Abstract
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.
Highlights
Penjadwalan perkuliahan merupakan masalah yang termasuk Non Polynomial (NP)-hard problem yakni masalah yang mempunyai kompleksitas waktu penyelesaian dengan tingkatan polinomial [1] [2] [3]
a problem that belongs to the NP-Hard problem
multi-objective problem because it has several variables that affect the preparation of the schedule
Summary
Penjadwalan perkuliahan merupakan masalah yang termasuk Non Polynomial (NP)-hard problem yakni masalah yang mempunyai kompleksitas waktu penyelesaian dengan tingkatan polinomial [1] [2] [3]. Hal ini terjadi dikarenakan dalam masalah penjadwalan perkuliahan terdapat beberapa variabel yang menjadi pengaruh yakni ruangan, kelas, waktu, mata kuliah, dan dosen. Jenis permasalahan yang memiliki lebih dari satu fungsi objektif seperti ini temasuk pada multi-objective problem [5] [6]. Salah satu algoritma yang digunakan dalam menyelesaikan masalah ini adalah genetic algorithm [7]. Fluid Genetic Algorithm (FGA) merupakan algoritma yang memiliki konsep dan proses yang mirip dengan GA. Pada penelitian sebelumnya FGA telah diterapkan untuk masalah sederhana yang bukan merupakan multi-objective problem yakni mencari titik optimal dari suatu fungsi. Berdasarkan uraian tersebut, GA telah berhasil diterapkan untuk masalah multi-objective problem seperti penjadwalan perkuliahan sedangkan FGA belum diterapkan sehingga belum diketahui hasil performanya.
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