TEKNOLOJİK GELİŞMELER IŞIĞINDA ENDÜSTRİ MÜHENDİSLİĞİNİN GELECEĞİ

Year 2023, Volume: 31 Issue: 4, 1094 - 1111, 22.12.2023

Ezgi Aktar Demirtaş Müjgan Sağır Özdemir Şerafettin Alpay N. Fırat Özkan Servet Hasgül Aydın Sipahioğlu

https://doi.org/10.31796/ogummf.1401960

Abstract

Endüstri Mühendisliği insan, makine ve malzemeden oluşan bütünleşik sistemlerin tasarımı, kurulması ve geliştirilmesi ile ilgilenir. Kaynakların verimli kullanımının gittikçe daha önemli olduğu küresel dünyada, tesislerin yer seçimi ve yerleşiminden, hammadde ve diğer girdilerin tedariğine, üretim süreçlerinin planlanması ve çizelgelenmesinden stok ve lojistik süreçlerinin yönetimine, standart süre ve kapasitelerin belirlenmesinden ürün, süreç ve hizmet kalitesinin iyileştirilmesine kadar pek çok aşamada eniyi kararların verilmesinde rol oynamaktadır. Çok çeşitli çalışma alanlarına sahip olan Endüstri Mühendisliği, son yıllarda hızla gelişen Yapay Zekâ teknikleri ve diğer teknolojik gelişmelerden oldukça etkilenmektedir. Bu makalede, son yıllarda Endüstri Mühendisliği alanındaki gelişme ve yenilikler, içerdiği bilim dalları temelinde literatüre dayandırılarak açıklanmaktadır. Çalışmanın bulguları Cumhuriyetimizin 100. yılında yeni mezun olan Endüstri Mühendisleri ve mühendis adayları için de bir farkındalık oluşturacaktır.

Keywords

Üretim ve Servis Sistemleri, Yöneylem Araştırması, Kalite Yönetimi, Ergonomi, Bilgi Sistemleri ve Yapay Zekâ, Endüstri Mühendisliği’nin tarihçesi

THE FUTURE OF INDUSTRIAL ENGINEERING WITH KNOWLEDGE OF TECHNOLOGICAL ADVANCEMENTS

Abstract

Industrial Engineering is concerned with the design, installation, and improvement of integrated systems comprising human, machine, and materials. In a globalized world where efficient resource utilization is increasingly crucial, Industrial Engineering plays a pivotal role in decision-making at various stages, from site selection and layout of facilities to procurement of raw materials and other inputs, planning and scheduling of production processes, management of inventory and logistics processes, determination of standard times and capacities, and enhancement of product, process, and service quality. With diverse areas of application, Industrial Engineering has rapidly evolved in recent years, significantly influenced by emerging artificial intelligence techniques and other technological advancements. This article explores the developments and innovations in the field of Industrial Engineering in the context of various scientific disciplines, relying on literature-based evidence. The findings of this study aim to create awareness for newly graduated Industrial Engineers and engineering candidates, particularly in the centennial year of Republic of Turkey.

