Akıllı Fabrikalarda Çizelgeleme Yöntemlerinin Analizi

Year 2023, , 761 - 774, 27.10.2023

Rumeysa Manzak Orhan Engin

https://doi.org/10.51551/verimlilik.1136778

Cited By: 1

Abstract

Amaç: Bu çalışmanın amacı, Akıllı Fabrikalarda gerçekleştirilen çizelgeleme yöntemlerini incelemektir.
Yöntem: Bu çalışmada, veri kaynağı olarak, “Google Scholar” üzerinden ulaşılan metin türünde veriler kullanılmıştır. Veri Seti, 2015-2022 yılları arasında yayınlanmış olan ve ‘Scheduling in Smart Factory’ anahtar kelimesini içeren araştırma makalelerinden oluşmaktadır. Çalışma gerçekleştirilirken, ‘Scheduling in Smart Factory’ anahtar kelimesi ile doğrudan ilgili araştırmalar analiz edilmiştir.
Bulgular: Akıllı Fabrikalarda gerçekleştirilen çizelgeleme yöntemlerinin incelenmesi çalışmasında, çizelgeleme problemlerinin çözümünde, Genetik Algoritma, Çoklu Robot Önleyici Görev Çizelgelemesi, Parçacık Optimizasyonu, Ağlar Arası Birleştirme ve Çizelgeleme gibi birçok yöntemin kullanıldığı belirlenmiştir. Önerilen bu yöntemlerin performanslarını değerlendirmek için duyarlılık analizi, hata kurtarma analizi ve karşılaştırma analizi gibi metotlar tercih edilmiştir. Bu çalışmaları doğrulamak için deney çalışmaları yürütülmüştür.
Özgünlük: Çizelgeleme problemleri, hem geleneksel fabrikalarda, hemde akıllı fabrikalarda stratejik bir öneme sahiptir. Özellikle son yıllarda, çizelgeleme çalışmaları üzerine çok sayıda algoritma geliştirilmiştir. Bu çalışmada, 2015-2022 yılları arasında gerçekleştirilen bilimsel çalışmaları içeren özgün bir tarama sunulmuştur.

Keywords

Akıllı Fabrika, Akıllı Fabrikalarda Çizelgeleme, Endüstri 4.0

Analysis of Scheduling Methods In Smart Factories

Abstract

Purpose: The objective of this study is to examine the scheduling methods implemented in Smart Factories.
Methodology: In this study, text-type data accessed through “Google Scholar” was used as a data source. The Data Set is consisting of articles published between 2015-2022 and contains the keyword 'Scheduling in Smart Factory'. While conducting the literature study, the keyword 'Scheduling in Smart Factory' was taken as a basis.
Findings: In the study of examining the scheduling methods carried out in Smart Factories, it was determined that many methods such as Genetic Algorithm, Multi-Robot Preventive Task Scheduling, Particle Optimization, Inter-Network Coupling and Scheduling were used in the solution of scheduling problems. Methods such as sensitivity analysis, error recovery analysis and comparison analysis were used to evaluate the performance of these proposed methods. Experimental studies were conducted to validate these studies.
Originality: Scheduling issues are of strategic importance in both traditional factories and smart factories. Especially in recent years, many algorithms have been developed for scheduling studies. In this study, an original survey study including scientific studies carried out between the years 2015-2022 is presented.

Keywords

Smart Factory, Scheduling in Smart Factory, Industry 4.0

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