A Study on PSO-Based University Course Timetabling Problem

Irene, Sheau Fen Ho; Deris, Safaai; Zaiton, Mohd Hashim Siti

doi:10.1109/icacc.2009.112

Cited by 15 publications

(4 citation statements)

References 4 publications

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“…The soultion to the problem is based on genetic algorithm. Sheu defined timetabling as assigning a set of events (lessons either lectures or labs) into number of venues and timeslots as long as the number of constraints are satisfied [5]. Instead of course timetabling, timetabling can be applied in other areas such as transportation timetable, final examination timetable and so forth.…”

Section: Related Workmentioning

confidence: 99%

Particle Swarm Inspired Timetabling for ICT Courses

Foong

Rahim

2012

AMM

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University course timetabling is a complex problem which must satisfy a list of constraints in order to allocate the right timeslots and venues for various courses. The challenge is to make the NP-hard problem user-friendly, highly interactive and faster run time complexity of algorithm. The objective of the paper is to propose Particle Swarm Optimization (PSO) timetabling model for Undergraduate Information and Communication Technology (ICT) courses. The PSO model satisfies hard constraints with minimal violation of soft constraints. Empirical results show that the rds: NP hard problem, timetabling, particle swarm optimization

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Section: Related Workmentioning

confidence: 99%

Particle Swarm Inspired Timetabling for ICT Courses

Foong

Rahim

2012

AMM

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“…Population-based algorithms start with a number of solutions and refine them to obtain global optimal solution(s) in the whole search space, and hence, are global-area-based algorithms. Population-based algorithms that are commonly used to tackle timetabling problems include evolutionary algorithms (EAs) [14], particle swarm optimization [19], ant-colony optimization [39], artificial immune system [31], etc.…”

Section: Related Workmentioning

confidence: 99%

Genetic Algorithms With Guided and Local Search Strategies for University Course Timetabling

Yang

Jat

2011

IEEE Trans. Syst., Man, Cybern. C

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The university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-ofthe-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP. Index Terms-Genetic algorithm (GA), guided search, local search (LS), university course timetabling problem (UCTP). I. INTRODUCTION T IMETABLING is one of the common scheduling problems, which can be described as the allocation of resources for tasks under predefined constraints so that it maximizes the possibility of allocation or minimizes the violation of constraints [40]. Timetabling problems are often made complicated by the details of a particular timetabling task. A general algorithm approach to one problem may turn out to be incapable for another problem, because certain special constraints are required in a particular instance of that problem. In the university course timetabling problem (UCTP), events (subjects and courses) have to be allocated into a number of time slots and rooms while satisfying various constraints. It is very difficult to find a general and effective solution for timetabling due to the diversity of the problem, the variance of constraints, and particular requirements from university to university according to the characteristics. There is no known deterministic polynomial time algorithm for the UCTP, since it is an NP-hard combinatorial optimization problem [18].

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“…These approaches have been proven to give feasible results. However, if they were implemented alone, near-optimal solution cannot be obtained by them [4][5][6] [7]. Some studies have shown that by incorporating constraint based reasoning, a nearoptimal solution can be obtained [3].…”

Section: Introductionmentioning

confidence: 99%

Incorporating dynamic constraint matching into vertex-based graph coloring approach for university course timetabling problem

Hiryanto

2013

2013 International Conference on QiR

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University Course Timetabling Problem (UCTP) belongs to Constraint Satisfaction Problems (CSPs), which are the set of objects whose state must satisfy a number of constraints. The constraints, in this case, are related to characteristics and regulations of a particular university. Certainly, these will vary from one university to the other. A number of approaches have provided feasible and optimal solutions for UCTP. However, their solutions are still based certain university's constraints. Our approach has given another way to occupy various constraints for various universities. Dynamic Constraint Matching (DCM) consists of constraints logical formulation, collision matrix generation, and validation using the collision matrix. Our experiment, using 93 subjects offered in Faculty of Information Technology TarumanagaraUniversity, has shown that all constraints, taken from the characteristics and regulations of the Faculty, can be formulated successfully. When DCM were integrated with Vertex Graph Coloring (VGC) as one of the guaranteed optimal solutions for UCTP, the approach results a course schedule that does not contain any hard or soft constraints violations. The processing time can be said fast, which is less than 1 minutes.

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A Study on PSO-Based University Course Timetabling Problem

Cited by 15 publications

References 4 publications

Particle Swarm Inspired Timetabling for ICT Courses

Particle Swarm Inspired Timetabling for ICT Courses

Genetic Algorithms With Guided and Local Search Strategies for University Course Timetabling

Incorporating dynamic constraint matching into vertex-based graph coloring approach for university course timetabling problem

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