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ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI

Yıl 2022, Cilt: 27 Sayı: 3, 1285 - 1304, 31.12.2022
https://doi.org/10.17482/uumfd.1123981

Öz

Üretim sistemleri için darboğaz üretim verimliliğini kısıtlayan en etkili faktörlerden biridir. Darboğaza sebep olan bir süreç daha hızlı çalışır ise tüm hattın üretim hızı artacak ve böylelikle üretim süreçlerinin ve tedarik zincirinin devamlılığı sağlanacaktır. Bu sebeple darboğazın tespit edilmesi ve kontrol altına alınması işletmeler için önem kazanmıştır. Literatürde bu konuda çok sayıda yöntem ve çalışma bulunmaktadır. Bu çalışmanın amacı ise literatürde bulunan darboğaz tespiti çalışmalarının incelenmesi, kullanılan yöntemlerin açıklanması ve analiz edilmesidir. Çalışma kapsamında 2007-2022 yıllarına ait toplam 48 makale incelenmiştir. İncelenen çalışmalardan elde edilen sonuçlara göre darboğaz tespitinde en çok benzetim yönteminin kullanıldığı görülmektedir. Aynı zamanda dönüm noktası yöntemi, aktif dönem yöntemi ve matematiksel yöntemler de darboğaz tespitinde diğer yöntemlere göre daha fazla kullanılmaktadır. Son yıllarda ise artan yapay zeka çalışmaları ile birlikte makine öğrenmesi tabanlı yaklaşımlar kullanılmaya başlanmıştır. Literatürde bu kadar sayıda darboğaz tespit yönteminin açıklandığı ve bu konudaki çalışmaların derlenip analiz edildiği bir çalışma bulunmamaktadır. Bu sebeple yapılan çalışmanın ilgili araştırmacılara yol göstermesi hedeflenmektedir.

Teşekkür

Bu çalışmanın 1. Yazarı TÜBİTAK 2211-A Yurt İçi Doktora Burs Programı tarafından desteklenmektedir. Ancak yayın ile ilgili tüm sorumluluk yayının sahibine aittir. Yayının içeriğinin bilimsel anlamda TÜBİTAK tarafından onaylandığı anlamına gelmez.

