2018 International Conference on Information Management and Technology (ICIMTech) 2018
DOI: 10.1109/icimtech.2018.8528111
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Reducing the Timeslot Used in Examination Timetable Problem

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Cited by 3 publications
(5 citation statements)
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“…Figure 1 below shows the flow diagram for the comprehensive review process. Reference Title Year Country [11] Developing a mobile-based application system to accelerate the efficiency of the course rescheduling process Malaysia [12] Modified and hybridised bi-objective firefly algorithms for university course scheduling Thailand [13] A general mathematical model for university courses timetabling: Implementation to a public university in Malaysia Malaysia [14] A genetic algorithm for the real-world university course timetabling problem Malaysia [15] Grouping and heuristics for a multi-stage class timetabling system Malaysia [16] Hybrid whale optimization algorithm for solving timetabling problems of ITC 2019 Indonesia [17] Investigation of heuristic orderings with a perturbation for finding feasibility in solving real-world university course timetabling problem Malaysia [18] Lecturer-course assignment model in national joint courses program to improve education quality and lecturers' time preference Indonesia [19] A compromise programming for multi-objective task assignment problem Vietnam [20] A hybrid of heuristic orderings and variable neighbourhood descent for a real-life university course timetabling problem Malaysia [21] An SHO-based approach to timetable scheduling: a case study Vietnam [22] Application of genetic algorithm to optimize lecture scheduling based on lecturers' teaching day willingness Indonesia [23] Automation and optimization of course timetabling using the iterated local search hyper-heuristic algorithm with the problem domain from the 2019 international timetabling competition Indonesia [24] Class scheduling framework using decorator and facade design pattern Philippines [25] Effective solution of university course timetabling using particle swarm optimizer based hyper heuristic approach Malaysia [26] Lecturer teaching scheduling that minimizes the difference of total teaching load using goal programming Indonesia [27] Multi-agent class timetabling for higher educational institutions using Prometheus platform Philippines [28] Particle swarm optimisation variants and its hybridisation ratios for generating cost-effective educational course timetables Thailand [29] Stemming the educational timetable problems Indonesia [18] University course timetabling model in joint courses program to minimize the number of unserved requests Indonesia [30] An effective hybrid local search approach for the post enrolment course timetabling problem Malaysia...…”
Section: Discussionmentioning
confidence: 99%
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“…Figure 1 below shows the flow diagram for the comprehensive review process. Reference Title Year Country [11] Developing a mobile-based application system to accelerate the efficiency of the course rescheduling process Malaysia [12] Modified and hybridised bi-objective firefly algorithms for university course scheduling Thailand [13] A general mathematical model for university courses timetabling: Implementation to a public university in Malaysia Malaysia [14] A genetic algorithm for the real-world university course timetabling problem Malaysia [15] Grouping and heuristics for a multi-stage class timetabling system Malaysia [16] Hybrid whale optimization algorithm for solving timetabling problems of ITC 2019 Indonesia [17] Investigation of heuristic orderings with a perturbation for finding feasibility in solving real-world university course timetabling problem Malaysia [18] Lecturer-course assignment model in national joint courses program to improve education quality and lecturers' time preference Indonesia [19] A compromise programming for multi-objective task assignment problem Vietnam [20] A hybrid of heuristic orderings and variable neighbourhood descent for a real-life university course timetabling problem Malaysia [21] An SHO-based approach to timetable scheduling: a case study Vietnam [22] Application of genetic algorithm to optimize lecture scheduling based on lecturers' teaching day willingness Indonesia [23] Automation and optimization of course timetabling using the iterated local search hyper-heuristic algorithm with the problem domain from the 2019 international timetabling competition Indonesia [24] Class scheduling framework using decorator and facade design pattern Philippines [25] Effective solution of university course timetabling using particle swarm optimizer based hyper heuristic approach Malaysia [26] Lecturer teaching scheduling that minimizes the difference of total teaching load using goal programming Indonesia [27] Multi-agent class timetabling for higher educational institutions using Prometheus platform Philippines [28] Particle swarm optimisation variants and its hybridisation ratios for generating cost-effective educational course timetables Thailand [29] Stemming the educational timetable problems Indonesia [18] University course timetabling model in joint courses program to minimize the number of unserved requests Indonesia [30] An effective hybrid local search approach for the post enrolment course timetabling problem Malaysia...…”
Section: Discussionmentioning
confidence: 99%
“…[28] General framework with four layers model with Layer 1 to Layer 3 is called Stemming Phase involving preprocessing steps, and the Layer 4 is called Solution Finding Phase with enhanced simulated annealing-based search [29] Model leveraged by artificial bee colonies (ABC), cloud theory-based simulated annealing (CTB-SA), and genetic algorithm (GA) [18] Two phase hybrid local search algorithm with Tabu Search with Sampling and Perturbation with Iterated Local Search (TSSP-ILS) and Simulated Annealing with Reheating (SAR) with two preliminary runs (SAR-2P) [30] Two-stage heuristic algorithm consists of Lecturer Grouping Stage and Group Allocation Stage [33] Two hybrid variants of flower pollination algorithm (FPA) which were Jaccard FPA (JFPA) which uses the Jaccard index and a greedy selection mechanism, and Dragonfly FPA (DFPA) which incorporates the navigational traits of the dragonfly algorithm (DA) [34] Hybridization of Self-Adaptive and Simulated Annealing Hyper-Heuristic approach [35] Sequential constructive algorithm and Fuzzy Logic [36] Multi-Agent System (MAS) incorporating Integer Programming (IP)…”
Section: Optimization Methodsmentioning
confidence: 99%
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