A Fast Genetic Algorithm for Solving University Scheduling Problem

Abstract

University course timetabling is a NP-hard problem which is very difficult to solve by conventional methods, we know scheduling problem is one of the Nondeterministic Polynomial (NP) problems. This means, solving NP problems through normal algorithm is a time-consuming process (it takes days or months with available equipment) which makes it impossible to be solved through a normal algorithm like this. In purposed algorithm the problem of university class scheduling is solved through a new chromosome structure and modifying the normal genetic methods which really improves the solution in this case. We include lecturer, class and course information in presented algorithm, with all their Constraints, and it creates optimized scheduling table for weekly program of university after creating primary population of chromosomes and running genetic operators. In the final part of this paper we conclude from the results of input data analysis that the results have high efficiency compared with other algorithms considering maximum Constraints.

Analisis Insertion Mutation pada Sistem Penjadwalan Mata Kuliah

“…Penggunaan jenis crossover dan mutasi yang tepat dalam algoritma genetika menujukkan hasil efektif dan lebih maksimal dalam mendesain persoalan waktu mengajar di universitas. Proses pembuatan timetables mencapai hasil yang efisien serta sangat cepat menemukan nilai fitness yang terbaik dalam prosesnya [8].…”

A Genetic Algorithm Based Method for Timetabling Problems Using Linguistics of Hedge Algebra in Constraints

“…In general, scheduling problem in universities (course timetabling problem -CTP) includes finding the appropriate allocation of time within a limited time period for all events (such as courses, semester exams) and assigns them to a number of resources (teachers, students and classrooms) to ensure that the constraints are met [1,2,3,4,6]. However, in credit training, depending on the characteristics of each university, the scheduling problem will be deployed with certain differences.…”

“…Typically, the constraints of the problem needed to be satisfied are divided into two categories [1,4,9]: hard constraints and soft constraints. Hard constraints must certainly be met and fulfilled.…”

“…M. Abbaszadeh et al in [1] using GA to solve scheduling problems in the universities, they have changed the structure of performing gene sequence, allowing genetic mutations to achieve the transferring 15% of better individuals to the next generation. Moreover, to avoid falling into local optimum, they considered the impact their parameters mutation, removed repetitive genes and replaced by better gene sequences.…”

Uma ferramenta para planejamento de estudos para concursos

“…Os algoritmos genéticos são uma abordagem que tem sido bastante utilizada para resolver o problema da grade de horários neste domínio. Abbaszadeh e Saeedvand[19] apresentaram um algoritmo genético que pretende trabalhar com alta eficiência e precisão considerando o máximo de restrições no planejamento da grade de horários. Dessa forma, é determinado o horário de início e término para cada grade e em seguida a mesma é dividida em intervalos de tempo menores.…”