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Selecting the Augmented Reality Glasses Used in the Automobile Manufacturing Industry by Multi-Criteria Decision-Making Methods

Yıl 2025, Cilt: 12 Sayı: 2, 425 - 446, 01.07.2025

Nuh Keleş , Ayhan Demirci

https://doi.org/10.17541/optimum.1572768

Öz

Augmented Reality Glasses (ARG) technology has entered people’s lives in recent years by playing virtual games for entertainment purposes and has also begun to find use in the film industry, storage systems, military field, and engineering, depending on the desire for innovation. This study aims to select ARG via Multi-Criteria Decision-Making (MCDM) methods to accelerate, simplify, and activate operational processes in the automobile manufacturing industry. This study determined eight different ARG alternatives, and nine criteria (battery power, field of view, price, camera, brightness, display resolution, internal memory, RAM, and weight). The CRITIC method is used in criteria evaluation, and ARAS, EDAS, and CODAS methods are used in alternative rankings. Vuzix M4000 brand/model ARG, which has more optimum values than other alternatives, comes first. While finding criterion weights, it can be said that the CRITIC method finds reasonable and close criterion weights. In future studies, ARGs with different models and features can be included in the analysis and compared with the findings obtained from this study.

Anahtar Kelimeler

Augmented Reality Automobile Manufacturing Industry MCDM Methods

Kaynakça

  • Abdelhafeez, A., & Myvizhi, M. (2023). Neutrosophic MCDM Model for Assessment Factors of Wearable Technological Devices to Reduce Risks and Increase Safety: Case Study in Education. International Journal of Advances in Applied Computational Intelligence, 3(1), 41-52. https://doi.org/10.54216/IJAACI.030104
  • Aksüt, G., Eren, T., & Alakaş, H. M. (2024). Using wearable technological devices to improve workplace health and safety: An assessment on a sector base with multi-criteria decision-making methods. Ain Shams Engineering Journal, 15(2), 102423. https://doi.org/10.1016/j.asej.2023.102423
  • Atici-Ulusu, H., Ikiz, Y. D., Taskapilioglu, O., & Gunduz, T. (2021). Effects of augmented reality glasses on the cognitive load of assembly operators in the automotive industry. International Journal of Computer Integrated Manufacturing, 34(5), 487-499. https://doi.org/10.1080/0951192X.2021.1901314
  • Ayçin, E. (2019). Çok Kriterli Karar Verme – Bilgisayar Uygulamalı Çözümler, Nobel Akademik Yayıncılık, Ankara.
  • Aydin, S. (2018). Augmented reality goggles selection by using neutrosophic MULTIMOORA method. Journal of Enterprise Information Management, 31(4), 565-576. https://doi.org/10.1108/JEIM-01-2018-0023
  • Badi, I., Ballem, M., & Shetwan, A. (2018). Site Selection of Desalination Plant in Libya by Using Combinative Distance-Based Assessment (CODAS) Method. International Journal for Quality Research, 12(3), 609-624. https://doi.org/10.18421/IJQR12.03-04
  • Bakir, M., & Alptekin, N. (2018). Hizmet Kalitesi Ölçümüne Yeni Bir Yaklaşım: CODAS Yöntemi İle Havayolu İşletmeleri Üzerine Bir Uygulama. Business & Management Studies: An International Journal, 6(4), 1336-1353. https://doi.org/10.15295/bmij.v6i4.409
  • Balco, P., Bajzík, P., & Škovierová, K. (2022). Virtual and augmented reality in manufacturing companies in Slovakia. Procedia Computer Science, 201, 313-320. https://doi.org/10.1016/j.procs.2022.03.042
  • Basoglu, N. A., Goken, M., Dabic, M., Ozdemir Gungor, D., & Daim, T. U. (2018). Exploring adoption of augmented reality smart glasses: Applications in the medical industry. Frontiers of Engineering Management, 2018, 5(2): 167-181. https://doi.org/10.15302/J-FEM-2018056
  • Blanco-Novoa, O., Fernandez-Carames, T. M., Fraga-Lamas, P., & Vilar-Montesinos, M. A. (2018). A practical evaluation of commercial industrial augmented reality systems in an industry 4.0 shipyard. Ieee Access, 6, 8201-8218. https://doi.org/10.1109/ACCESS.2018.2802699
  • Boboc, R. G., Gîrbacia, F., & Butilă, E. V. (2020). The application of augmented reality in the automotive industry: A systematic literature review. Applied Sciences, 10(12), 4259. https://doi.org/10.3390/app10124259
  • Danielsson, O., Holm, M., & Syberfeldt, A. (2020). Augmented reality smart glasses in industrial assembly: Current status and future challenges. Journal of Industrial Information Integration, 20, 100175. https://doi.org/10.1016/j.jii.2020.100175
  • Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770. https://doi.org/10.1016/0305-0548(94)00059-H
  • Dinçer, E. (2019). Çok Kriterli Karar Alma, Gece Akademi, Ankara.
  • Dini, G., & Dalle Mura, M. (2015). Application of augmented reality techniques in through-life engineering services. Procedia Cirp, The Fourth International Conference on Through-life Engineering Services, 38, 14-23. https://doi.org/10.1016/j.procir.2015.07.044
  • Ghorabaee, M. K., Zavadskas, E. K., Turskis, Z., & Antuchevičienė, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies and Research, 50(3), 25-44.
  • Ghorabaee, M. K., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435-451. https://doi.org/10.15388/Informatica.2015.57