Keywords

Production and Service Systems, Operations Research, Quality Management, Ergonomics, Information Systems and Artificial Intelligence, History of Industrial Engineering

References

• Abualsauod, E. H. (2023). Machine learning based fault detection approach to enhance quality control in smart manufacturing. Production Planning & Control, 1-9. doi: https://doi.org/10.1080/09537287.2023.2175736
• Ackoff, R.,1972, A Note on Systems Science, Interfaces, 2,4. doi: https://doi.org/10.1287/inte.2.4.40
• Adhikari, A., Joshi, R., & Basu, S. (2023). Collaboration and coordination strategies for a multi-level AI-enabled healthcare supply chain under disaster. International Journal of Production Research, 1-27. doi: https://doi.org/10.1080/00207543.2023.2252933
• Ahmed, S., Alshater, M. M., El Ammari, A., & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646. doi: https://doi.org/10.1016/j.ribaf.2022.101646
• Al-Refaie, A., Abbasi, G., & Ghanim, D. (2021). Proposed α-cut CUSUM and EWMA control charts for fuzzy response observations. International Journal of Reliability, Quality and Safety Engineering, 28(02), 2150012. doi: https://doi.org/10.1142/S0218539321500121
• Alwan, W., Ngadiman, N. H. A., Hassan, A., Saufi, S. R., & Mahmood, S. (2023). Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns. Machines, 11(1), 115. doi: https://doi.org/10.3390/machines11010115
• Aouag, H., Soltani, M., & Mouss, M. D. (2021). Enhancement of value stream mapping application process through using fuzzy DEMATEL and fuzzy QFD approaches: a case study considering economic and environmental perspectives. Journal of Modelling in Management, 16(3), 1002-1023. doi: http://dx.doi.org/10.1108/JM2-01-2020-0007
• Apaydin-Özkan, H. (2022). Appliance-Level Anomaly Detection by Using Control Charts and Artificial Neural Networks with Power Profiles. Sensors, 22(17), 6639. doi: https://doi.org/10.3390/s22176639
• Aslam, M., AL-Marshadi, A. H., & Khan, N. (2019). A new X-bar control chart for using neutrosophic exponentially weighted moving average. Mathematics, 7(10), 957. doi: https://doi.org/10.3390/math7100957
• Azmat, S., Sabir, Q. U. A., Tariq, S., Shafqat, A., Rao, G. S., & Aslam, M. (2023). Monitoring Air Quality using the Neural Network based Control Chart. MAPAN, 1-9. doi: http://dx.doi.org/10.1007/s12647-023-00663-9
• Bahroun, Z., Tanash, M., As’ad, R., & Alnajar, M. (2023). Artificial Intelligence Applications in Project Scheduling: A Systematic Review, Bibliometric Analysis, and Prospects for Future Research. Management Systems in Production Engineering, 31(2), 144-161. doi: https://doi.org/10.2478/mspe-2023-0017
• Bai, R., Chen, X., Chen, Z. L., Cui, T., Gong, S., He, W., ... & Zhang, H. (2023). Analytics and machine learning in vehicle routing research. International Journal of Production Research, 61(1), 4-30. doi: https://doi.org/10.48550/arXiv.2102.10012
• Balasubramanian, S., Shukla, V., Islam, N., Upadhyay, A., & Duong, L. (2023). Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic. International Journal of Production Research, 1-34. doi: https://doi.org/10.1080/00207543.2023.2263102
• Bayraktar, C., & Gökçen, H. (2020). Yüksek raflı depolama sistemlerinin enerji optimizasyonunda anomali tespiti için sınıflama algoritmalarının karşılaştırılması, Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, 4(2), 89-109. doi: https://doi.org/10.33461/uybisbbd.790369
• Behnia, F., Ahmadabadi, H. Z., Schuelke-Leech, B. A., & Mirhassani, M. (2023). Developing a Fuzzy Optimized Model for Selecting Maintenance Strategy in Paper Industry: An Integrated FGP-ANP-FMEA approach. Expert Systems with Applications, 120899. doi: https://doi.org/10.1016/j.eswa.2023.120899
• Beseiso, M., & Kumar, G. (2021). A fuzzy computational approach for selecting interdependent projects using prioritized criteria. Journal of Intelligent & Fuzzy Systems, 40(6), 11341-11354. doi: https://doi.org/10.3233/JIFS-202506
• Bhambri, P., & Rani, S. (2024). Challenges, Opportunities, and the Future of Industrial Engineering with IoT and AI. Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet of Things, 1-18.
