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Öğrenme Eğrilerinin Karşılaştırılması

Year 2021, , 119 - 129, 25.06.2021
https://doi.org/10.46810/tdfd.833452

Abstract

Şirketlerin iç ve dış süreçlerinde öğrenme eğrisi önemli bir yer tutmaktadır. Üretim sektöründe üretim miktarı veya işin tekrar sayısı arttıkça üretim süresi belirli bir yüzde oranında azalır. Bu çalışmada farklı öğrenme eğri modelleri için üretim miktarının üretim süresi üzerindeki etkisi incelenmiştir. Öğrenme eğrisi modellerinin karşılaştırılması amacı kullanılan veri seti rastgele üretilmiştir. Üretilen veri seti, öğrenme eğrisi eşitlikleri ve lüteratürde önerilen katsayılar kullanılarak üretim için gerekli iş gücü süresi Matlab 2020b yazılımında hesaplanmıştır. Öğrenme eğrisi modelleri için tekrar sayısına bağlı olarak gerekli iş gücü mikarının değişimi araştırılmıştır. İncelenen tüm öğrenme modelleri, üretim miktarının artmasıyla üretim süresinin kısalacağını öngörmüştür. Üretim süresinin belirlenmesinde kullanılacak öğrenme eğrisi modeli üretim prosesine uygun olarak seçilmelidir. Birey ve grup arasında tecrübe aktarımınının modellenmesine imkan tanıyan HLO algoritması iş postaları halinde çalışılan bakım hatlarında üretim süresinin belirlenmesine yönelik büyük bir potansiyele sahiptir.