  • Gutiérrez, L. E., Samper, J. J., Jabba, D., Nieto, W., Guerrero, C. A., Betts, M. M., & López-Ospina, H. A. (2023). Combined Framework of Multicriteria Methods to Identify Quality Attributes in Augmented Reality Applications. Mathematics, 11(13), 2834. https://doi.org/10.3390/math11132834
  • Ikiz, Y. D., Atici-Ulusu, H., Taskapilioglu, O., & Gunduz, T. (2019). Usage of augmented reality glasses in automotive industry: Age-related effects on cognitive load. International Journal of Recent Technology and Engineering, 8(3), 1-6. http://www.doi.org/10.35940/ijrte.C3853.098319
  • 360avm (2024, July). Artırılmış Gerçeklik Gözlüğü, https://www.360avm.com/, accessed: 13.07.2024.
  • Kamble, S. S., Belhadi, A., Gunasekaran, A., Ganapathy, L., & Verma, S. (2021). A large multi-group decision-making technique for prioritizing the big data-driven circular economy practices in the automobile component manufacturing industry. Technological Forecasting and Social Change, 165, 120567. https://doi.org/10.1016/j.techfore.2020.120567
  • Karabasevic, D., Zavadskas, E. K., Stanujkic, D., Popovic, G., & Brzakovic, M. (2018). An Approach to Personnel Selection in the IT Industry Based on the EDAS Method. Transformations in Business & Economics, 17(2), 54-65.
  • Keleş, N. (2024). OECD ülkelerinde kullanılan bilgi ve iletişim teknolojilerinin çok kriterli karar verme yöntemleriyle karşılaştırılması. Gazi İktisat ve İşletme Dergisi, 10(2), 215-229. https://doi.org/10.30855/gjeb.2024.10.2.002
  • Koutromanos, G., & Kazakou, G. (2023). Augmented reality smart glasses use and acceptance: Α literature review. Computers & Education: X Reality, 2, 100028. https://doi.org/10.1016/j.cexr.2023.100028
  • Linkov, I. and Moberg, E. (2012). Multi-Criteria Decision Alanysis: Environmental Applications and Case Studies, CRC Press, USA.
  • Madic, M., & Radovanovic, M. (2015). Ranking of some most commonly used nontraditional machining processes using ROV and CRITIC methods. UPB Sci. Bull., Series D, 77(2), 193-204.
  • Mariyam, A., Basha, S. A. H., & Raju, S. V. (2022). Industry 4.0: augmented reality in smart manufacturing industry environment to facilitate faster and easier work procedures. Cloud Analytics for Industry 4.0, 6, 141. https://doi.org/10.1515/9783110771572-009
  • Mishra, B., & Singh, F. B. (2024). Estimating regulatory governance gaps for adoption of augmented reality in automobile sector: the application of analytical hierarchy approach. International Journal of Information and Decision Sciences, 16(2), 169-190. https://doi.org/10.1504/IJIDS.2024.139831
  • Morales Méndez, G., & del Cerro Velázquez, F. (2024). Augmented Reality in Industry 4.0 Assistance and Training Areas: A Systematic Literature Review and Bibliometric Analysis. Electronics, 13(6), 1147. https://doi.org/10.3390/electronics13061147
  • Nguyen, P. H. (2024). A data-driven MCDM approach-based spherical fuzzy sets for evaluating global augmented reality providers in education. IEEE Access, 13, 6102-6119. https://doi.org/10.1109/ACCESS.2024.3361320
  • Omerali, M., & Kaya, T. (2022). Augmented reality application selection framework using spherical fuzzy COPRAS multi criteria decision making. Cogent Engineering, 9(1), 2020610. https://doi.org/10.1080/23311916.2021.2020610
  • Özbek, A. (2017). Çok Kriterli Karar Verme Yöntemleri ve Excel ile Problem Çözümü, Seçkin Akademik ve Mesleki Yayınlar, Ankara.
  • Prathibha, S., Palanikumar, K., Ponshanmugakumar, A., & Kumar, M. R. (2024). Application of augmented reality and virtual reality technologies for maintenance and repair of automobile and mechanical equipment. In Machine Intelligence in Mechanical Engineering (pp. 63-89). Elsevier.
  • Renzi, C., Leali, F., & Di Angelo, L. (2017). A review on decision-making methods in engineering design for the automotive industry. Journal of Engineering Design, 28(2), 118-143. https://doi.org/10.1080/09544828.2016.1274720
  • Stanujkic, D., Zavadskas, E. K., Ghorabaee, M. K., & Turskis, Z. (2017). An extension of the EDAS method based on the use of interval grey numbers. Studies in Informatics and Control, 26(1), 5-12. https://doi.org/10.24846/v26i1y201701
  • Touami, O., Djekoune, O., Benbelkacem, S., Mellah, R., Guerroudji, M. A., & Zenati-Henda, N. (2022, October). An Application of a Fuzzy TOPSIS Multi-Criteria Decision Analysis Algorithm for Augmented Reality Maintenance Aid Systems Selection. In 2022 2nd International Conference on Advanced Electrical Engineering (ICAEE) (pp. 1-6). IEEE. https://doi.org/10.1109/ICAEE53772.2022.9962127
  • Tuş, A., & Aytaç Adalı, E. (2019). The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem. Opsearch, 56, 528-538. https://doi.org/10.1007/s12597-019-00371-6
  • Wang, D., & Zhao, J. (2016). Design optimization of mechanical properties of ceramic tool material during turning of ultra-high-strength steel 300M with AHP and CRITIC method. The International Journal of Advanced Manufacturing Technology, 84, 2381-2390. https://doi.org/10.1007/s00170-015-7903-7
  • Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Technological and economic development of economy, 16(2), 159-172. https://doi.org/10.3846/tede.2010.1

