Tuning the fuzzy logic system by two meta-heuristics: case study of airline market share on long-haul routes

Mijović, Nemanja; Kalić, Milica; Kuljanin, Jovana

doi:10.1016/j.trpro.2021.01.053

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…Further, a fuzzy-based ABC algorithm is employed to solve the construction site layout problem by satisfying the multi-objective function [56]. Mijović, Kalić, and Kuljanin [57] applied two meta-heuristics for FIS fine-tuning, where the BCO approach outperformed the PSO algorithm in terms of achieved solutions. A systematic review of the studies that use metaheuristic algorithms based on artificial bees to optimize FIS performance is given in Table II.…”

Section: Implementation Of Bco Based Algorithmmentioning

confidence: 99%

A Decision Support Model for Transportation Companies to Examine Driver Behavior

Čubranić-Dobrodolac

Švadlenka

Marković

et al. 2023

IEEE Trans. Eng. Manage.

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Section: Implementation Of Bco Based Algorithmmentioning

confidence: 99%

A Decision Support Model for Transportation Companies to Examine Driver Behavior

Čubranić-Dobrodolac

Švadlenka

Marković

et al. 2023

IEEE Trans. Eng. Manage.

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“…In addition, the method provided important information for improving air route operations. Mijovic, Kalic & Kuljanin (2021) developed a fuzzy logic model using metaheuristic algorithms to determine the market share of airlines on long-haul routes in the London airport system. The model significantly increased the efficiency of market share value prediction and could improve the operational performance of airlines.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 spread algorithm in the international airport network-DetArpds

Guevara

Coronel

Maldonado

et al. 2023

PeerJ Computer Science

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Due to COVID-19, the spread of diseases through air transport has become an important issue for public health in countries globally. Moreover, mass transportation (such as air travel) was a fundamental reason why infections spread to all countries within weeks. In the last 2 years in this research area, many studies have applied machine learning methods to predict the spread of COVID-19 in different environments with optimal results. These studies have implemented algorithms, methods, techniques, and other statistical models to analyze the information in accuracy form. Accordingly, this study focuses on analyzing the spread of COVID-19 in the international airport network. Initially, we conducted a review of the technical literature on algorithms, techniques, and theorems for generating routes between two points, comprising an analysis of 80 scientific papers that were published in indexed journals between 2017 and 2021. Subsequently, we analyzed the international airport database and information on the spread of COVID-19 from 2020 to 2022 to develop an algorithm for determining airport routes and the prevention of disease spread (DetARPDS). The main objective of this computational algorithm is to generate the routes taken by people infected with COVID-19 who transited the international airport network. The DetARPDS algorithm uses graph theory to map the international airport network using geographic allocations to position each terminal (vertex), while the distance between terminals was calculated with the Euclidian distance. Additionally, the proposed algorithm employs the Dijkstra algorithm to generate route simulations from a starting point to a destination air terminal. The generated routes are then compared with chronological contagion information to determine whether they meet the temporality in the spread of the virus. Finally, the obtained results are presented achieving a high probability of 93.46% accuracy for determining the entire route of how the disease spreads. Above all, the results of the algorithm proposed improved different computational aspects, such as time processing and detection of airports with a high rate of infection concentration, in comparison with other similar studies shown in the literature review.

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“…Mahasiswa [17] C. Aturan Logika Kelulusan Mahasiswa Untuk Menentukan tingkat kelulusan mahasiswa tentunya harus dibuat aturan logika yang akan digunakan sebagai acuan dalam pengolahan data [18], [19]. Aturan logika yang digunakan ini dibuat sesuai dengan himpunan yang digunakan pada variabel jumlah penerimaan peserta wisuda dan jumlah mahasisawa sehingga dapat ditentukan suatu kondisi atau status jumlah yang lulus [20].…”

Section: Gambar 1 Proses Fuzzy Tsukamoto Kelulusanunclassified

Analisis Prediksi Tingkat Kelulusan Mahasiswa Universitas Wiralodra Indramayu Menggunakan Metode Fuzzy Tsukamoto

2020

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Universitas Wiralodra merupakan Universitas swasta pertama di Indramayu, terletak di Kota Indramayu, Jawa Barat. Seiring dengan perjalanannya, Universitas Wiralodra terus berbenah untuk meningkatkan kapasitasnya, melalui peningkatan kualitas Sumber Daya Manusia (SDM) Dosen, Manajemen, kegiatan mahasiswa, penelitian dan publikasi, serta perbaikan sarana dan prasarana kampus. Kelulusan merupakan suatu hal yang dapat diharapkan oleh setiap mahasiswa, yang nantinya akan menjadi bekal seseorang mahasiswa dalam menempuh jenjang selanjutnya. Permasalahannya apakah mahasiswa Universitas Wiralodra bisa lulus dengan maksimal atau tidak. Dalam penelitian lebih memfokuskan bagaimana implementasi teori logika fuzzy dalam memprediksi tingkat kelulusan mahasiswa Universitas Wiralodra. Teori logika fuzzy ini digunakan karena mudah untuk dimengerti. Pada Penelitian ini menggunakan teori fuzzy tsukamoto dengan 2 variabel input yang terdiri dari jumlah penerimaan wisuda dan jumlah mahasiswa yang akan menhasilkan status jumlah mahasiswa yang lulus. Tujuan dari penelitian ini yaitu untuk memprediksi jumlah mahasiswa yang lulus dalam setiap tahunnya di Universitas Wiralodra Indramayu. Pada penelitian selanjutnya agar keputusan dalam menganalisis kondisi tingkat kelulusan mahasiswa agar digabungkan metode fuzzy tsukamoto dan markov chain agar masalah yang terjadi dalam memprediksi tingkat kelulusan mahasiswa dapat lebih akurat.

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Tuning the fuzzy logic system by two meta-heuristics: case study of airline market share on long-haul routes

Cited by 9 publications

References 11 publications

A Decision Support Model for Transportation Companies to Examine Driver Behavior

A Decision Support Model for Transportation Companies to Examine Driver Behavior

COVID-19 spread algorithm in the international airport network-DetArpds

Analisis Prediksi Tingkat Kelulusan Mahasiswa Universitas Wiralodra Indramayu Menggunakan Metode Fuzzy Tsukamoto

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