• Blanc, J., & Deb, K. (2020), Pymoo: Multi-Objective Optimization in Python, 2020, IEEE Access, 8, 89497-89509. doi: http://dx.doi.org/10.1109/ACCESS.2020.2990567
• Boff Medeiros, N., Fogliatto, F. S., Karla Rocha, M., & Tortorella, G. L. (2023). Predicting the length-of-stay of pediatric patients using machine learning algorithms. International Journal of Production Research, 1-14. doi: http://dx.doi.org/10.1080/00207543.2023.2235029
• Bolatan, G. İ. S. (2019). Kalite 4.0. Iğdır Üniversitesi Sosyal Bilimler Dergisi, 21, 437-454. Erişim Adresi: https://dergipark.org.tr/tr/download/article-file/2154486
• Buchmeister, B., Palcic, I., & Ojstersek, R. (2019). Artificial intelligence in manufacturing companies and broader: an overvıew. Chapter 07 in DAAAM International Scientific Book, 081-098. doi: http://dx.doi.org/10.2507/daaam.scibook.2019.07
• Carl May, M., Nestroy, C., Overbeck, L., & Lanza, G. (2023). Automated model generation framework for material flow simulations of production systems. International Journal of Production Research, 1-16. doi: https://doi.org/10.1080/00207543.2023.2284833
• Castañé, G., Dolgui, A., Kousi, N., Meyers, B., Thevenin, S., Vyhmeister, E., & Östberg, P. O. (2023). The ASSISTANT project: AI for high level decisions in manufacturing. International Journal of Production Research, 61(7), 2288-2306. doi: https://doi.org/10.1080/00207543.2022.2069525
• Cavallo, F., Sinigaglia, S., Megali, G., Pietrabissa, A., Dario, P., Mosca, F., & Cuschieri, A. (2014). Biomechanics–machine learning system for surgical gesture analysis and development of technologies for minimal access surgery. Surgical Innovation, 21(5), 504-512. doi: https://doi.org/10.1177/1553350613510612
• Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58. doi: http://dx.doi.org/10.1145/1541880.1541882
• Chaouch, F., Ben Khalifa, A., Zitoune, R., & Zidi, M. (2023). Modeling and multi-objective optimization of abrasive water jet machining process of composite laminates using a hybrid approach based on neural networks and metaheuristic algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. doi: https://doi.org/10.1177/09544054231191816
• Charalambous O., & Hindi, K. S. (1991). A Review a of Artificial Intelligence Based Job Shop Scheduling Systems, Information and Decisions Technologies, 17,3,189-202.
• Chiarini, A. (2020). Industry 4.0, quality management and TQM world. A systematic literature review and a proposed agenda for further research. The TQM Journal. 32(4), 603-616. doi: http://dx.doi.org/10.1108/TQM-04-2020-0082
• Chukhrova, N., & Johannssen, A. (2018). Inspection tables for single acceptance sampling with crisp and fuzzy formulation of quality limits. International Journal of Quality & Reliability Management, 35(9), 1755-1791. doi: http://dx.doi.org/10.1108/IJQRM-03-2017-0034
• Ciccone, F., Bacciaglia, A., & Ceruti, A. (2023). Optimization with Artificial Intelligence in Additive Manufacturing, a systematic review. Journal of Brazilian Society of Mechanical Sciences of Engineering, 45, 6. doi: https://doi.org/10.1007/s40430-023-04200-2
• Čisar, P., & Maravić-Čisar, S. (2019). EWMA statistics and fuzzy logic in function of network anomaly detection. Facta universitatis-series: Electronics and Energetics, 32(2), 249-265. doi: http://dx.doi.org/10.2298/FUEE1902249C
• Clark, C. (1957). The Conditions of Economic Progress, 3.Edition, London, Macmillan.
• CoReceptionist (2023), Industry 4.0-What is it? History and Current Applications & Future. Retrieved from https://coreceptionist.co/industry-4-0-what-is-it-history-current-applications-future
• Davenport, T. H., & Short, J. E. (2003). The new industrial engineering: Information technology and business process redesign. Operations management: critical perspectives on business and management, 97-123.