Çok Kriterli Karar Verme Yöntemleri ile Otomotiv Üretim Sektöründe Kullanılan Artırılmış Gerçeklik Gözlüklerinin Seçimi

Yıl 2025, Cilt: 12 Sayı: 2, 425 - 446, 01.07.2025

Nuh Keleş , Ayhan Demirci

https://doi.org/10.17541/optimum.1572768

Öz

Artırılmış Gerçeklik Gözlükleri (ARG) teknolojisi son yıllarda eğlence amaçlı sanal oyunlar oynamasıyla insanların hayatına girmiş olup yenilik isteğine bağlı olarak film endüstrisi, depolama sistemleri, askeri alan ve mühendislikte de kullanım alanı bulmaya başlamıştır. Bu çalışmada, otomobil üretim endüstrisinde operasyonel süreçleri hızlandırmak, basitleştirmek ve etkinleştirmek için Çok Kriterli Karar Verme (ÇKKV) yöntemleriyle ARG seçimi yapılması amaçlanmıştır. Bu çalışmada sekiz farklı ARG alternatifi ve dokuz kriter (pil gücü, görüş alanı, fiyat, kamera, parlaklık, ekran çözünürlüğü, dahili bellek, RAM ve ağırlık) belirlenmiştir. Kriter değerlendirmesinde CRITIC yöntemi, alternatif sıralamalarında ise ARAS, EDAS ve CODAS yöntemleri kullanılmıştır. Diğer alternatiflere göre daha fazla optimum değere sahip olan Vuzix M4000 marka/model ARG ilk sırada gelmektedir. Kriter ağırlıkları bulunurken CRITIC yönteminin makul ve yakın kriter ağırlıkları bulduğu söylenebilir. Gelecekteki çalışmalarda, farklı model ve özelliklere sahip ARG'ler analize dahil edilebilir ve bu çalışmadan elde edilen bulgularla karşılaştırılabilir.

