Genetically Evolved Solution to Timetable Scheduling Problem

Timilsina, Sandesh; Negi, Rohit; Khurana, Yashika; Seth, Jyotsna

doi:10.5120/20077-2100

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…Segundo Timilsina et al (2015) e Sigl et al (2003) uma primeira aproximação para a modelação do problema de organização de horários é a adaptação do processo de otimização ao de um problema de corte 3D.…”

Section: Metodologiaunclassified

Algoritmos Genéticos Aplicados Na Criação De Agendas De Horário Semanal De Aulas

et al. 2019

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Determining a weekly class schedule is a computationally complex problem whose computational cost can increase exponentially in relation to the number of variables involved in the solution. There areseveral software on the market that build this timetable based on deterministic rules. Alternatively, we intend to build this solution using Genetic Algorithms, making use of its exploratory capacity in multidimensional spaces.

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Section: Metodologiaunclassified

Algoritmos Genéticos Aplicados Na Criação De Agendas De Horário Semanal De Aulas

et al. 2019

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“…There are many algorithms in solving TPP such as local search [2,3], simulated annealing [4,5], cultural algorithm [6], and genetic algorithm(GA) [1,[7][8][9][10][11][12][13][14][15][16]. An improved version of GA was using distributed population in GA that called distributed GA (DGA).…”

Section: Introductionmentioning

confidence: 99%

Comparing Selection Method in Course Scheduling Using Genetic Algorithm

Saptarini¹,

Ciptayani²,

Wisswani³

et al. 2018

Proceedings of the International Conference on Science and Technology (ICST 2018)

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Selection technique is an important operator in genetic algorithm (GA). Defining the best selection technique is critical in order to get the optimum solution for certain problem. The purpose of this study was to compare 3 selection techniques in high school scheduling problem using distributed GA (DGA). The selection techniques implemented in this study were roulette wheel, tournament and truncation selection. The migration probabilities used in DGA was 0.5. The results showed that the best selection method in avoiding early convergence is the tournament method

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“…Constraints are almost universally employed by people dealing with timetable scheduling problem [4]. Although many researchers involved in solving the timetabling problem, it is impossible to perfectly solve it because of the variety of constraints in each problem [5]. There are two categories of constraints which are soft and hard constraints.…”

Section: Introductionmentioning

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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Genetically Evolved Solution to Timetable Scheduling Problem

Cited by 8 publications

References 8 publications

Algoritmos Genéticos Aplicados Na Criação De Agendas De Horário Semanal De Aulas

Algoritmos Genéticos Aplicados Na Criação De Agendas De Horário Semanal De Aulas

Comparing Selection Method in Course Scheduling Using Genetic Algorithm

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

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