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Akıllı Fabrikalarda Çizelgeleme Yöntemlerinin Analizi

Yıl 2023, Cilt: 57 Sayı: 4, 761 - 774, 27.10.2023

Rumeysa Manzak Orhan Engin

https://doi.org/10.51551/verimlilik.1136778
Cited By: 1

Öz

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.

Anahtar Kelimeler

Akıllı Fabrika Akıllı Fabrikalarda Çizelgeleme Endüstri 4.0

Kaynakça

  • Alemão, D., Rocha, A.D. ve Barata, J. (2021). “Smart Manufacturing Scheduling Approaches-Systematic Review and Future Directions”, Applied Science, 11, 2186.
  • Aydoğmuş, U., Engin, O. (2021). “Endüstri 4.0 Sürecinde Ağırlama Sektörüne Yönelik Uygulamaların İncelemesi”, İstanbul Aydın Üniversitesi Sosyal Bilimler Dergisi, 13(3), 851-874.
  • Büchi, G., Cugno, M., Castagnoli, R. (2020). “Smart Factory Performance and Indusrty 4.0”, Technological Forecasting and Social Change, 150,1-10.
  • Chen B., Wan, J., Shu, L., Li, P., Mukherjee, M. ve Yin, B. (2017). “Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges”, IEE Access, 6, 6505-6519.
  • Hermann, F. (2018). “The Smart Factory and Its Risks”, Systems, 6(38), 1-15.
  • Hozdic, E. (2015). “Smart Factory for Industry 4.0: A Review”, International Journal of Modern Manufacturing Technologies, 7(1), 28-35.
  • Illa, P.K. ve Padhi, N. (2018). “Practical Guide to Smart Factory Transition Using Iot, Big Data and Edge Analytics”, IEEE Access, 6, 55162-55170.
  • Ivanov, D., Sokolov, B.V., Werner, F. ve Dolgui, A. (2015). “A Dynamic Model and an Algorithm for Shortterm Supply Chain Scheduling in the Smart Factory Industry 4.0”, International Journal of Production Research, 54(2), 386-402.
  • Kalempa, V.C., Piardi, L., Limeira, M. ve Oliveira, A.S. (2021). “Multi-Robot Preemptive Task Scheduling with Fault Recovery: A Novel Approach to Automatic Logistics of Smart Factories”, Sensors, 21, 01-26.
  • Kalsoom T., Ramzan, N., Ahmed, S. ve Ur-Rehman, M. (2020). “Advances in Sensor Technologies in the Era of Smart Factory and Industry 4.0”, Sensors, 20, 1-22.
  • Kusiak A. (2018). “Smart Manufacturing”, International Journal of Production Research, 56(1-2), 508-517.
  • Li, M., Zhong, R.Y., Qu, T. ve Huang, G.Q. (2021). “Spatial–Temporal Out‑of‑Order Execution for Advanced Planning and Scheduling in Cyber‑Physical Factories”, Journal of Intelligent Manufacturing, 33, 1355-1372.
  • Lim, C.H., Moon, S.K. ve Okpoti, E.S. (2019). “A Reusable Scheduling Problem Decomposition Framework for Smart Factories”, 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Macao, China, 516-520.
  • Mabkhot, M.M., Al-Ahmari, A.M., Salah, B. ve Alkhalefah, H. (2018). “Requirements of the Smart Factory Syste: A Survey and Perspective”, Machines, 6, 1-22.
  • Malik, S. ve Kim, D. (2021). “Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory”, Actuators, 10(2), 1-17.
  • Malik, S. ve Kim, D., (2020). “A Hybrid Scheduling Mechanism Based on Agent Cooperation Mechanism and Fair Emergency First in Smart Factory”, IEEE Access, 8, 227064-227075.
  • Osterrieder, P., Budde, L. ve Friedli, T. (2020). “The Smart Factory as a Key Construct of Industry 4.0: A Systematic Literature Review”, International Journal of Production Economics, 107476.
  • Parente, M., Figueira, G., Amorim, P. ve Marques, A. (2020). “Production Scheduling in the Context of Industry 4.0: Review ve Trends”, International Journal of Production Research, 58(17), 5401-5431.
  • Pereira, A.C. ve Romero, F. (2017). “A Review of the Meanings and the Implications of the Industry 4.0 Concept”, Procedia Manufacturing, 13, 1206-1214.