• Decker, L., Leite, D., Giommi, L., & Bonacorsi, D. (2020, July). Real-time anomaly detection in data centers for log-based predictive maintenance using an evolving fuzzy-rule-based approach. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-8. IEEE. doi: http://dx.doi.org/10.1109/FUZZ48607.2020.9177762
• Dehghan Shoorkand, H., Nourelfath, M., & Hajji, A. (2023). A deep learning approach for integrated production planning and predictive maintenance. International Journal of Production Research, 1-20. doi: http://dx.doi.org/10.1080/00207543.2022.2162618
• Desoutter (2023), Industrial Revolution - From Industry 1.0 to Industry 4.0. Erişim adresi: https://www.desouttertools.com/your-industry/news/503/industrial-revolution-from-industry-1-0-to-industry-4-0
• Doumpos, M., Zopounidis, C., Gounopoulos, D., Platanakis, E., & Zhang, W. (2023). Operational research and artificial intelligence methods in banking. European Journal of Operational Research, 306(1), 1-16. doi: https://doi.org/10.1016/j.ejor.2022.04.027
• Du, P., He, X., Cao, H., Garg, S., Kaddoum, G., & Hassan, M. M. (2023). AI-based energy-efficient path planning of multiple logistics UAVs in intelligent transportation systems. Computer Communications, 207, 46-55. doi: http://dx.doi.org/10.1016/j.comcom.2023.04.032
• Du‐Harpur, X., Watt, F. M., Luscombe, N. M., & Lynch, M. D. (2020). What is AI? Applications of artificial intelligence to dermatology. British Journal of Dermatology, 183(3), 423-430. doi: https://doi.org/10.1111/bjd.18880
• Esteso, A., Peidro, D., Mula, J., & Díaz-Madroñero, M. (2023). Reinforcement learning applied to production planning and control. International Journal of Production Research, 61(16), 5772-5789. doi: http://dx.doi.org/10.1080/00207543.2022.2104180
• Ever, D., Demircioğlu, E. N. (2022). Yapay Zekâ Teknolojilerinin Kalite Maliyetleri Üzerine Etkisi. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 31(1), 59-72. doi: https://doi.org/10.35379/cusosbil.1023004
• Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 119-142. doi: http://dx.doi.org/10.1080/13675567.2020.1803246
• García-Alcaraz, J. L., Díaz Reza, J. R., Villalon Turrubiates, I. E., Lopez Herrera, R., Soto Cabral, A., Ganzalez Lazalde, I., & Rodriguez Alvarez, J. L. (2022). A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE. Instituto de Ingeniería y Tecnología. doi: https://doi.org/10.3390/math10163000
• Geramian, A., Abraham, A., & Ahmadi Nozari, M. (2019). Fuzzy logic-based FMEA robust design: a quantitative approach for robustness against groupthink in group/team decision-making. International Journal of Production Research, 57(5), 1331-1344. doi: https://doi.org/10.1080/00207543.2018.1471236
• Geramian, A., Shahin, A., Minaei, B., & Antony, J. (2020). Enhanced FMEA: An integrative approach of fuzzy logic-based FMEA and collective process capability analysis. Journal of the Operational Research Society, 71(5), 800-812. doi: http://dx.doi.org/10.1080/01605682.2019.1606986
• Ghislieri, C., Molino, M., & Cortese, C. G. (2018). Work and organizational psychology looks at the fourth industrial revolution: how to support workers and organizations? Frontiers in psychology, 9, 2365. doi: https://doi.org/10.3389/fpsyg.2018.02365
• Gholizadeh, H., Javadian, N., & Fazlollahtabar, H. (2020). An integrated fuzzy-genetic failure mode and effect analysis for aircraft wing reliability. Soft Computing, 24, 13401-13412. doi: https://link.springer.com/article/10.1007/s00500-020-04757-3
• Giudici, P., & Raffinetti, E. (2023). SAFE artificial intelligence in finance. Finance Research Letters, 104088. doi: https://doi.org/10.1016/j.frl.2023.104088
• Gojković, R., Đurić, G., Tadić, D., Nestić, S., & Aleksić, A. (2021). Evaluation and selection of the quality methods for manufacturing process reliability improvement—Intuitionistic fuzzy sets and genetic algorithm approach. Mathematics, 9(13), 1531. doi: https://doi.org/10.3390/math9131531
