A Heuristics Approach for Classroom Scheduling Using Genetic Algorithm Technique

Ahmad, Izah Rafidah; Sufahani, Suliadi Firdaus; Ali, Maselan; Razali, Siti Noor Asyikin Mohd

doi:10.1088/1742-6596/995/1/012050

Cited by 10 publications

(8 citation statements)

References 8 publications

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“…Scheduling timetables for courses for the big departments in the universities is a very hard problem and is often be solved by many previous works although results are partially optimal because some works were taken in their consideration that the input data are constants as shown in 16 ,17, 21 . Other works depended on fitness value to organize the timetable as indicated in (18) and the proposed partially exam timetable presented in 20 , 22 . Also, 15 dealt with the scheduling problem on a narrow and specific scale.…”

Section: Resultsmentioning

confidence: 99%

“…In 2018, Izah R. Ahmad and others 18 , created a timetable using genetic algorithm (GA) by Java programming languages to solve many restrictions such as the class size with student's ability, irritability time to each class and lecturer who is a responsible for each class, the number of classes in one day, and the subject of participation and lecturer. The genetic algorithm with Java programming languages managed to decrease the conflicts and to improve the fitness function.…”

Section: Related Workmentioning

confidence: 99%

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An Evolutionary Algorithm for Solving Academic Courses Timetable Scheduling Problem

Abdul-Jabbar

Abdullah

2022

Baghdad Sci.J

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Scheduling Timetables for courses in the big departments in the universities is a very hard problem and is often be solved by many previous works although results are partially optimal. This work implements the principle of an evolutionary algorithm by using genetic theories to solve the timetabling problem to get a random and full optimal timetable with the ability to generate a multi-solution timetable for each stage in the collage. The major idea is to generate course timetables automatically while discovering the area of constraints to get an optimal and flexible schedule with no redundancy through the change of a viable course timetable. The main contribution in this work is indicated by increasing the flexibility of generating optimal timetable schedules with different copies by increasing the probability of giving the best schedule for each stage in the campus with the ability to replace the timetable when needed. The Evolutionary Algorithm (EA) utilized in this paper is the Genetic Algorithm (GA) which is a common multi-solution metaheuristic search based on the evolutionary population that can be applied to solve complex combinatorial problems like timetabling problems. In this work, all inputs: courses, teachers, and time acted by one array to achieve local search and combined this acting of the timetable by using the heuristic crossover to ensure that the essential conditions are not broken. The result of this work is a flexible scheduling system, which shows the diversity of all possible timetables that can be created depending on user conditions and needs.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Evolutionary Algorithm for Solving Academic Courses Timetable Scheduling Problem

Abdul-Jabbar

Abdullah

2022

Baghdad Sci.J

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“…The mathematical model formulation is presented in following section. It was adapted from [22]. The notations and parameters used in the model are as follows: The objective is to minimize the overall summation of all penalties from the generated timetable.…”

Section: Lecturer Timetabling Problemmentioning

confidence: 99%

Genetic Algorithm with Elitist-Tournament for Clashes-Free Slots of Lecturer Timetabling Problem

Yusoff

Othman

2018

IJEECS

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<span>Genetic algorithm (GA) approach is one of an evolutionary optimization technique relies on natural selection. The employment of GA still popular and it was applied to many real-world problems, especially in many combinatorial optimization solutions. Lecturer Timetabling Problem (LTP) has been researched for a few decades and produced good solutions. Although, some of LTP offers good results, the criteria and constraints of each LTP however are different from other universities. The LTP appears to be a tiresome job to the scheduler that involves scheduling of students, classes, lecturers and rooms at specific time-slots while satisfying all the necessary requirements to build a feasible timetable. This paper addresses the employment and evaluation of GA to overcome the biggest challenge in LTP to find clashes-free slots for lecturer based on a case study in the Faculty of Computer and Mathematical Sciences, University Technologi MARA, Malaysia. Hence, the performance of the GA is evaluated based on selection, mutation and crossover using different number of population size. A comparison of performance between simple GA with Tournament Selection scheme combined with Elitism (TE) and a GA with Tournament (T) selection scheme. The findings demonstrate that the embedded penalty measures and elitism composition provide good performance that satisfies all the constraints in producing timetables to lecturers. </span>

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“…Laboratory technicians scheduling is one of scheduling an educational institution/university other than class timetabling [7], courses timetabling [8][9] and lectures timetabling [10] [11]. Besides, scheduling staff has also been applied in various areas such as nurseries [12] [13], banks [14], and railways [15].…”

Section: Introductionmentioning

confidence: 99%

“…Besides, scheduling staff has also been applied in various areas such as nurseries [12] [13], banks [14], and railways [15]. Various scheduling techniques can be chosen to make effective and efficient scheduling such as genetic algorithms, linear programming [12], integer programming 0-1 [13], heuristics [7] [11], and mathematical programming [14].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Laboratory Technicians Scheduling using Binary Genetic Algorithm

Wibowo*,

Ivander

2019

IJRTE

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A laboratory needs at least one technician to maintain the laboratory’s activity every day. The technicians should prevent any technical interference in a daily learning activity. The technicians must be placed in a different lab the next day to check the work of the technician previously. This scheduling model has assigned 4 technicians into 3 laboratories in a month. We proposed a mathematical model for multi-objective optimization of laboratory technicians scheduling since it has many objective functions such as avoid collisions, workload balancing of technicians, and works distribution in the laboratories. We presented a Binary Genetic Algorithm to find the best technicians scheduling that can be used to support daily operations. As a result, we noticed that Binary GA could effectively be used in daily operational since the computing time was relatively short in finding the best laboratory technicians scheduling. From ten times of testing, the best solution needs 285.406s to calculate with the minimum function value is 2.

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A Heuristics Approach for Classroom Scheduling Using Genetic Algorithm Technique

Cited by 10 publications

References 8 publications

An Evolutionary Algorithm for Solving Academic Courses Timetable Scheduling Problem

An Evolutionary Algorithm for Solving Academic Courses Timetable Scheduling Problem

Genetic Algorithm with Elitist-Tournament for Clashes-Free Slots of Lecturer Timetabling Problem

Multi-Objective Optimization of Laboratory Technicians Scheduling using Binary Genetic Algorithm

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