University course timetabling model using ant colony optimization algorithm approach

Mazlan, Munirah; Makhtar, Mokhairi; Mohamed, Mohamad Afendee

doi:10.11591/ijeecs.v13.i1.pp72-76

Cited by 19 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In order to achieve a specific objective of the organization, this resource may be a person, property, equipment or capital.The personnel assignment problem was first addressed by Votaw and Orden in 1952 [14], illustrating a method of assignment that helps to match a task to the most appropriate agent, in order to achieve the most efficient result. Many articles address the RAPunder several variances that appear in several areas such as: staff transfer/mobility [15]- [18], scheduling [19], [20], Timetabling Problem [21]- [23], maintenance [24] and also team formation [21], [25]- [28].…”

Section: About the Np-hard Problems: Resource Assignment And Vr Problemsmentioning

confidence: 99%

“…In the literature, the timetabling problem appears in applications related to time slot assignment such as conferences, exams, courses, meetings, etc. [22], [23]. On the other hand, the assignment problem arises in robotics and control theory due to applications involving the assignment of tasks or objectives to agents (Robots) [30].…”

Section: About the Np-hard Problems: Resource Assignment And Vr Problemsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

2020

IJATCSE

View full text Add to dashboard Cite

Artificial intelligence (AI) techniques play an important role in the development and optimization of complex problems in various fields. This study presents a systematic analysis of previously publications on the use of AI techniques to optimize the NP-hard problems in the area of human resource assignment to multi-constrained activities in order to increase the productivity and performance of a company, and in the area of vehicle routing in order to cost minimization. These two problems belonged to the NP-hard family. Among these AI techniques used for a better optimization of this type of problems, we discuss the genetic algorithms (GA), hybrid GA, parallel GA, GA with Big Data and improved AI techniques with special features are also discussed and presented through the different contributions of the researchers.

show abstract

Section: About the Np-hard Problems: Resource Assignment And Vr Problemsmentioning

confidence: 99%

Section: About the Np-hard Problems: Resource Assignment And Vr Problemsmentioning

confidence: 99%

Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

2020

IJATCSE

View full text Add to dashboard Cite

show abstract

“…It arranges a series of examinations in a set of time slots and no student is assigned to more than one exam at a time. Exams are assigned to days and time windows within those days based on availability, fair distribution of student workload, and room capacities [8]- [11]. It entails arranging a set of examinations to set of exam periods in such a way that the problem's hard constraints are met while soft constraints are minimized.…”

Section: Introductionmentioning

confidence: 99%

“…It entails arranging a set of examinations to set of exam periods in such a way that the problem's hard constraints are met while soft constraints are minimized. From one institution to the next, the hard and soft limitations are different [11]- [13]. Exam timetabling is a difficult task that involves scheduling tests, minimizing exam overlap, and spreading exams out as widely as feasible for students [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Intelligent university timetable scheduling system using sudoku grid with magic square

Alkallak

Sha’ban²,

Alnema³

2022

Bulletin EEI

View full text Add to dashboard Cite

Examination timetabling is problem faced by many academic educational institutions. The situation formulated as artificial intelligence problems as Sudoku grid with Magic square. Problem assigning a set of examinations to a fixed number of examination periods so that no student is required to take more than one examination at a time. The research describes two proposed heuristic algorithms to create university examination timetabling and avoiding the overlapping of exams. We proposed two algorithms, the first algorithm is based on Sudoku grid, a number of hard constraints as well as a number of soft constraints are proposed. We assigned exams as 9 to period as 27 slot times and to 3 rooms, with 3 classes. Each student in each class takes 9 exams While, the second algorithm simulates the classic manual methods while observing the hard and soft constraints of the problem. The heuristic approach used to further optimize the result to include hard constraints with soft. The proposed methodology for creating the Exam timetabling problem has been implemented in Matlab and closing the gap between classical methods and intelligent methods. The results obtained with very less computational effort through two algorithms.

show abstract

“…UCTTP is an NP-complete problem (Socha et al, 2003;Mazlan et al, 2019), which is usually solved by heuristics (Babaei et al, 2015;Schaerf, 1999). Lewis (2008), Qu and Burke (2009), Burke et al (2010), Jat and Yang (2011), Al-Betar and Khader (2012), Abdullah et al (2012), Bolaji et al (2014), Bellio et al (2016), Abdelhalim and El Khayat (2016), Pillay and Özcan (2019) and Mazlan et al (2019) are examples of using heuristics for UCTTP. All of them are quite efficient but applied to a single institution.…”

Section: Introductionmentioning

confidence: 99%

Hybrid VNS-Ts Heuristics for University Course Timetabling Problem

Vianna

Martins²,

Lima³

et al. 2020

BJO&PM

View full text Add to dashboard Cite

Goal: Propose hybrid heuristics combining VNS and Tabu Search (TS) for adding adaptability to University Course Timetabling Problem (UCTTP) resolution. Design / Methodology / Approach: VNS and TS metaheuristics were used isolated and in combination in order to verify which shape acquired the best solutions. Those heuristics were verified using constraints found at two undergraduate courses of Federal Fluminense University. Effectiveness is verified by comparing them with solutions manually elaborated by undergraduate course coordinators. Results: The computational results showed the efficiency of the hybrid VNS-TS heuristics, with emphasis on the heuristic VNS-TS1, which uses tabu search as local search method. Practical implications: The hybrid heuristic VNS-TS1 has been in use since the first half of 2019 by some of UFF undergraduate courses coordinations. Even though each university has specific constraints on its timetable, the use of the proposed heuristics is possible because it has been developed using the framework FINESS that allows for the easy insertion and removal of constraints. Originality / Value: We have the following innovative contributions: Development of heuristics with a high degree of adaptation to the needs of educational institutions; Two proposed VNS-TS heuristics; Proposed VND-TS improvement method.

show abstract

University course timetabling model using ant colony optimization algorithm approach

Cited by 19 publications

References 14 publications

Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

Intelligent university timetable scheduling system using sudoku grid with magic square

Hybrid VNS-Ts Heuristics for University Course Timetabling Problem

Contact Info

Product

Resources

About