Comparison Using Particle Swarm Optimization and Genetic Algorithm for Timetable Scheduling

Adrianto, Dennise

doi:10.3844/jcssp.2014.341.346

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Population-based algorithms can be categorised into evolution-inspired, physical-phenomenon-inspired and bio-inspired approaches. To some extent, bio-inspired approaches have better performance over the other two groups [40,41]. Consequently, this research chose a bio-inspired approach in applying to the proposed model.…”

Section: Related Bio-inspired Optimisation Methodsmentioning

confidence: 99%

School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method

Zhu

2021

Mathematics

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Although educational timetabling problems have been studied for decades, one instance of this, the school timetabling problem (STP), has not developed as quickly as examination timetabling and course timetabling problems due to its diversity and complexity. In addition, most STP research has only focused on the educators’ availabilities when studying the educator aspect, and the educators’ preferences and expertise have not been taken into consideration. To fill in this gap, this paper proposes a conceptual model for the school timetabling problem considering educators’ availabilities, preferences and expertise as a whole. Based on a common real-world school timetabling scenario, the artificial bee colony (ABC) algorithm is adapted to this study, as research shows its applicability in solving examination and course timetabling problems. A virtual search space for dealing with the large search space is introduced to the proposed model. The proposed approach is simulated with a large, randomly generated dataset. The experimental results demonstrate that the proposed approach is able to solve the STP and handle a large dataset in an ordinary computing hardware environment, which significantly reduces computational costs. Compared to the traditional constraint programming method, the proposed approach is more effective and can provide more satisfactory solutions by considering educators’ availabilities, preferences, and expertise levels.

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Section: Related Bio-inspired Optimisation Methodsmentioning

confidence: 99%

School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method

Zhu

2021

Mathematics

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“…This method worked effectively for the university of Tsukuba dataset. In 2014, a comparative study was conducted, which proved that PSO can solve university lecture timetabling problems better than genetic algorithms 30 . PSO gets one near the optima faster than GA, but GA eventually gets one closer.…”

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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“…A considerable number of studies have been carried out in the area of timetable scheduling. Various methods including the methods of operation research, human-machine interaction, and artificial intelligence (Andrianto, 2014). Shrinivasan et'al.…”

Section: Related Workmentioning

confidence: 99%

Paperless Master Timetable Scheduling System

Daniel¹,

Maruf²,

Modi³

2018

IJAST

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Timetable generation is a very difficult task. It is a time consuming, and arduous process. To manually generate a timetable, takes a lot of time, effort, and manpower. However, a timetable scheduling system is designed for different purposes such as: organizing lectures in higher institutions, private organization, airlines, bus station, etc. This paper tries to minimize the difficulties in generating a timetable for academic purposes, using Logarithmic algorithm, to be precise the modified Quicksort algorithm for its design. The algorithm is designed to eradicate collisions on the timetable, and to run in parallel. This helps to minimize the human errors, improve the accuracy of the timetable scheduling process as well as the computation time. To this extent, a Paperless Master Timetable Scheduling System (PMTSS) was proposed and implemented tin this paper to achieve the said requirements. The system can be installed on any android or windows platform. The content of the design also provides exciting services such as: Live Chat for Administrators, Lecturers and Students, Timetable alertsetc. The paperless timetable scheduling system involves dynamic system utilization and modifications.

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Comparison Using Particle Swarm Optimization and Genetic Algorithm for Timetable Scheduling

Cited by 8 publications

References 13 publications

School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method

School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method

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

Paperless Master Timetable Scheduling System

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