• Gomez, C., Guardia, A., Mantari, J. L., Coronado, A. M., & Reddy, J. N. (2022). A contemporary approach to the MSE paradigm powered by Artificial Intelligence from a review focused on Polymer Matrix Composites. Mechanics of Advanced Materials and Structures, 29(21), 3076-3096. doi: https://doi.org/10.1080/15376494.2021.1886379
• Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2022). Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 1-60. doi: https://link.springer.com/article/10.1007/s10479-020-03856-6
• Gümüşoğlu, Ş. (2018). Bilimsel yaklaşımlarla değişim, dönüşüm ve kalite 4.0. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 33(2), 543-568. doi: https://doi.org/10.24988/deuiibf.2018332773
• Gürsoy, M. Ü., Çolak, U.C., Gökçe, M. H., Akkulak, C., & Ötleş, S. (2019). Endüstri için kestirimci bakım. International Journal of 3D Printing Technologies and Digital Industry, 3(1), 56-66. Erişim adresi: https://dergipark.org.tr/tr/download/article-file/706015
• Hassan, A., Purnomo, M. R. A., & Anugerah, A. R. (2020). Fuzzy-analytical-hierarchy process in failure mode and effect analysis (FMEA) to identify process failure in the warehouse of a cement industry. Journal of Engineering, Design and Technology, 18(2), 378-388. doi: http://dx.doi.org/10.1108/JEDT-05-2019-0131
• Hassouna, M., El-henawy, I., & Haggag, R. (2022). A Multi-Objective Optimization for supply chain management using Artificial Intelligence (AI), International Journal of Advanced Computer Science and Applications, 13,8, 140-149. doi: https://dx.doi.org/10.14569/IJACSA.2022.0130817
• Hatami, M., & Franz, B. (2021), Using Deep Learning Artificial Intelligence Foresight Method in the Optimization of Planning and Scheduling of Construction Processes, Computing in Civil Engineering, 1171-1178. doi: http://dx.doi.org/10.1061/9780784483893.143
• Hsieh, Y. C., You, P. S., & Chen, C. S. (2021). Scheduling the periodic delivery of liquefied petroleum gas tank with time window by using artificial intelligence approaches: An example in Taiwan. Science Progress, 104(3_suppl), 00368504211040355. doi: https://doi.org/10.1177/00368504211040355
• Ivančan, J., & Lisjak, D. (2021). New FMEA risks ranking approach utilizing four fuzzy logic systems. Machines, 9(11), 292. doi: https://doi.org/10.3390/machines9110292
• Jafarzadeh, H., Akbari, P., & Abedin, B. (2018). A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency–combination of fuzzy QFD and DEA. Expert Systems with Applications, 110, 237-249. doi: https://doi.org/10.1016/j.eswa.2018.05.028
• Jiang, J. (2023). A survey of machine learning in additive manufacturing technologies. International Journal of Computer Integrated Manufacturing, 1-23. doi: https://doi.org/10.1080/0951192X.2023.2177740
• Kang, X., & Wang, N. (2022). A hybrid model to develop aesthetic product design of customer satisfaction. Concurrent Engineering, 1063293X221138650. doi: https://doi.org/10.1177/1063293X221138650
• Kara, İ. 1985, Yöneylem Araştırmasının Yöntembilimi, Anadolu Üniversitesi Yayınları 96, Anadolu Üniversitesi Basımevi, 117 s.
• Kaya, İ., ve Engin, O. (2005). Kalite İyileştirme Sürecinde Yapay Zekâ Tekniklerinin Kullanımı. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 11(1), 103-114. Erişim adresi: https://dergipark.org.tr/tr/download/article-file/191103
• Kaya, İ., İlbahar, E., & Karaşan, A. (2023). A design methodology based on two dimensional fuzzy linguistic variables for attribute control charts with real case applications. Engineering Applications of Artificial Intelligence, 126, 106792. doi: http://dx.doi.org/10.1016/j.engappai.2023.106792
• Kesici, B. ve Yıldız, M. S. (2016). Kalite kontrol faaliyetlerinde Yapay Zekâ kullanımı ve bir otomotiv yan sanayisinde uygulanması. Yalova Sosyal Bilimler Dergisi, 6(12), 307-323. Erişim adresi: https://dergipark.org.tr/tr/download/article-file/272287
• Kousi, N., Dimosthenopoulos, D., Matthaiakis, A. S., Michalos, G., & Makris, S. (2019). AI based combined scheduling and motion planning in flexible robotic assembly lines. Procedia CIRP, 86, 74-79. doi: https://doi.org/10.1016/j.procir.2020.01.041
• Kula, U., Torkul, O. ve Taşkın, H. (2006). Endüstri ve sistem mühendisliğine giriş. Sakarya: Değişim Yayınları.