  • Resman, M., Turk, M. ve Herakovic, N. (2021). “Methodology for Planning Smart Factory”, 8th CIRP Conference of Assembly Technology and Systems, 29 September - 1 October 2020, Athens, Greece, 97, 401-406.
  • Rub, J. ve Bahemia, H. (2019). “A Review of the Literature on Smart Factory Implementation”, 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 17-19 June 2019, Valbonne Sophia-Antipolis, France, 1-9.
  • Shi, Z., Xie, Y., Xue, W., Chen, Y., Fu, L. ve Xu, X. (2020). “Smart Factory in Industry 4.0”, System Research and Behavioral Science, 37, 607-617.
  • Shiue, Y.R., Lee, K.C. ve Su, C.T. (2018). “Real-time Scheduling for a Smart Factory Using a Reinforcement Learning Approach”. Computers & Industrial Engineering, 125, 604-614.
  • Sinha, D. ve Roy, R. (2020). “Reviewing Cyber-Physical System as a Part of Smart Factory in Industry 4.0”, IEEE Engineering Management Review, 48(2), 103-117.
  • Viagas, V.F ve Framinan, J.M. (2021). “Exploring the Benefits of Scheduling with Advanced and Real-time Information Integration in Industry 4.0: A Computational Study”, Journal of Information Integration, 27, 1-11.
  • Wan, J., Chen, B., Wang, S., Xia, M., Li, D. ve Liu, C. (2018b). “Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory”, IEEE Transactions on Industrial Informatics, 14, 4548-4556.
  • Wan, J., Yang, J., Wang, Z., Hua, Q., (2018a). “Artificial Intelligence for Cloud-Assisted Smart Factory”, IEEE Access, 6, 55419-55430.
  • Wan, J., Yang, J., Wnag, S., Li, D., Li, P. ve Xia, M. (2020). “Cross-Network Fusion and Scheduling for Heterogeneous Networks in Smart Factory”, IEEE Transactions on Industrial Informatics, 16(9), 6059-6068.
  • Wang, S., Wan, J., Li, D. ve Zhang, C. (2015). “Implementing Smart Factory of Industrie 4.0: An Outlook”, International Journal of Distributed Sensor Networks, 2016, 1-10.
  • Xiao, R., Zhang, Y., Cui, X.H., Zhang, F. ve Wang, H.H. (2021). “A Hybrid Task Crash Recovery Solution for Edge Computing in IoT-Based Manufacturing”, IEEE Access, 9, 106220-106231.
  • Xu, L.D., Xu, E.L. ve Li, L. (2018). “Industry 4.0: State of the Art and Future Trends”, International Journal of Production Research, 56, 2941-2962.
  • Zhao, S., Dziurzanski, P., Przewozniczek, M., Komarnicki, M. ve Indrusiak, L.S. (2019). “Cloud-based Dynamic Distributed Optimisation of Integrated Process Planning and Scheduling in Smart Factories, GECCO’19: Genetic and Evolutionary Computation Conference”, 13-17 July 2019, Prague Czech Republic, 1381-1389.
  • Zheng, T., Ardolino, M., Bacchetti, A. ve Perona, M. (2020). “The Applications of Industry 4.0 Technologies in Manufacturing Context: A Systematic Literature Review”, International Journal of Production Research, 59, 1922-1954.
  • Zhou, M.T., Ren, T.F., Dai, Z.M. ve Feng, X.Y. (2022). “Task Scheduling and Resource Balancing of Fog Computing in Smart Factory”, Mobile Network and Applications, 1-12.
  • Zhou, T., Tang, D., Zhu, H. ve Wang, L. (2021b). “Reinforcement Learning with Composite Rewards for Production Scheduling in a Smart Factory”, IEEE Access, 9, 752-766.
  • Zhou, T., Tang, D., Zhu, H. ve Zhang, Z. (2021a). “Multi-Agent Reinforcement Learning for Online Scheduling in Smart Factories”, Robotics and Computer-Integrated Manufacturing, 72, 1-14.

Analysis of Scheduling Methods In Smart Factories

Yıl 2023, Cilt: 57 Sayı: 4, 761 - 774, 27.10.2023

Rumeysa Manzak Orhan Engin

https://doi.org/10.51551/verimlilik.1136778
Cited By: 1

Öz

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.