Anahtar Kelimeler

Artırılmış Gerçeklik Otomobil Üretim Endüstrisi MCDM Yöntemleri

Kaynakça

  • Abdelhafeez, A., & Myvizhi, M. (2023). Neutrosophic MCDM Model for Assessment Factors of Wearable Technological Devices to Reduce Risks and Increase Safety: Case Study in Education. International Journal of Advances in Applied Computational Intelligence, 3(1), 41-52. https://doi.org/10.54216/IJAACI.030104
  • Aksüt, G., Eren, T., & Alakaş, H. M. (2024). Using wearable technological devices to improve workplace health and safety: An assessment on a sector base with multi-criteria decision-making methods. Ain Shams Engineering Journal, 15(2), 102423. https://doi.org/10.1016/j.asej.2023.102423
  • Atici-Ulusu, H., Ikiz, Y. D., Taskapilioglu, O., & Gunduz, T. (2021). Effects of augmented reality glasses on the cognitive load of assembly operators in the automotive industry. International Journal of Computer Integrated Manufacturing, 34(5), 487-499. https://doi.org/10.1080/0951192X.2021.1901314
  • Ayçin, E. (2019). Çok Kriterli Karar Verme – Bilgisayar Uygulamalı Çözümler, Nobel Akademik Yayıncılık, Ankara.
  • Aydin, S. (2018). Augmented reality goggles selection by using neutrosophic MULTIMOORA method. Journal of Enterprise Information Management, 31(4), 565-576. https://doi.org/10.1108/JEIM-01-2018-0023
  • Badi, I., Ballem, M., & Shetwan, A. (2018). Site Selection of Desalination Plant in Libya by Using Combinative Distance-Based Assessment (CODAS) Method. International Journal for Quality Research, 12(3), 609-624. https://doi.org/10.18421/IJQR12.03-04
  • Bakir, M., & Alptekin, N. (2018). Hizmet Kalitesi Ölçümüne Yeni Bir Yaklaşım: CODAS Yöntemi İle Havayolu İşletmeleri Üzerine Bir Uygulama. Business & Management Studies: An International Journal, 6(4), 1336-1353. https://doi.org/10.15295/bmij.v6i4.409
  • Balco, P., Bajzík, P., & Škovierová, K. (2022). Virtual and augmented reality in manufacturing companies in Slovakia. Procedia Computer Science, 201, 313-320. https://doi.org/10.1016/j.procs.2022.03.042
  • Basoglu, N. A., Goken, M., Dabic, M., Ozdemir Gungor, D., & Daim, T. U. (2018). Exploring adoption of augmented reality smart glasses: Applications in the medical industry. Frontiers of Engineering Management, 2018, 5(2): 167-181. https://doi.org/10.15302/J-FEM-2018056
  • Blanco-Novoa, O., Fernandez-Carames, T. M., Fraga-Lamas, P., & Vilar-Montesinos, M. A. (2018). A practical evaluation of commercial industrial augmented reality systems in an industry 4.0 shipyard. Ieee Access, 6, 8201-8218. https://doi.org/10.1109/ACCESS.2018.2802699
  • Boboc, R. G., Gîrbacia, F., & Butilă, E. V. (2020). The application of augmented reality in the automotive industry: A systematic literature review. Applied Sciences, 10(12), 4259. https://doi.org/10.3390/app10124259
  • Danielsson, O., Holm, M., & Syberfeldt, A. (2020). Augmented reality smart glasses in industrial assembly: Current status and future challenges. Journal of Industrial Information Integration, 20, 100175. https://doi.org/10.1016/j.jii.2020.100175
  • Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770. https://doi.org/10.1016/0305-0548(94)00059-H
  • Dinçer, E. (2019). Çok Kriterli Karar Alma, Gece Akademi, Ankara.
  • Dini, G., & Dalle Mura, M. (2015). Application of augmented reality techniques in through-life engineering services. Procedia Cirp, The Fourth International Conference on Through-life Engineering Services, 38, 14-23. https://doi.org/10.1016/j.procir.2015.07.044
  • Ghorabaee, M. K., Zavadskas, E. K., Turskis, Z., & Antuchevičienė, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies and Research, 50(3), 25-44.
  • Ghorabaee, M. K., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435-451. https://doi.org/10.15388/Informatica.2015.57
  • Gutiérrez, L. E., Samper, J. J., Jabba, D., Nieto, W., Guerrero, C. A., Betts, M. M., & López-Ospina, H. A. (2023). Combined Framework of Multicriteria Methods to Identify Quality Attributes in Augmented Reality Applications. Mathematics, 11(13), 2834. https://doi.org/10.3390/math11132834
  • Ikiz, Y. D., Atici-Ulusu, H., Taskapilioglu, O., & Gunduz, T. (2019). Usage of augmented reality glasses in automotive industry: Age-related effects on cognitive load. International Journal of Recent Technology and Engineering, 8(3), 1-6. http://www.doi.org/10.35940/ijrte.C3853.098319
  • 360avm (2024, July). Artırılmış Gerçeklik Gözlüğü, https://www.360avm.com/, accessed: 13.07.2024.
