SA is compared with different combinations of LSHs.…”

Section: Implementation Of the Simulated Annealing Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

The performance analysis of hyper-heuristics algorithms over examination timetabling problems

Muklason,

Tendio,

Angelita Depari

et al. 2024

IJ-AI

View full text Add to dashboard Cite

In general, uncapacitated exam timetabling is conducted manually, which can be time-consuming. Many studies aim to automate and optimize uncapacitated exam timetabling. However, pinpointing the most efficient algorithm is challenging since most studies assert that their algorithms surpass previous ones. To identify the optimal algorithm, this research evaluates the performance of four algorithms: Hill climbing (HC), simulated annealing (SA), great deluge (GD), and tabu search (TS) in addressing the exam timetabling problem. The Kempe chain operator’s influence on optimization solutions is also examined. A simple random method is employed to select the low-level heuristic (LLH). The Carter (Toronto) dataset served as the test material, with each algorithm undergoing 200,000 iterations for comparison. The results indicate that the TS algorithm is superior, providing the best solution in 13 instances. The use of a tabu list enhanced the search process’s efficiency by preventing redundant modifications. The Kempe chain LLH exhibited a tendency towards achieving better solutions.

show abstract

Section: Low Level Heuristicmentioning

confidence: 99%

Section: Implementation Of the Simulated Annealing Algorithmmentioning

confidence: 99%

The performance analysis of hyper-heuristics algorithms over examination timetabling problems

Muklason,

Tendio,

Angelita Depari

et al. 2024

IJ-AI

View full text Add to dashboard Cite

show abstract

“…In the field of QAP, Bölte and Thonemann [45] proposed an annealing schedule with cosine-based oscillation; Misevičius [6] presented an annealing schedule with Lundy-Mees-function-based oscillation. In the field of the uncapacitated exam scheduling problem, Dowsland and Thompson [46] recommended that less time be spent exploring the neighborhood at high temperatures where most moves are accepted; Cheraitia and Haddadi [47] presented an annealing schedule with increasing MCL based on geometry sequence.…”

Section: Variable Markov Chain Length Based On Arithmetic Sequencementioning

confidence: 99%

Enhanced List-Based Simulated Annealing Algorithm for Large-Scale Traveling Salesman Problem

et al. 2019

View full text Add to dashboard Cite

List-based simulated annealing (LBSA) algorithm is a novel simulated annealing algorithm where list-based cooling scheme is used to control the change of parameter temperature. Aiming to improve the efficiency of the LBSA algorithm for large-scale optimization problems, this paper proposes an enhanced LBSA (ELBSA) algorithm for solving large-scale traveling salesman problem (TSP). The ELBSA algorithm can drive more sampling at more suitable temperatures and from more promising neighborhoods. Specifically, heuristic augmented sampling strategy is used to ensure that more neighbors are from promising neighborhoods, systematic selection strategy is proposed to guarantee that each component of the current solution has a chance to be improved, and variable Markov chain length (VMCL), based on arithmetic sequence, is used to sample more neighbors at more suitable temperatures. Extensive experiments were performed to show the contribution of the heuristic augmented sampling strategy, and to verify the advantage of using systematic selection and VMCL. Comparative experiments, which were conducted on a wide range of large-scale TSP instances, show that the ELBSA algorithm is better than or competitive with most other state-of-the-art metaheuristics.INDEX TERMS Simulated annealing, traveling salesman problem, list-based cooling scheme, heuristic augmented sampling, systematic selection, variable Markov chain length.

show abstract

A systematic mapping study on solving university timetabling problems using meta-heuristic algorithms

Bashab

Ibrahim

AbedElgabar

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

Since university timetabling is commonly classified as a combinatorial optimisation problem, researchers tend to use optimisation approaches to reach the optimal timetable solution. Meta-heuristic algorithms have been presented as effective solutions as proven on their leverage over the last decade. Extensive literature studies have been published until today. However, a comprehensive systematic overview is missing. Therefore, this mapping study aimed to provide an organised view of the current state of the field and comprehensive awareness of the meta-heuristic approaches, by conducting meta-heuristic for solving university timetabling problems. In addition, the mapping study tried to highlight the intensity of publications over the last years, spotting the current trends and directions in the field of solving university timetabling problems, as well as having the work to provide guidance for future research by indicating the gaps and open questions to be fulfilled. Primary studies on mapping study that have been published in the last decade from 2009 until the first quarter of 2020, which consist of 131 publications, were selected as a benchmark for future research to solve university timetabling problems using meta-heuristic algorithms. The majority of the articles based on the publication type are hybrid methods (32%), in which the distribution of meta-heuristic algorithms the hybrid algorithms represent the higher application (31%). Likewise, the majority of the research is solution proposals (66%). The result of this study confirmed the efficiency and intensive application of the meta-heuristic algorithms in solving university timetabling problems, specifically the hybrid algorithms. A new trend of meta-heuristic algorithms such as grey wolf optimiser, cat swarm optimisation algorithm, Elitist self-adaptive step-size search and others with high expectations for reliable and satisfying results can be proposed to fill this gap.

show abstract

Simulated annealing for the uncapacitated exam scheduling problem

Cited by 11 publications

References 39 publications

The performance analysis of hyper-heuristics algorithms over examination timetabling problems

The performance analysis of hyper-heuristics algorithms over examination timetabling problems

Enhanced List-Based Simulated Annealing Algorithm for Large-Scale Traveling Salesman Problem

A systematic mapping study on solving university timetabling problems using meta-heuristic algorithms

Contact Info

Product

Resources

About