Anahtar Kelimeler

Smart Factory Scheduling in Smart Factory Industry 4.0

Kaynakça

  • Alemão, D., Rocha, A.D. ve Barata, J. (2021). “Smart Manufacturing Scheduling Approaches-Systematic Review and Future Directions”, Applied Science, 11, 2186.
  • Aydoğmuş, U., Engin, O. (2021). “Endüstri 4.0 Sürecinde Ağırlama Sektörüne Yönelik Uygulamaların İncelemesi”, İstanbul Aydın Üniversitesi Sosyal Bilimler Dergisi, 13(3), 851-874.
  • Büchi, G., Cugno, M., Castagnoli, R. (2020). “Smart Factory Performance and Indusrty 4.0”, Technological Forecasting and Social Change, 150,1-10.
  • Chen B., Wan, J., Shu, L., Li, P., Mukherjee, M. ve Yin, B. (2017). “Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges”, IEE Access, 6, 6505-6519.
  • Hermann, F. (2018). “The Smart Factory and Its Risks”, Systems, 6(38), 1-15.
  • Hozdic, E. (2015). “Smart Factory for Industry 4.0: A Review”, International Journal of Modern Manufacturing Technologies, 7(1), 28-35.
  • Illa, P.K. ve Padhi, N. (2018). “Practical Guide to Smart Factory Transition Using Iot, Big Data and Edge Analytics”, IEEE Access, 6, 55162-55170.
  • Ivanov, D., Sokolov, B.V., Werner, F. ve Dolgui, A. (2015). “A Dynamic Model and an Algorithm for Shortterm Supply Chain Scheduling in the Smart Factory Industry 4.0”, International Journal of Production Research, 54(2), 386-402.
  • Kalempa, V.C., Piardi, L., Limeira, M. ve Oliveira, A.S. (2021). “Multi-Robot Preemptive Task Scheduling with Fault Recovery: A Novel Approach to Automatic Logistics of Smart Factories”, Sensors, 21, 01-26.
  • Kalsoom T., Ramzan, N., Ahmed, S. ve Ur-Rehman, M. (2020). “Advances in Sensor Technologies in the Era of Smart Factory and Industry 4.0”, Sensors, 20, 1-22.
  • Kusiak A. (2018). “Smart Manufacturing”, International Journal of Production Research, 56(1-2), 508-517.
  • Li, M., Zhong, R.Y., Qu, T. ve Huang, G.Q. (2021). “Spatial–Temporal Out‑of‑Order Execution for Advanced Planning and Scheduling in Cyber‑Physical Factories”, Journal of Intelligent Manufacturing, 33, 1355-1372.
  • Lim, C.H., Moon, S.K. ve Okpoti, E.S. (2019). “A Reusable Scheduling Problem Decomposition Framework for Smart Factories”, 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Macao, China, 516-520.
  • Mabkhot, M.M., Al-Ahmari, A.M., Salah, B. ve Alkhalefah, H. (2018). “Requirements of the Smart Factory Syste: A Survey and Perspective”, Machines, 6, 1-22.
  • Malik, S. ve Kim, D. (2021). “Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory”, Actuators, 10(2), 1-17.
  • Malik, S. ve Kim, D., (2020). “A Hybrid Scheduling Mechanism Based on Agent Cooperation Mechanism and Fair Emergency First in Smart Factory”, IEEE Access, 8, 227064-227075.
  • Osterrieder, P., Budde, L. ve Friedli, T. (2020). “The Smart Factory as a Key Construct of Industry 4.0: A Systematic Literature Review”, International Journal of Production Economics, 107476.
  • Parente, M., Figueira, G., Amorim, P. ve Marques, A. (2020). “Production Scheduling in the Context of Industry 4.0: Review ve Trends”, International Journal of Production Research, 58(17), 5401-5431.
  • Pereira, A.C. ve Romero, F. (2017). “A Review of the Meanings and the Implications of the Industry 4.0 Concept”, Procedia Manufacturing, 13, 1206-1214.
  • Resman, M., Turk, M. ve Herakovic, N. (2021). “Methodology for Planning Smart Factory”, 8th CIRP Conference of Assembly Technology and Systems, 29 September - 1 October 2020, Athens, Greece, 97, 401-406.
  • Rub, J. ve Bahemia, H. (2019). “A Review of the Literature on Smart Factory Implementation”, 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 17-19 June 2019, Valbonne Sophia-Antipolis, France, 1-9.