Kaynakça

  • 1. Alzubi, E., Atieh, A. M., Abu Shgair, K., Damiani, J., Sunna, S. and Madi, A. (2019) Hybrid integrations of value stream mapping, theory of constraints and simulation: application to wooden furniture industry, Processes, 7(11), 816. doi: 10.3390/pr7110816
  • 2. Bernedixen, J. (2018) Automated bottleneck analysis of production systems: increasing the applicability of simulation-based multi-objective optimization for bottleneck analysis within industry, Doctoral Thesis, University of Skövde.
  • 3. Betterton, C.E. and Cox, J.F. (2009) Espoused drum-buffer-rope flow control in serial lines: a comparative study of simulation models, International Journal of Production Economics, 117(1), 66–79. doi: 10.1016/j.ijpe.2008.08.050
  • 4. Betterton, C.E. and Silver, S.J. (2012) Detecting bottlenecks in serial production lines–a focus on interdeparture time variance, International Journal of Production Research, 50(15), 4158-4174 doi: 10.1080/00207543.2011.596847
  • 5. Biller, S., Li, J., Marin, S. P., Meerkov, S. M. and Zhang, L. (2009) Bottlenecks in Bernoulli serial lines with rework, IEEE Transactions on Automation Science and Engineering, 7(2), 208-217. doi: 10.1109/TASE.2009.2023463
  • 6. Blackstone, J.H. (2008) APICS dictionary, Chicago: APICS The Association for Operations Management.
  • 7. Chiang, S.Y., Kuo, C.T. and Meerkov, S.M. (2001) C-bottlenecks in serial production lines – identification and application, Mathematical Problems in Engineering, 7, 543–578. doi: 10.1155/S1024123X01001776
  • 8. Esmaeeli, H. and Aleahmad, M. (2019) Bottleneck detection in job shop production by high-level Petri nets, In 2019 15th Iran International Industrial Engineering Conference (IIIEC), 178-183. doi: 10.1109/IIIEC.2019.8720639
  • 9. Hao, P.C. and Lin, B.M. (2021) Text mining approach for bottleneck detection and analysis in printed circuit board manufacturing, Computers & Industrial Engineering, 154, 107121. doi: 10.1016/j.cie.2021.107121
  • 10. Hofmann, C., Staehr, T., Cohen, S., Stricker, N., Haefner, B. and Lanza, G. (2019) Augmented go & see: an approach for improved bottleneck identification in production lines, Procedia Manufacturing, 31, 148-154. doi: 10.1016/j.promfg.2019.03.023
  • 11. Hopp, W.J. and Spearman, M.L. (2000) Factory physics, 2nd ed. New York, NY: McGraw-Hill.
  • 12. Kang, Y. and Ju, F. (2017) Identifying bottlenecks in serial production lines with geometric machines: indicators and rules, IFAC-PapersOnLine, 50(1), 13952-13957. doi: 10.1016/j.ifacol.2017.08.2217
  • 13. Kumbhar, M., Ng, A.H. and Bandaru, S. (2022) Bottleneck detection through data ıntegration, process mining and factory physics-based analytics, In 10th Swedish Production Symposium (SPS2022), Skövde, 737-748. doi:10.3233/ATDE220192
  • 14. Kuo, C.T., Lim, J.T. and Meerkov, S.M. (1996) Bottlenecks in serial production lines: a system-theoretic approach, Mathematical Problems in Engineering, 2, 233–276. doi: 10.1155/S1024123X96000348
  • 15. Kwon, C.M. and Lim, S. (2013) Bottleneck detection based on duration of active periods, Journal of The Korea Society for Simulation, 22(3), 35-41. doi: 10.9709/JKSS.2013.22.3.035
  • 16. Lai, X., Shui, H., Ding, D. and Ni, J. (2021) Data-driven dynamic bottleneck detection in complex manufacturing systems, Journal of Manufacturing Systems, 60, 662-675. doi: 10.1016/j.jmsy.2021.07.016
  • 17. Lawrence, S.R. and Buss, A.H. (1994) Shifting production bottlenecks: causes, cures, and conundrums, Production and Operations Management, 3(1), 21–37. doi: 10.1111/j.1937-5956.1994.tb00107.x
  • 18. Lemessi, M., Rehbein, S., Rehn, G. and Schulze, T. (2012) Semi-automatic simulation-based bottleneck detection approach, In Proceedings of the 2012 Winter Simulation Conference (WSC), 1-12. doi: 10.1109/WSC.2012.6465048
  • 19. Leporis, M. and Králová, Z. (2010) A simulation approach to production line bottleneck analysis, In International Conference Cybernetics and Informatics, 13-22.
  • 20. Li, L., Chang, Q., Ni, J., Xiao, G. and Biller, S. (2007) Bottleneck detection of manufacturing systems using data driven method, In 2007 IEEE International Symposium on Assembly and Manufacturing, 76-81. doi: 10.1109/ISAM.2007.4288452
  • 21. Li, L., Chang, Q., Ni, J. and Biller, S. (2009a) Real time production improvement through bottleneck control, International Journal of Production Research, 47(21), 6145-6158. doi: 10.1080/00207540802244240
  • 22. Li, L., Chang, Q. and Ni, J. (2009b) Data driven bottleneck detection of manufacturing systems, IntJ Prod Res, 47, 5019–5036. doi: 10.1080/00207540701881860
