Genetic Algorithm with Search Bank Strategies for University Course Timetabling Problem

Mahiba, A. Araisa; Durai, C. Anand Deva

doi:10.1016/j.proeng.2012.06.033

Cited by 29 publications

(11 citation statements)

References 8 publications

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“… Provide the number of double lessons requested by each event. [15,20,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]  Classes taught by the same professor  Classes held in the same room  A class cannot be assigned to a particular room unless the capacity of the room is  Balance or spread out the lectures over the week.  Classes may request contiguous time slots.…”

Section: Resultsmentioning

confidence: 99%

A study on optimization methods for solving course timetabling problem in university

Mazlan¹,

Makhtar²,

Mohamed³

et al. 2018

IJET

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Course timetabling is one of the most important processes faced by any educational institution. However, the course timetabling process is time consuming and tiresome as it needs to be done for each regular semester. This paper aims to study on the Optimization methods to solve the course timetabling problem. The study is obtained and discussed by categorizing between the classification of Hard Constraint and Soft Constraint and the classification of Optimization Methods. From the study, it shows that Meta-Heuristics are the mostly method used in solving the course timetabling problem. It is concluded that this method is suitable for future used compared to other techniques studied. An analysis and observation will be carried out for the research future.

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Section: Resultsmentioning

confidence: 99%

A study on optimization methods for solving course timetabling problem in university

Mazlan¹,

Makhtar²,

Mohamed³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…Pada pemasalahan penjadwalan perkuliahan terdapat dua fungsi objektif yakni fungsi yang dipengaruhi oleh hard constraints dan fungsi yang dipengaruhi oleh soft constraints [1], [4]. Jenis permasalahan yang memiliki lebih dari satu fungsi objektif seperti ini temasuk pada multi-objective problem [5] [6].…”

Section: Introductionunclassified

“…TINJAUAN PUSTAKA GA digunakan untuk penjadwalan seperti pada penelitian yang dilakukan [4] dan [11]. Pada penelitian tersebut dihasilkan jadwal yang optimal.…”

Section: Introductionunclassified

Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan

Rivan¹,

Bhagaskara²

2020

SISFOKOM

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The lecture schedule is a problem that belongs to the NP-Hard problem and multi-objective problem because it has several variables that affect the preparation of the schedule and has limitations that must be met. One solution that has been found is using a Genetic Algorithm (GA). GA has been proven to be able to provide a schedule that can meet limitations in scheduling. Besides, it also found a new concept of thought from GA, namely the Fluid Genetic Algorithm (FGA). The most visible difference between FGA and GA is that there is no mutation process in each iteration. FGA has a new stage, namely individual born and new constants, namely global learning rate, individual learning rate, and diversity rate. This concept of thinking was tested in previous studies and found that FGA is superior to GA for the problem of finding the optimum value of a predetermined function, but this function is not included in the multi-objective problem. In this study, the testing and comparison of FGA and GA were conducted for the problem of scheduling lectures at STMIK XYZ. Based on the results obtained, FGA can produce a schedule without any hard constraint violations. FGA can be used to solve multi-objective problems. FGA has a smaller number of generations than GA. However, overall GA is superior in producing schedules without any problems.

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“…A wide variety of methods such as simulated annealing (Ceschia et al, 2012;Basir et al, 2013;Bellio et al, 2016), tabu search (Aladag et al, 2009;Lü and Hao, 2010;Abdullah and Turabieh, 2012), genetic algorithms (Mirrazavi et al, 2003;Agustin-Blas, 2009;Mahiba and Durai, 2012), constraint logic programming (Deris et al, 1997;Panagiotis et al, 1998) and graph colouring (Cangalovic and Schreuder, 1991;Burke et al, 1994) have been used to formulate and solve timetabling problems. Most of these are heuristic search methods which may not produce globally optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

An Integer Programming Model for a Complex University Timetabling Problem: A Case Study

Prabodanie¹

2017

Industrial Engineering and Management Systems

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A binary integer programming model is proposed for a complex timetabling problem in a university faculty which conducts various degree programs. The decision variables are defined with fewer dimensions to economize the model size of large scale problems and to improve modeling efficiency. Binary matrices are used to incorporate the relationships between the courses and students, and the courses and teachers. The model includes generally applicable constraints such as completeness, uniqueness, and consecutiveness; and case specific constraints. The model was coded and solved using Open Solver which is an open-source optimizer available as an Excel add-in. The results indicate that complicated timetabling problems with large numbers of courses and student groups can be formulated more efficiently with fewer numbers of variables and constraints using the proposed modeling framework. The model could effectively generate timetables with a significantly lower number of work hours per week compared to currently used timetables. The model results indicate that the particular timetabling problem is bounded by the student overlaps, and both human and physical resource constraints are insignificant.

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Genetic Algorithm with Search Bank Strategies for University Course Timetabling Problem

Cited by 29 publications

References 8 publications

A study on optimization methods for solving course timetabling problem in university

A study on optimization methods for solving course timetabling problem in university

Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan

An Integer Programming Model for a Complex University Timetabling Problem: A Case Study

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