Modified genetic algorithm for high school time-table scheduling with fuzzy time window

Febrita, Ruth Ema; Mahmudy, Wayan Firdaus

doi:10.1109/siet.2017.8304115

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Kegunaan lain dari algoritma yang dipaparkan dalam paper ini adalah bahwa berbagai algoritma tersebut dapat digunakan untuk mendapatkan nilai objektif bila persoalan penjadwalan perkuliahan ini diselesaikan dengan menggunakan pendekatan meta-heuristik [9] [10] atau berbasis riset operasi [11] [12]. Metode meta-heuristik banyak digunakan untuk berbagai masalah penjadwalan karena kemampuanya menanganai constraint yang kompleks [13] [14]. Misalnya untuk mengetahui berapa banyak pelanggaran constraint ruang yang terjadi pada sebuah solusi yang diperoleh dengan algoritma genetika atau simulated annealing.…”

Section: Pendahuluanunclassified

Algoritma Penanganan Constraint pada Persoalan Penjadwalan Perkuliahan Universitas di Lingkungan Pendidikan Tinggi Keagamaan Islam (PTKI)

Fatchurrochman,

Afandi,

Arifin

et al. 2023

JEPIN

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Penjadwalan perkuliahan di universitas adalah kegitan rutin yang membutuhkan waktu relatif lama untuk menyelesaikannya jika dikerjakan secara manual. Waktu yang dibutuhkan akan semakin besar ketika semakin banyak constraint yang dipertimbangkan. Mekanisme penanganannya akan berbeda di tiap universitas karena mungkin mereka mempunyai constraint yang unik. Dalam paper ini dipaparkan berbagai algoritma penanganan untuk tiap jenis constraint dalam persoalan penjadwalan perkuliahan. Algoritma penjadwalan perkuliahan otomatis yang digunakan dalam penjadwalan otomatispenelitian ini adalah sequential search yang bekerja dengan cara mencari slot waktu yang masih belum dipergunakan untuk ditempati oleh kelas perkuliahan . Bila slot waktu telah dipergunakan maka sistem akan mencari slot waktu yang lain secara berurutan. Uji coba dilakukan di program studi Teknik Informatika UIN Malang pada semester Ganjil tahun akademik 2021/2022. Hasilnya menunjukkan bahwa dengan 10 constraint, sistem yang dibangun dapat menjadwalkan 190 kelas perkuliahan secara otomatis dan 21 kelas perkuliahan dijadwalkan secara interaktif. Dengan sistem yang diajukan maka seluruh kelas perkuliahan sebanyak 211 dapat terjadwal meskipun ada pelanggaran soft constraint oleh 17 kelas perkuliahan.

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Section: Pendahuluanunclassified

Algoritma Penanganan Constraint pada Persoalan Penjadwalan Perkuliahan Universitas di Lingkungan Pendidikan Tinggi Keagamaan Islam (PTKI)

Fatchurrochman,

Afandi,

Arifin

et al. 2023

JEPIN

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“…Implementasi algoritma dilakukan dengan bahasa pemrograman Java dan IntelliJ IDEA 2017. 2) Hitung fitness dengan menggunakan Persamaan 1 yang merupakan tahap dilakukannya perhitungan nilai objektif dari setiap individu yang menggunakan fungsi sebagai berikut [13] ( , ) =…”

Section: Metode Penelitianunclassified

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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“…The schedule sometimes makes the vehicle arrive early or even late from the specified time windows. This deviation in delivery time can affect the level of customer satisfaction which will affect the distributor's income (Febrita & Mahmudy, 2018). Therefore, the application of Fuzzy Logic to time windows can mask time deviations, because fuzzy is vague or uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Routing Problem with Fuzzy Time Windows using Genetic Algorithm and Tabu Search

Syafrizal

Sugiharti²

2023

J. Adv. Inf. Syst. Tech

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The distribution of goods becomes a very calculated thing in the economic aspect, especially in the case of wide and complex distribution. The greater the range of distribution of goods, the more precise, fast, and accurate calculations are needed. Specifically, the calculation of the distribution required starts from mileage, total travel time, customer satisfaction level based on customer time windows, and operational costs. Vehicle Routing Problem (VRP) is a solution to the problem of distributing goods from the depot to its customers. This study aims to determine the optimal route. The methods used for VRP optimization are the Genetic Algorithm (GA) and Tabu Search (TS) methods. Fuzzy logic is used to provide leeway on the limitations of the time windows parameters, thus providing a time tolerance in the event of early arrival of the vehicle or delay in delivery. Data processing using the GA-TS combination was carried out as many as two types of trials, namely trials with the same dataset ten times and trials with various types of datasets ten times. The results of the first trial fitness value on E-VRPFTW average increased by 14.39% compared to the results of the E-VRPTW fitness value that did not use fuzzy. The results of the second trial also experienced an average increase of 8.49% compared to the results of the E-VRPTW fitness value that did not use fuzzy. Therefore, the addition of fuzzy logic has an effect in determining the optimum route of E-VRPTW.

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Modified genetic algorithm for high school time-table scheduling with fuzzy time window

Cited by 7 publications

References 10 publications

Algoritma Penanganan Constraint pada Persoalan Penjadwalan Perkuliahan Universitas di Lingkungan Pendidikan Tinggi Keagamaan Islam (PTKI)

Algoritma Penanganan Constraint pada Persoalan Penjadwalan Perkuliahan Universitas di Lingkungan Pendidikan Tinggi Keagamaan Islam (PTKI)

Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan

Electric Vehicle Routing Problem with Fuzzy Time Windows using Genetic Algorithm and Tabu Search

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