  • 23. Li, L. (2018) A systematic-theoretic analysis of data-driven throughput bottleneck detection of production systems, Journal of Manufacturing Systems, 47, 43-52. doi: 10.1016/j.jmsy.2018.03.001
  • 24. Lima, E., Chwif, L. and Barreto, M.R.P. (2008) Metodology for selecting the best suitable bottleneck detection method, In 2008 Winter Simulation Conference, 1746-1751. doi: 10.1109/WSC.2008.4736262
  • 25. Lizarralde-Aiastui, A., Apaolaza-Perez de Eulate, U. and Mediavilla-Guisasola, M. (2020) A strategic approach for bottleneck identification in make-to-order environments: A drum-buffer-rope action research based case study, Journal of Industrial Engineering and Management (JIEM), 13(1), 18-37. doi: 10.3926/jiem.2868
  • 26. McClelland, G. (2022) Data-driven bottleneck identification for serial production lines, Doctoral Thesis, Queen’s University, Canada.
  • 27. Muthiah, K.M.N. and Huang, S.H. (2007) Overall throughput effectiveness (OTE) metric for factory-level performance monitoring and bottleneck detection, International Journal of Production Research, 45(20), 4753-4769. doi: 10.1080/00207540600786731
  • 28. Nandakumar, N., Saleeshya, P. G. and Harikumar, P. (2020) Bottleneck identification and process improvement by lean six sigma DMAIC methodology, Materials Today: Proceedings, 24, 1217-1224. doi: 10.1016/j.matpr.2020.04.436
  • 29. Ongbali, S.O., Afolalu, S.A. and Igboanugo, A.C. (2018) Bottleneck problem detection in production system using Fourier transform analytics, International Journal of Mechanical Engineering and Technology, 9(12), 113-122.
  • 30. Roh P., Kunz, A. and Netland, T. (2018) Data-driven detection of moving bottlenecks in multi-variant production lines, IFAC-PapersOnLine, 51(11), 158-163. doi: 10.1016/j.ifacol.2018.08.251
  • 31. Roser, C., Lorentzen, K. and Deuse, J. (2014) Reliable shop floor bottleneck detection for flow lines through process and inventory observations, Procedia Cirp, 19, 63-68. doi: 10.1016/j.procir.2014.05.020
  • 32. Roser, C., Nakano, M. and Tanaka, M. (2001) A practical bottleneck detection method, In Proceeding of the 2001 Winter Simulation Conference, 2, 949-953. doi: 10.1109/WSC.2001.977398
  • 33. Roser, C., Nakano, M. and Tanaka, M. (2002) Shifting bottleneck detection, Winter Simulation Conference. doi: 10.1109/WSC.2002.1166360
  • 34. Roser, C., Nakano, M. and Tanaka, M. (2003) Comparison of bottleneck detection methods for AGV systems, In Winter Simulation Conference, 2, 1192-1198. doi: 10.1109/WSC.2003.1261549
  • 35. Roser, C. and Nakano, M. (2015) A quantitative comparison of bottleneck detection methods in manufacturing systems with particular consideration for shifting bottlenecks, In IFIP International Conference on Advances in Production Management Systems, 273-281. doi: 10.1007/978-3-319-22759-7_32
  • 36. Roser, C., Subramaniyan, M., Skoogh, A. and Johansson, B. (2021) An enhanced data-driven algorithm for shifting bottleneck detection. In IFIP International Conference on Advances in Production Management Systems, 683-689. doi: 10.1007/978-3-030-85874-2_74
  • 37. Rudnitckaia, J., Venkatachalam, H. S., Essmann, R., Hruška, T., and Colombo, A. W. (2022) Screening process mining and value stream techniques on industrial manufacturing processes: process modelling and bottleneck analysis. IEEE Access, 10, 24203-24214. doi: 10.1109/ACCESS.2022.3152211
  • 38. Sengupta, S., Das, K. and Vantil, R.P. (2008) A new method for bottleneck detection, In 2008 Winter Simulation Conference, 1741-1745. doi: 10.1109/WSC.2008.4736261
  • 39. Singh, M. and Thathia, H. (2019) Analytic tool for identifying bottlenecks using turning point method, Master’s Thesis, Chalmers Unıversıty of Technology.
  • 40. Su, X., Lu, J., Chen, C., Yu, J. and Ji, W. (2022) Dynamic bottleneck identification of manufacturing resources in complex manufacturing system, Applied Sciences, 12(9), 4195. doi: 10.3390/app12094195
  • 41. Subramaniyan, M., Skoogh, A., Gopalakrishnan, M. and Hanna, A. (2016) Real-time data-driven average active period method for bottleneck detection, International Journal of Design & Nature and Ecodynamics, 11(3), 428-437. doi: 10.2495/DNE-V11-N3-428-437
  • 42. Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P. and Bokrantz, J. (2018a) A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines, Computers & Industrial Engineering, 125, 533-544. doi: 10.1016/j.cie.2018.04.024
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Bottleneck Detection in Production Systems: Literature Research