  • Kamble, S. S., Belhadi, A., Gunasekaran, A., Ganapathy, L., & Verma, S. (2021). A large multi-group decision-making technique for prioritizing the big data-driven circular economy practices in the automobile component manufacturing industry. Technological Forecasting and Social Change, 165, 120567. https://doi.org/10.1016/j.techfore.2020.120567
  • Karabasevic, D., Zavadskas, E. K., Stanujkic, D., Popovic, G., & Brzakovic, M. (2018). An Approach to Personnel Selection in the IT Industry Based on the EDAS Method. Transformations in Business & Economics, 17(2), 54-65.
  • Keleş, N. (2024). OECD ülkelerinde kullanılan bilgi ve iletişim teknolojilerinin çok kriterli karar verme yöntemleriyle karşılaştırılması. Gazi İktisat ve İşletme Dergisi, 10(2), 215-229. https://doi.org/10.30855/gjeb.2024.10.2.002
  • Koutromanos, G., & Kazakou, G. (2023). Augmented reality smart glasses use and acceptance: Α literature review. Computers & Education: X Reality, 2, 100028. https://doi.org/10.1016/j.cexr.2023.100028
  • Linkov, I. and Moberg, E. (2012). Multi-Criteria Decision Alanysis: Environmental Applications and Case Studies, CRC Press, USA.
  • Madic, M., & Radovanovic, M. (2015). Ranking of some most commonly used nontraditional machining processes using ROV and CRITIC methods. UPB Sci. Bull., Series D, 77(2), 193-204.
  • Mariyam, A., Basha, S. A. H., & Raju, S. V. (2022). Industry 4.0: augmented reality in smart manufacturing industry environment to facilitate faster and easier work procedures. Cloud Analytics for Industry 4.0, 6, 141. https://doi.org/10.1515/9783110771572-009
  • Mishra, B., & Singh, F. B. (2024). Estimating regulatory governance gaps for adoption of augmented reality in automobile sector: the application of analytical hierarchy approach. International Journal of Information and Decision Sciences, 16(2), 169-190. https://doi.org/10.1504/IJIDS.2024.139831
  • Morales Méndez, G., & del Cerro Velázquez, F. (2024). Augmented Reality in Industry 4.0 Assistance and Training Areas: A Systematic Literature Review and Bibliometric Analysis. Electronics, 13(6), 1147. https://doi.org/10.3390/electronics13061147
  • Nguyen, P. H. (2024). A data-driven MCDM approach-based spherical fuzzy sets for evaluating global augmented reality providers in education. IEEE Access, 13, 6102-6119. https://doi.org/10.1109/ACCESS.2024.3361320
  • Omerali, M., & Kaya, T. (2022). Augmented reality application selection framework using spherical fuzzy COPRAS multi criteria decision making. Cogent Engineering, 9(1), 2020610. https://doi.org/10.1080/23311916.2021.2020610
  • Özbek, A. (2017). Çok Kriterli Karar Verme Yöntemleri ve Excel ile Problem Çözümü, Seçkin Akademik ve Mesleki Yayınlar, Ankara.
  • Prathibha, S., Palanikumar, K., Ponshanmugakumar, A., & Kumar, M. R. (2024). Application of augmented reality and virtual reality technologies for maintenance and repair of automobile and mechanical equipment. In Machine Intelligence in Mechanical Engineering (pp. 63-89). Elsevier.
  • Renzi, C., Leali, F., & Di Angelo, L. (2017). A review on decision-making methods in engineering design for the automotive industry. Journal of Engineering Design, 28(2), 118-143. https://doi.org/10.1080/09544828.2016.1274720
  • Stanujkic, D., Zavadskas, E. K., Ghorabaee, M. K., & Turskis, Z. (2017). An extension of the EDAS method based on the use of interval grey numbers. Studies in Informatics and Control, 26(1), 5-12. https://doi.org/10.24846/v26i1y201701
  • Touami, O., Djekoune, O., Benbelkacem, S., Mellah, R., Guerroudji, M. A., & Zenati-Henda, N. (2022, October). An Application of a Fuzzy TOPSIS Multi-Criteria Decision Analysis Algorithm for Augmented Reality Maintenance Aid Systems Selection. In 2022 2nd International Conference on Advanced Electrical Engineering (ICAEE) (pp. 1-6). IEEE. https://doi.org/10.1109/ICAEE53772.2022.9962127
  • Tuş, A., & Aytaç Adalı, E. (2019). The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem. Opsearch, 56, 528-538. https://doi.org/10.1007/s12597-019-00371-6
  • Wang, D., & Zhao, J. (2016). Design optimization of mechanical properties of ceramic tool material during turning of ultra-high-strength steel 300M with AHP and CRITIC method. The International Journal of Advanced Manufacturing Technology, 84, 2381-2390. https://doi.org/10.1007/s00170-015-7903-7
  • Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Technological and economic development of economy, 16(2), 159-172. https://doi.org/10.3846/tede.2010.1
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Makaleler
Yazarlar