  • Shi, Z., Xie, Y., Xue, W., Chen, Y., Fu, L. ve Xu, X. (2020). “Smart Factory in Industry 4.0”, System Research and Behavioral Science, 37, 607-617.
  • Shiue, Y.R., Lee, K.C. ve Su, C.T. (2018). “Real-time Scheduling for a Smart Factory Using a Reinforcement Learning Approach”. Computers & Industrial Engineering, 125, 604-614.
  • Sinha, D. ve Roy, R. (2020). “Reviewing Cyber-Physical System as a Part of Smart Factory in Industry 4.0”, IEEE Engineering Management Review, 48(2), 103-117.
  • Viagas, V.F ve Framinan, J.M. (2021). “Exploring the Benefits of Scheduling with Advanced and Real-time Information Integration in Industry 4.0: A Computational Study”, Journal of Information Integration, 27, 1-11.
  • Wan, J., Chen, B., Wang, S., Xia, M., Li, D. ve Liu, C. (2018b). “Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory”, IEEE Transactions on Industrial Informatics, 14, 4548-4556.
  • Wan, J., Yang, J., Wang, Z., Hua, Q., (2018a). “Artificial Intelligence for Cloud-Assisted Smart Factory”, IEEE Access, 6, 55419-55430.
  • Wan, J., Yang, J., Wnag, S., Li, D., Li, P. ve Xia, M. (2020). “Cross-Network Fusion and Scheduling for Heterogeneous Networks in Smart Factory”, IEEE Transactions on Industrial Informatics, 16(9), 6059-6068.
  • Wang, S., Wan, J., Li, D. ve Zhang, C. (2015). “Implementing Smart Factory of Industrie 4.0: An Outlook”, International Journal of Distributed Sensor Networks, 2016, 1-10.
  • Xiao, R., Zhang, Y., Cui, X.H., Zhang, F. ve Wang, H.H. (2021). “A Hybrid Task Crash Recovery Solution for Edge Computing in IoT-Based Manufacturing”, IEEE Access, 9, 106220-106231.
  • Xu, L.D., Xu, E.L. ve Li, L. (2018). “Industry 4.0: State of the Art and Future Trends”, International Journal of Production Research, 56, 2941-2962.
  • Zhao, S., Dziurzanski, P., Przewozniczek, M., Komarnicki, M. ve Indrusiak, L.S. (2019). “Cloud-based Dynamic Distributed Optimisation of Integrated Process Planning and Scheduling in Smart Factories, GECCO’19: Genetic and Evolutionary Computation Conference”, 13-17 July 2019, Prague Czech Republic, 1381-1389.
  • Zheng, T., Ardolino, M., Bacchetti, A. ve Perona, M. (2020). “The Applications of Industry 4.0 Technologies in Manufacturing Context: A Systematic Literature Review”, International Journal of Production Research, 59, 1922-1954.
  • Zhou, M.T., Ren, T.F., Dai, Z.M. ve Feng, X.Y. (2022). “Task Scheduling and Resource Balancing of Fog Computing in Smart Factory”, Mobile Network and Applications, 1-12.
  • Zhou, T., Tang, D., Zhu, H. ve Wang, L. (2021b). “Reinforcement Learning with Composite Rewards for Production Scheduling in a Smart Factory”, IEEE Access, 9, 752-766.
  • Zhou, T., Tang, D., Zhu, H. ve Zhang, Z. (2021a). “Multi-Agent Reinforcement Learning for Online Scheduling in Smart Factories”, Robotics and Computer-Integrated Manufacturing, 72, 1-14.
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Üretim ve Endüstri Mühendisliği (Diğer)
Bölüm Derleme
Yazarlar

Rumeysa Manzak Bu kişi benim 0000-0002-5319-1758

Orhan Engin 0000-0002-7250-0317

Yayımlanma Tarihi 27 Ekim 2023
Gönderilme Tarihi 28 Haziran 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 57 Sayı: 4

Kaynak Göster

APA Manzak, R., & Engin, O. (2023). Akıllı Fabrikalarda Çizelgeleme Yöntemlerinin Analizi. Verimlilik Dergisi, 57(4), 761-774. https://doi.org/10.51551/verimlilik.1136778

Cited By

Çizelgeleme Problemlerinin Çözümünde Pekiştirmeli Öğrenme Etkisinin Analizi
ALKÜ Fen Bilimleri Dergisi
https://doi.org/10.46740/alku.1390397
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