Yıl 2022, Cilt: 27 Sayı: 3, 1285 - 1304, 31.12.2022
https://doi.org/10.17482/uumfd.1123981

Öz

For production systems, the bottleneck is one of the most effective factors limiting production efficiency. If a process that causes a bottleneck runs faster, the production speed of the entire line will increase, thus ensuring the continuity of the production processes and supply chain. For this reason, it has become important for businesses to detect and control bottlenecks. There are many methods and studies on this subject in the literature. This study aims to examine the bottleneck detection studies in the literature and to explain and analyze the methods used. Within the scope of the study, a total of 48 articles belonging to the years 2007-2022 were examined. According to the results obtained from the studies examined, it is seen that the simulation method is mostly used in bottleneck detection. At the same time, the turning point method, active period method and mathematical methods are also more used in bottleneck detection than other methods. In recent years, machine learning-based approaches have been used in with increasing artificial intelligence studies. There is no study in the literature in which so many bottleneck detection methods are explained and studies on this subject are compiled and analyzed. For this reason, it is aimed that the study will guide the relevant researchers.

Kaynakça

  • 1. Alzubi, E., Atieh, A. M., Abu Shgair, K., Damiani, J., Sunna, S. and Madi, A. (2019) Hybrid integrations of value stream mapping, theory of constraints and simulation: application to wooden furniture industry, Processes, 7(11), 816. doi: 10.3390/pr7110816
  • 2. Bernedixen, J. (2018) Automated bottleneck analysis of production systems: increasing the applicability of simulation-based multi-objective optimization for bottleneck analysis within industry, Doctoral Thesis, University of Skövde.
  • 3. Betterton, C.E. and Cox, J.F. (2009) Espoused drum-buffer-rope flow control in serial lines: a comparative study of simulation models, International Journal of Production Economics, 117(1), 66–79. doi: 10.1016/j.ijpe.2008.08.050
  • 4. Betterton, C.E. and Silver, S.J. (2012) Detecting bottlenecks in serial production lines–a focus on interdeparture time variance, International Journal of Production Research, 50(15), 4158-4174 doi: 10.1080/00207543.2011.596847
  • 5. Biller, S., Li, J., Marin, S. P., Meerkov, S. M. and Zhang, L. (2009) Bottlenecks in Bernoulli serial lines with rework, IEEE Transactions on Automation Science and Engineering, 7(2), 208-217. doi: 10.1109/TASE.2009.2023463
  • 6. Blackstone, J.H. (2008) APICS dictionary, Chicago: APICS The Association for Operations Management.
  • 7. Chiang, S.Y., Kuo, C.T. and Meerkov, S.M. (2001) C-bottlenecks in serial production lines – identification and application, Mathematical Problems in Engineering, 7, 543–578. doi: 10.1155/S1024123X01001776
  • 8. Esmaeeli, H. and Aleahmad, M. (2019) Bottleneck detection in job shop production by high-level Petri nets, In 2019 15th Iran International Industrial Engineering Conference (IIIEC), 178-183. doi: 10.1109/IIIEC.2019.8720639
  • 9. Hao, P.C. and Lin, B.M. (2021) Text mining approach for bottleneck detection and analysis in printed circuit board manufacturing, Computers & Industrial Engineering, 154, 107121. doi: 10.1016/j.cie.2021.107121
  • 10. Hofmann, C., Staehr, T., Cohen, S., Stricker, N., Haefner, B. and Lanza, G. (2019) Augmented go & see: an approach for improved bottleneck identification in production lines, Procedia Manufacturing, 31, 148-154. doi: 10.1016/j.promfg.2019.03.023
  • 11. Hopp, W.J. and Spearman, M.L. (2000) Factory physics, 2nd ed. New York, NY: McGraw-Hill.
  • 12. Kang, Y. and Ju, F. (2017) Identifying bottlenecks in serial production lines with geometric machines: indicators and rules, IFAC-PapersOnLine, 50(1), 13952-13957. doi: 10.1016/j.ifacol.2017.08.2217