Nuh Keleş 0000-0001-6768-728X

Ayhan Demirci 0000-0003-3788-4586

Yayımlanma Tarihi 1 Temmuz 2025
Gönderilme Tarihi 23 Ekim 2024
Kabul Tarihi 29 Mayıs 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 12 Sayı: 2

Kaynak Göster

APA Keleş, N., & Demirci, A. (2025). Selecting the Augmented Reality Glasses Used in the Automobile Manufacturing Industry by Multi-Criteria Decision-Making Methods. Optimum Ekonomi Ve Yönetim Bilimleri Dergisi, 12(2), 425-446. https://doi.org/10.17541/optimum.1572768
AMA Keleş N, Demirci A. Selecting the Augmented Reality Glasses Used in the Automobile Manufacturing Industry by Multi-Criteria Decision-Making Methods. OEYBD. Temmuz 2025;12(2):425-446. doi:10.17541/optimum.1572768
Chicago Keleş, Nuh, ve Ayhan Demirci. “Selecting the Augmented Reality Glasses Used in the Automobile Manufacturing Industry by Multi-Criteria Decision-Making Methods”. Optimum Ekonomi Ve Yönetim Bilimleri Dergisi 12, sy. 2 (Temmuz 2025): 425-46. https://doi.org/10.17541/optimum.1572768.
EndNote Keleş N, Demirci A (01 Temmuz 2025) Selecting the Augmented Reality Glasses Used in the Automobile Manufacturing Industry by Multi-Criteria Decision-Making Methods. Optimum Ekonomi ve Yönetim Bilimleri Dergisi 12 2 425–446.
IEEE N. Keleş ve A. Demirci, “Selecting the Augmented Reality Glasses Used in the Automobile Manufacturing Industry by Multi-Criteria Decision-Making Methods”, OEYBD, c. 12, sy. 2, ss. 425–446, 2025, doi: 10.17541/optimum.1572768.
ISNAD Keleş, Nuh - Demirci, Ayhan. “Selecting the Augmented Reality Glasses Used in the Automobile Manufacturing Industry by Multi-Criteria Decision-Making Methods”. Optimum Ekonomi ve Yönetim Bilimleri Dergisi 12/2 (Temmuz 2025), 425-446. https://doi.org/10.17541/optimum.1572768.
JAMA Keleş N, Demirci A. Selecting the Augmented Reality Glasses Used in the Automobile Manufacturing Industry by Multi-Criteria Decision-Making Methods. OEYBD. 2025;12:425–446.
MLA Keleş, Nuh ve Ayhan Demirci. “Selecting the Augmented Reality Glasses Used in the Automobile Manufacturing Industry by Multi-Criteria Decision-Making Methods”. Optimum Ekonomi Ve Yönetim Bilimleri Dergisi, c. 12, sy. 2, 2025, ss. 425-46, doi:10.17541/optimum.1572768.
Vancouver Keleş N, Demirci A. Selecting the Augmented Reality Glasses Used in the Automobile Manufacturing Industry by Multi-Criteria Decision-Making Methods. OEYBD. 2025;12(2):425-46.
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