• Kurt, R. (2022). Control of system parameters by estimating screw withdrawal strength values of particleboards using artificial neural network-based statistical control charts. Journal of Wood Science, 68(1), 64. doi: https://doi.org/10.1186/s10086-022-02065-y
• Lee, S. M., Lee, D., & Kim, Y.S. (2019). The quality management ecosystem for predictive maintenance in the Industry 4.0 era. International Journal of Quality Innovation, 5(1), 1-11. doi: https://doi.org/10.1186/s40887-019-0029-5
• Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., & Yang, C. W. (2017). Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86-96. doi: http://dx.doi.org/10.1631/FITEE.1601885
• Liu, X., He, X., Wang, M., & Shen, H. (2022). What influences patients' continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics. Technology in Society, 70, 101996. doi: https://doi.org/10.1016/j.techsoc.2022.101996
• Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Rimini, B., & Regattieri, A. (2019). Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning & Control, 30(1), 76-89. doi: https://doi.org/10.1080/09537287.2018.1525506
• Ma, G., & Wu, M. (2019). A Big Data and FMEA-based construction quality risk evaluation model considering project schedule for Shanghai apartment projects. International Journal of Quality & Reliability Management, 37(1), 18-33. doi: http://dx.doi.org/10.1108/IJQRM-11-2018-0318
• Maiti, C., & Muthuswamy, S. (2023). Classification of materials in cylindrical workpieces using image processing and machine learning techniques. International Journal of Production Research, 1-18. doi: https://doi.org/10.1080/00207543.2023.2219344
• Majumder, M. (2016). Technology as work and work as technology. International Journal of Human Capital and Information Technology Professionals (IJHCITP), 7(1), 20-34. doi: https://doi.org/10.4018/IJHCITP.2016010102
• Mariajayaprakash, A., Senthilvelan, T., & Gnanadass, R. (2015). Optimization of process parameters through fuzzy logic and genetic algorithm–A case study in a process industry. Applied Soft Computing, 30, 94-103. doi: https://doi.org/10.1016/j.asoc.2015.01.042
• Murad, C. A., Melani, A. H. D. A., Michalski, M. A. D. C., Caminada Netto, A., de Souza, G. F. M., & Nabeta, S. I. (2020). Fuzzy-FMSA: Evaluating Fault Monitoring and Detection Strategies Based on Failure Mode and Symptom Analysis and Fuzzy Logic. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6(3), 031001. doi: https://doi.org/10.1115/1.4045974
• Na’amnh, S., Salim, M. B., Husti, I., & Daróczi, M. (2021). Using artificial neural network and fuzzy inference system based prediction to improve failure mode and effects analysis: A case study of the busbars production. Processes, 9(8), 1444. doi: https://doi.org/10.3390/pr9081444
• Nitnara, C., & Tragangoon, K. (2023). Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA. International Journal of Technology, 14(2). doi: https://doi.org/10.14716/ijtech.v14i2.5573
• Norzelan, N. A., Mohamed, I. S., & Mohamad, M. (2024). Technology acceptance of artificial intelligence (AI) among heads of finance and accounting units in the shared service industry. Technological Forecasting and Social Change, 198, 123022. doi: https://doi.org/10.1016/j.techfore.2023.123022
• Öztemel, E. (2020). Yapay Zekâ ve İnsanlığın Geleceği, Ankara: Türkiye Bilimler Akademisi. doi: https://doi.org/10.53478/TUBA.2020.011
• Panda, S. K., Mishra, V., Balamurali, R., & Elngar, A. A. (Eds.). (2021). Artificial Intelligence and Machine Learning in Business Management: Concepts, Challenges, and Case Studies (1st ed.). CRC Press. doi: https://doi.org/10.1201/9781003125129
• Patil, C. K., Husain, M., & Halegowda, N. V. (2018). Study of quality function deployment model based on artificial neural network with optimization techniques. Journal of Advanced Manufacturing Systems, 17(01), 119-136. doi: https://doi.org/10.1142/S0219686718500087
• Petrat, D. (2021). Artificial intelligence in human factors and ergonomics: an overview of the current state of research. Discover Artificial Intelligence, 1(1), 3. doi: http://doi.org/10.1007/s44163-021-00001-5
• Priore, P., Ponte, B., Rosillo, R., & de la Fuente, D. (2019). Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments. International Journal of Production Research, 57(11), 3663-3677. doi: http://dx.doi.org/doi:10.1080/00207543.2018.1552369
• Rasheed, H. M. W., Chen, Y., Khizar, H. M. U., & Safeer, A. A. (2023). Understanding the factors affecting AI services adoption in hospitality: The role of behavioral reasons and emotional intelligence. Heliyon. doi: https://doi.org/10.1016/j.heliyon.2023.e16968