  • 13. Kumbhar, M., Ng, A.H. and Bandaru, S. (2022) Bottleneck detection through data ıntegration, process mining and factory physics-based analytics, In 10th Swedish Production Symposium (SPS2022), Skövde, 737-748. doi:10.3233/ATDE220192
  • 14. Kuo, C.T., Lim, J.T. and Meerkov, S.M. (1996) Bottlenecks in serial production lines: a system-theoretic approach, Mathematical Problems in Engineering, 2, 233–276. doi: 10.1155/S1024123X96000348
  • 15. Kwon, C.M. and Lim, S. (2013) Bottleneck detection based on duration of active periods, Journal of The Korea Society for Simulation, 22(3), 35-41. doi: 10.9709/JKSS.2013.22.3.035
  • 16. Lai, X., Shui, H., Ding, D. and Ni, J. (2021) Data-driven dynamic bottleneck detection in complex manufacturing systems, Journal of Manufacturing Systems, 60, 662-675. doi: 10.1016/j.jmsy.2021.07.016
  • 17. Lawrence, S.R. and Buss, A.H. (1994) Shifting production bottlenecks: causes, cures, and conundrums, Production and Operations Management, 3(1), 21–37. doi: 10.1111/j.1937-5956.1994.tb00107.x
  • 18. Lemessi, M., Rehbein, S., Rehn, G. and Schulze, T. (2012) Semi-automatic simulation-based bottleneck detection approach, In Proceedings of the 2012 Winter Simulation Conference (WSC), 1-12. doi: 10.1109/WSC.2012.6465048
  • 19. Leporis, M. and Králová, Z. (2010) A simulation approach to production line bottleneck analysis, In International Conference Cybernetics and Informatics, 13-22.
  • 20. Li, L., Chang, Q., Ni, J., Xiao, G. and Biller, S. (2007) Bottleneck detection of manufacturing systems using data driven method, In 2007 IEEE International Symposium on Assembly and Manufacturing, 76-81. doi: 10.1109/ISAM.2007.4288452
  • 21. Li, L., Chang, Q., Ni, J. and Biller, S. (2009a) Real time production improvement through bottleneck control, International Journal of Production Research, 47(21), 6145-6158. doi: 10.1080/00207540802244240
  • 22. Li, L., Chang, Q. and Ni, J. (2009b) Data driven bottleneck detection of manufacturing systems, IntJ Prod Res, 47, 5019–5036. doi: 10.1080/00207540701881860
  • 23. Li, L. (2018) A systematic-theoretic analysis of data-driven throughput bottleneck detection of production systems, Journal of Manufacturing Systems, 47, 43-52. doi: 10.1016/j.jmsy.2018.03.001
  • 24. Lima, E., Chwif, L. and Barreto, M.R.P. (2008) Metodology for selecting the best suitable bottleneck detection method, In 2008 Winter Simulation Conference, 1746-1751. doi: 10.1109/WSC.2008.4736262
  • 25. Lizarralde-Aiastui, A., Apaolaza-Perez de Eulate, U. and Mediavilla-Guisasola, M. (2020) A strategic approach for bottleneck identification in make-to-order environments: A drum-buffer-rope action research based case study, Journal of Industrial Engineering and Management (JIEM), 13(1), 18-37. doi: 10.3926/jiem.2868
  • 26. McClelland, G. (2022) Data-driven bottleneck identification for serial production lines, Doctoral Thesis, Queen’s University, Canada.
  • 27. Muthiah, K.M.N. and Huang, S.H. (2007) Overall throughput effectiveness (OTE) metric for factory-level performance monitoring and bottleneck detection, International Journal of Production Research, 45(20), 4753-4769. doi: 10.1080/00207540600786731
  • 28. Nandakumar, N., Saleeshya, P. G. and Harikumar, P. (2020) Bottleneck identification and process improvement by lean six sigma DMAIC methodology, Materials Today: Proceedings, 24, 1217-1224. doi: 10.1016/j.matpr.2020.04.436
  • 29. Ongbali, S.O., Afolalu, S.A. and Igboanugo, A.C. (2018) Bottleneck problem detection in production system using Fourier transform analytics, International Journal of Mechanical Engineering and Technology, 9(12), 113-122.
  • 30. Roh P., Kunz, A. and Netland, T. (2018) Data-driven detection of moving bottlenecks in multi-variant production lines, IFAC-PapersOnLine, 51(11), 158-163. doi: 10.1016/j.ifacol.2018.08.251
  • 31. Roser, C., Lorentzen, K. and Deuse, J. (2014) Reliable shop floor bottleneck detection for flow lines through process and inventory observations, Procedia Cirp, 19, 63-68. doi: 10.1016/j.procir.2014.05.020
