Monte Carlo Tree Search in Finding Feasible Solutions for Course Timetabling Problem

Goh, Say Leng; Kendall, Graham; Sabar, Nasser R.

doi:10.18517/ijaseit.9.6.10224

Cited by 13 publications

(3 citation statements)

References 19 publications

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“…The proposed approaches may require flexibility as timetables can be assigned and unassigned dynamically. One particular challenge arises with the MCTS approach by Goh, Kendall, and Sabar [46], which faces difficulties due to limited search space connectivity when compared to local search methods like TS. However, the MAS approach used in incorporating integer programming by Abdalla et al, [47] involved the stated advantage of MAS as it facilitates optimization by guiding agents towards more promising search spaces in the pursuit of finding the best solution.…”

Section: Discussion On Optimization Methodsmentioning

confidence: 99%

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Optimization in University Student Timetables: A Comprehensive Literature Review

Farradini Zabidee,

Muhamad Hariz Muhamad Adnan

2024

ARASET

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The University Course Timetabling Problem presents a complex optimization problem as it seeks to meet the diverse requirements and constraints of a university's academic program through efficient timetabling. The objective of this research is to conduct comprehensive review of existing studies on optimization approaches in optimizing university timetable problems. The aim of the comprehensive studies is to further analyse the existing studies to discuss comparative studies to compare the performance of the approach methods in satisfying the constraints involved in university timetabling. The methodology used for this paper is systematic literature review which brought three main themes such constraints, optimization approaches, and datasets involved in University Course Timetabling Problem. The result shown that the majority of approaches involved hybrid approaches. In conclusion, this comprehensive review summarizes the constraints, optimization approach and datasets involved University Course Timetabling Problem in higher educational institutions of ASEAN countries.

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Section: Discussion On Optimization Methodsmentioning

confidence: 99%

“…Goh, Kendall and Sabar [46] suggested MCTS as a solution for UCTP. Socha, ITC 2002, and ITC 2007 datasets were implemented for testing the algorithm.…”

Section: Heuristicmentioning

confidence: 99%

Optimization in University Student Timetables: A Comprehensive Literature Review

Farradini Zabidee,

Muhamad Hariz Muhamad Adnan

2024

ARASET

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“…Authors 32 have given a comprehensive review of hyper-heuristic methods, nature of heuristic space with data sets used in hyper-heuristic models. Although research on metaheuristic [33][34][35]…”

Section: Literature Reviewmentioning

confidence: 99%

Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach

et al. 2021

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The university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed approach generates solutions into two phases (initial and improvement). A new LLH named “least possible rooms left” has been developed and proposed to schedule events. Both datasets of international timetabling competition (ITC) i.e., ITC 2002 and ITC 2007 are used to evaluate the proposed method. Experimental results indicate that the proposed low-level heuristic helps to schedule events at the initial stage. When compared with other LLH’s, the proposed LLH schedule more events for 14 and 15 data instances out of 24 and 20 data instances of ITC 2002 and ITC 2007, respectively. The experimental study shows that HH PSO gets a lower soft constraint violation rate on seven and six data instances of ITC 2007 and ITC 2002, respectively. This research has concluded the proposed LLH can get a feasible solution if prioritized.

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Hybrid particle swarm optimization with particle elimination for the high school timetabling problem

et al. 2020

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In this paper, a PSO-based algorithm that hybridized Particle Swarm Optimization (PSO) and Hill Climbing (HC) is applied to high school timetabling problem. This hybrid has two features, a novel solution transformation and particle elimination. The proposed methodologies are tested on the XHSTT-2014 dataset (which is relatively new for the school timetabling problem) plus other additional instances. The experimental results show that the proposed algorithm is efective in solving small and medium instances compared to standalone HC and better than the conventional PSO for most instances. In a comparison to the state of the art methods, it achieved the lowest mean of soft constraint violations for 7 instances and the lowest mean of hard constraint violations for 1 instance.

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Monte Carlo Tree Search in Finding Feasible Solutions for Course Timetabling Problem

Cited by 13 publications

References 19 publications

Optimization in University Student Timetables: A Comprehensive Literature Review

Optimization in University Student Timetables: A Comprehensive Literature Review

Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach

Hybrid particle swarm optimization with particle elimination for the high school timetabling problem

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