• Reda, H., & Dvivedi, A. (2022). Decision-making on the selection of lean tools using fuzzy QFD and FMEA approach in the manufacturing industry. Expert Systems with Applications, 192, 116416. doi: https://doi.org/10.1016/j.eswa.2021.116416
• Sabahno, H., & Niaki, S. T. A. (2023). New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification. Mathematics, 11(16), 3566. doi: https://doi.org/10.3390/math11163566
• Sharma, A., Zhang, Z., & Rai, R. (2021). The interpretive model of manufacturing: a theoretical framework and research agenda for machine learning in manufacturing. International Journal of Production Research, 59(16), 4960-4994. doi: https://doi.org/10.1080/00207543.2021.1930234
• Singh, A., & Kumar, S. (2021). Picture fuzzy set and quality function deployment approach based novel framework for multi-criteria group decision making method. Engineering Applications of Artificial Intelligence, 104, 104395. doi: https://doi.org/10.1016/j.engappai.2021.104395
• Singh, R., & Mishra, V. K. (2023). Machine learning based fuzzy inventory model for imperfect deteriorating products with demand forecast and partial backlogging under green investment technology. Journal of the Operational Research Society, 1-16. doi: https://doi.org/10.1080/01605682.2023.2239868
• Siskon (2023), Endüstri Devriminin Tarihsel Gelişimi. Erişim adresi: https://www.siskon.com.tr/haberler/endustri-devriminin-tarihsel-gelisimi
• Souza, F. F., Corsi, A., Pagani, R. N., Balbinotti, G., & Kovaleski, J. L. (2021). Total quality management 4.0: adapting quality management to Industry 4.0. The TQM Journal, 1-21. doi: https://doi.org/10.1108/TQM-10-2020-0238
• Stancheva-Todorova, E.P. (2018). How artificial intelligence is challenging accounting profession. “Journal of International Scientific Publications" Economy & Business, 12, 126-141. Erişim adresi: https://www.scientific-publications.net/get/1000031/1536783976137495.pdf
• Swamidass, P.M. (Ed.), (2000), Moving assembly line, Encyclopedia of production and manufacturing management. Springer Science & Business Media, Boston, MA. doi: https://doi.org/10.1007/1-4020-0612-8_596
• Swarnkar, A., Swarnkar, A. (2020). Artificial Intelligence based optimization techniques: A Review. Intelligent Computing Techniques for Smart Energy Systems, 95-103. doi: http://dx.doi.org/10.1007/978-981-15-0214-9_12
• Şahan, A.N. (2020). Stratejik yönetim perspektifinden sigortacılık sektöründe Makine Öğrenmesi algoritmaları ile anomali tespiti [Doktora Tezi, İstanbul Teknik Üniversitesi].
• Tamasiga, P., Onyeaka, H., Bakwena, M., Happonen, A., & Molala, M. (2023). Forecasting disruptions in global food value chains to tackle food insecurity: The role of AI and big data analytics–A bibliometric and scientometric analysis. Journal of Agriculture and Food Research, 14, 100819. doi: https://doi.org/10.1016/j.jafr.2023.100819
• Teksen, H. E., & Anagün, A. S. (2018). Type 2 fuzzy control charts using likelihood and deffuzzification methods. In Advances in Fuzzy Logic and Technology 2017: Proceedings of: EUSFLAT-2017–The 10th Conference of the European Society for Fuzzy Logic and Technology, September 11-15, 2017, Warsaw, Poland IWIFSGN’2017–The Sixteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, September 13-15, 2017, Warsaw, Poland, Volume 3 10 (pp. 405-417). Springer International Publishing. doi: http://dx.doi.org/10.1007/978-3-319-66827-7_37
• Testik, O. M., & Unlu, E. T. (2023). Fuzzy FMEA in risk assessment for test and calibration laboratories. Quality and Reliability Engineering International, 39(2), 575-589. doi: http://doi.org/10.1002/qre.3198
• Tseng, C. Y., Li, J., Lin, L. H., Wang, K., White III, C. C., & Wang, B. (2023). Deep reinforcement learning approach for dynamic capacity planning in decentralised regenerative medicine supply chains. International Journal of Production Research, 1-16. doi: https://doi.org/10.1080/00207543.2023.2262043
• Wan, (2020). Economic-statistical design of integrated model of VSI control chart and maintenance incorporating multiple dependent state sampling. IEEE Access, 8, 87609-87620. doi: https://doi.org/10.1109/ACCESS.2020.2993024
• Wan, Q., Chen, L., & Zhu, M. (2023). A reliability-oriented integration model of production control, adaptive quality control policy and maintenance planning for continuous flow processes. Computers & Industrial Engineering, 176, 108985. doi: https://doi.org/10.1016/j.cie.2023.108985
• Wang, W., Li, R., Chen, Y., Diekel, Z. M., & Jia, Y. (2018). Facilitating human–robot collaborative tasks by teaching-learning-collaboration from human demonstrations. IEEE Transactions on Automation Science and Engineering, 16(2), 640-653. doi: https://doi.org/10.1109/TASE.2018.2840345.