  • 32. Roser, C., Nakano, M. and Tanaka, M. (2001) A practical bottleneck detection method, In Proceeding of the 2001 Winter Simulation Conference, 2, 949-953. doi: 10.1109/WSC.2001.977398
  • 33. Roser, C., Nakano, M. and Tanaka, M. (2002) Shifting bottleneck detection, Winter Simulation Conference. doi: 10.1109/WSC.2002.1166360
  • 34. Roser, C., Nakano, M. and Tanaka, M. (2003) Comparison of bottleneck detection methods for AGV systems, In Winter Simulation Conference, 2, 1192-1198. doi: 10.1109/WSC.2003.1261549
  • 35. Roser, C. and Nakano, M. (2015) A quantitative comparison of bottleneck detection methods in manufacturing systems with particular consideration for shifting bottlenecks, In IFIP International Conference on Advances in Production Management Systems, 273-281. doi: 10.1007/978-3-319-22759-7_32
  • 36. Roser, C., Subramaniyan, M., Skoogh, A. and Johansson, B. (2021) An enhanced data-driven algorithm for shifting bottleneck detection. In IFIP International Conference on Advances in Production Management Systems, 683-689. doi: 10.1007/978-3-030-85874-2_74
  • 37. Rudnitckaia, J., Venkatachalam, H. S., Essmann, R., Hruška, T., and Colombo, A. W. (2022) Screening process mining and value stream techniques on industrial manufacturing processes: process modelling and bottleneck analysis. IEEE Access, 10, 24203-24214. doi: 10.1109/ACCESS.2022.3152211
  • 38. Sengupta, S., Das, K. and Vantil, R.P. (2008) A new method for bottleneck detection, In 2008 Winter Simulation Conference, 1741-1745. doi: 10.1109/WSC.2008.4736261
  • 39. Singh, M. and Thathia, H. (2019) Analytic tool for identifying bottlenecks using turning point method, Master’s Thesis, Chalmers Unıversıty of Technology.
  • 40. Su, X., Lu, J., Chen, C., Yu, J. and Ji, W. (2022) Dynamic bottleneck identification of manufacturing resources in complex manufacturing system, Applied Sciences, 12(9), 4195. doi: 10.3390/app12094195
  • 41. Subramaniyan, M., Skoogh, A., Gopalakrishnan, M. and Hanna, A. (2016) Real-time data-driven average active period method for bottleneck detection, International Journal of Design & Nature and Ecodynamics, 11(3), 428-437. doi: 10.2495/DNE-V11-N3-428-437
  • 42. Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P. and Bokrantz, J. (2018a) A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines, Computers & Industrial Engineering, 125, 533-544. doi: 10.1016/j.cie.2018.04.024
  • 43. Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P., Gopalakrishnan, M. and Sheikh Muhammad, A. (2018b) Data-driven algorithm for throughput bottleneck analysis of production systems, Production & Manufacturing Research, 6(1), 225-246. doi: 10.1080/21693277.2018.1496491
  • 44. Subramaniyan, M., Skoogh, A., Muhammad, A. S., Bokrantz, J., Johansson, B. and Roser, C. (2020a) A generic hierarchical clustering approach for detecting bottlenecks in manufacturing, Journal of Manufacturing Systems, 55, 143-158. doi: 10.1016/j.jmsy.2020.02.011
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  • 46. Tang, H. (2019) A new method of bottleneck analysis for manufacturing systems, Manufacturing Letters, 19, 21-24. doi: 10.1016/j.mfglet.2019.01.003
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  • 54. Yemane, A., Haque, S. and Malfanti, I. S. (2017) Bottleneck identification using time study and simulation modeling of apparel industries, Proceedings of the International Conference on Industrial Engineering and Operations Management Bogota, Colombia.
  • 55. Yu, C. and Matta, A. (2014) Data-driven bottleneck detection in manufacturing systems: A statistical approach, In 2014 IEEE International Conference on Automation Science and Engineering (CASE), 710-715. doi: 10.1109/CoASE.2014.6899406
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Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Endüstri Mühendisliği
Bölüm Derleme Makaleler
Yazarlar

Nagihan Akkurt 0000-0002-8128-2964

Servet Hasgül 0000-0002-9329-6335

Erken Görünüm Tarihi 9 Aralık 2022
Yayımlanma Tarihi 31 Aralık 2022
Gönderilme Tarihi 1 Haziran 2022
Kabul Tarihi 8 Ekim 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 27 Sayı: 3

Kaynak Göster

APA Akkurt, N., & Hasgül, S. (2022). ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 27(3), 1285-1304. https://doi.org/10.17482/uumfd.1123981
AMA Akkurt N, Hasgül S. ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI. UUJFE. Aralık 2022;27(3):1285-1304. doi:10.17482/uumfd.1123981
Chicago Akkurt, Nagihan, ve Servet Hasgül. “ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27, sy. 3 (Aralık 2022): 1285-1304. https://doi.org/10.17482/uumfd.1123981.
EndNote Akkurt N, Hasgül S (01 Aralık 2022) ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27 3 1285–1304.
IEEE N. Akkurt ve S. Hasgül, “ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI”, UUJFE, c. 27, sy. 3, ss. 1285–1304, 2022, doi: 10.17482/uumfd.1123981.
ISNAD Akkurt, Nagihan - Hasgül, Servet. “ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27/3 (Aralık 2022), 1285-1304. https://doi.org/10.17482/uumfd.1123981.
JAMA Akkurt N, Hasgül S. ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI. UUJFE. 2022;27:1285–1304.
MLA Akkurt, Nagihan ve Servet Hasgül. “ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, c. 27, sy. 3, 2022, ss. 1285-04, doi:10.17482/uumfd.1123981.
Vancouver Akkurt N, Hasgül S. ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI. UUJFE. 2022;27(3):1285-304.

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