• Wiers, V. C. S. (1997), A Review of the Applicability of OR and AI scheduling techniques in Practice, OMEGA-International Journal of Management Science, 25,2,145-153. doi: https://doi.org/10.1016/S0305-0483(96)00050-3
• Xia, H., Muskat, B., Li, G., & Prayag, G. (2023). Ai-based counterfactual reasoning for tourism research, 101, 103617. doi: https://doi.org/10.1016/j.annals.2023.103617
• Yaiprasert, C., & Hidayanto, A. N. (2023). AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business. Intelligent Systems with Applications, 18, 200235. doi: https://doi.org/10.1016/j.iswa.2023.200235
• Yamamura, C. L. K., Santana, J. C. C., Masiero, B. S., Quintanilha, J. A., & Berssaneti, F. T. (2022). Forecasting New Product Demand Using Domain Knowledge and Machine Learning: A proposed method uses machine learning and an expert’s domain knowledge to enhance the accuracy of new product predictions. Research-Technology Management, 65(4), 27-36. doi: https://doi.org/10.1080/08956308.2022.2062553
• Yasir, M., Ansari, Y., Latif, K., Maqsood, H., Habib, A., Moon, J., & Rho, S. (2022). Machine learning–assisted efficient demand forecasting using endogenous and exogenous indicators for the textile industry. International Journal of Logistics Research and Applications, 1-20. doi: https://doi.org/10.1080/13675567.2022.2100334
• Yeganeh, A., Abbasi, S. A., Shongwe, S. C., Malela-Majika, J. C., & Shadman, A. R. (2023). Evolutionary support vector regression for monitoring Poisson profiles. Soft Computing, 1-25. doi: https://doi.org/10.1007/s00500-023-09047-2
• Yeganeh, A., Johannssen, A., Chukhrova, N., Abbasi, S. A., & Pourpanah, F. (2023). Employing machine learning techniques in monitoring autocorrelated profiles. Neural Computing and Applications, 1-20. doi: https://doi.org/10.1007/s00521-023-08483-3
• Yildirim, S. (2023), Python Libraries You Need to Know in 2023. Erişim adresi: https://learnpython.com/blog/python-libraries/.
• Yoo, S. D., Kim, J. Y., Han, S. K., Lee, B. H., Choi, D. H., & Park, E. S. (2023). Development of prediction model with machine learning in continuous twin screw granulation. Journal of Pharmaceutical Investigation, 1-16. doi: https://doi.org/10.1007/s40005-023-00625-y
• Zhang, HF., Ge, HW., Tong, YB. (2022). Review of Vehicle Routing Problems: Models, Classification and Solving Algorithms. Archievs of Computational Methods in Engineering, 29,1 195-221. doi: https://doi.org/10.1007/s11831-021-09574-x
• Zhang, Y., Peng, P., Liu, C., & Zhang, H. (2019). Anomaly detection for industry product quality inspection based on Gaussian restricted Boltzmann machine. Ekim 2019, IEEE International Conference On Systems, Man And Cybernetics, 1-6. Erişim adresi: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8914524
• Zhang, Y., Zhu, H., Tang, D., Zhou, T., & Gui, Y. (2022). Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems. Robotics and Computer-Integrated Manufacturing, 78, 102412. doi: https://doi.org/10.1016/j.rcim.2022.102412