References

  • Ebbinghaus H, Memory: a contribution to experimental psychology. Ann Neurosci. 2013;20(4): 155-156.
  • Pegels CC, On Startup or Learning Curves: An Expanded View. A I I E Transactions. 1969;1(3): 216-222.
  • Wright TP, Factors affecting the cost of airplanes.Journal of the Aeronautical Sciences. 1936;3: 122-128.
  • Hirsch WZ, Manufacturing Progress Functions. The Review of Economics and Statistics. 1952;34(2): 143-155.
  • Andress FJ, The Learning Curve as a Production Tool, Harvard University;1954
  • Lundberg RH, Learning Curve Theory as Applied to Production Costs. SAE International. 1956; 64: 775-781.
  • Carlson JG, How Management Can Use the Improvement Phenomenon. California Management Review. 1961;3(2): 83-94.
  • Badiru AB, Manufacturing cost estimation: A multivariate learning curve approach. Journal of Manufacturing Systems. 1991;10(6):431-441.
  • Badiru AB, Multivariate Learning Curve Model for Manufacturing Economic Analysis, in Economics of Advanced Manufacturing Systems. Springer: Boston, MA; 1992
  • Thomopoulos NT, Lehman M, The Mixed Model Learning Curve. A I I E Transactions, 1969;1(2):127-132.
  • Argote L, Insko C, Yovetich N, Romero A, Group Learning Curves: The Effects of Turnover and Task Complexity on Group Performance1. Journal of Applied Social Psychology. 1995;25: 512-529.
  • Peltokorpi J, Jaber and MY. A group learning curve model with motor, cognitive and waste elements. Computers & Industrial Engineering. 2020;146:106621.
  • Biskup D. Single-machine scheduling with learning considerations. European Journal of Operational Research. 1999;115(1):173-178.
  • Lee WC. Scheduling with general position-based learning curves. Information Sciences. 2011;181(24):5515-5522.
  • Wang L, Ni H, Yang R, Fei M, Ye W. A Simple Human Learning Optimization Algorithm. 2014;462:56-65.
  • Wang L, Pei J, Menhas MI, Pi J, Fei M, Pardalos P.M. A Hybrid-coded Human Learning Optimization for mixed-variable optimization problems. Knowledge-Based Systems. 2017;127: 114-125.
  • Shoja A, Molla-Alizadeh-Zavardehi S, Niroomand S. Hybrid adaptive simplified human learning optimization algorithms for supply chain network design problem with possibility of direct shipment. Applied Soft Computing. 2020;96:106594.
  • Wei Z, Zhang Y, Xu X, Shi L, Feng L. A task scheduling algorithm based on Q-learning and shared value function for WSNs. Computer Networks. 2017;126: 141-149.
  • Wei Z, Liu F, Zhang Y, Xu J, Ji J, Lyu Z. A Q-learning algorithm for task scheduling based on improved SVM in wireless sensor networks.Computer Networks. 2019;161:138-149.
  • Wene CO, Quantum modelling of the learning curve. Futures. 2018;103: 123-135.
  • Li Y, Yang X, Yang Z. Uncertain learning curve and its application in scheduling. Computers & Industrial Engineering. 2019;131:534-541.
  • Valsamis E, Sukeik M. Evaluating learning and change in orthopaedics: What is the evidence-base? World Journal of Orthopaedics. 2019;10.
  • Zhang L, Liu J, Luo M, Chang X, Zheng Q, Hauptmann AG. Scheduled sampling for one-shot learning via matching network. Pattern Recognition. 2019;96: 106962.
  • Palmanovich E, Ohana N, Atzmon R, Slevin O, Brin Y, Feldman V, Segal D. MICA: A Learning Curve. The Journal of Foot and Ankle Surgery. 2020;59(4):781-783.
  • Kang JN, Wei YM, Liu L, Han R, Chen H, Li J, Wang JW, Yu BY. The Prospects of Carbon Capture and Storage in China’s Power Sector under the 2 °C Target: A Component-based Learning Curve Approach. International Journal of Greenhouse Gas Control. 2020;101: 103149.
  • Zhou P, He X, Luo S, Yu H, Sun G. JPAS: Job-progress-aware flow scheduling for deep learning clusters. Journal of Network and Computer Applications. 2020;158: 102590.
  • Wang H, Wu Y, Min G, Xu J, Tang P. Data-driven dynamic resource scheduling for network slicing: A Deep reinforcement learning approach. Information Sciences, 2019;498: 106-116.
  • Luo S. Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning. Applied Soft Computing. 2020;91:106208.
  • Tong Z, Chen H, Deng X, Li K, Li K. A scheduling scheme in the cloud computing environment using deep Q-learning. Information Sciences. 2020;512:1170-1191.
  • Garg A, Milliman P. The aircraft progress curve modified for design changes. 1961.
  • Nadler G, Smith WD. Manufacturing Progress Functions for Types of Processes. International Journal of Production Research. 1963;2(2):115-135.
  • Hirschmann WB. Profit from the Learning Curve. Profit from the Learning Curve. 1964;2.
  • Keachie EC, Fontana RJ, Effects of Learning on Optimal Lot Size. Management Science. 1966;13(2): B-102-B-108.
  • Knecht GR. Costing, Technological Growth and Generalized Learning Curves. Journal of the Operational Research Society. 1974;25(3):487-491.
  • Montgomery, D. and G. Day, Diagnosing the Experience Curve. Journal of Marketing, 1983. 47.
  • Yelle L. Common Flaws in Learning Curve Analysis. Journal of Purchasing and Materials Management. 1985;21:10-15.
  • Fine CH. Quality Improvement and Learning in Productive Systems. Management Science, 1986;32(10):1301-1315.
  • Reis DA. Learning Curves in Food Services. The Journal of the Operational Research Society. 1991;42(8):623-629.
  • Jordan RB. How to Use the Learning Curve. Materials Management Institute;1965.
  • Baloff N. The Learning Curve--Some Controversial Issues. The Journal of Industrial Economics. 1966;14(3):275-282.
  • Baloff N. Extension of the Learning Curve-Some Empirical Results. Journal of the Operational Research Society. 1971;22(4):329-340.
  • Badiru AB. Quality improvement through learning curve analysis. In: Handbook of Total Quality Management. Springer: Boston, MA;1998.
  • Jaber MY, Glock CH, Zanoni S. A Learning Curve with Improvement in Process Quality. IFAC-PapersOnLine. 2018;51(11):681-685.
  • Kemerer C. How the Learning Curve Affects CASE Tool Adoption. IEEE Software. 1992;9:23-28.
  • Heng TM, Low L. Estimating and comparing learning curves in three Asian economies. Asia Pacific Journal of Management. 1995;12(1):21-35.
  • Klenow P. Learning Curves and the Cyclical Behavior of Manufacturing Industries. Review of Economic Dynamics. 1998;1:531-550.
  • Zangwill W, Kantor P. Toward a Theory of Continuous Improvement and the Learning Curve. Management Science. 1998;4:910-920.
  • Mosheiov G, Sidney JB. Scheduling with general job-dependent learning curves. European Journal of Operational Research. 2003;147(3):665-670.
  • Lee WC, Chuang MC, Yeh WC. Uniform parallel-machine scheduling to minimize makespan with position-based learning curves. Computers & Industrial Engineering. 2012;63(4):813-818.
  • Senyigit E, Atici U. Scheduling with Job Dependent Learning Effect and Ergonomic Risk Deterioration. 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT 2018).
  • Şenyiğit E, Atici U, Şenol MB. Effects of OCRA parameters and learning rate on machine scheduling. Central European Journal of Operations Research, 2020.
  • Li K, Chen J, Fu H, Jia Z, Wu J. Parallel machine scheduling with position-based deterioration and learning effects in an uncertain manufacturing system. Computers & Industrial Engineering. 2020;149:106858.
  • Soleimani H, Ghaderi H, Tsai PW, Zarbakhshnia N, Maleki M. Scheduling of unrelated parallel machines considering sequence-related setup time, start time-dependent deterioration, position-dependent learning and power consumption minimization. Journal of Cleaner Production. 2020;249:119428.
  • Ding H, Gu X. Hybrid of human learning optimization algorithm and particle swarm optimization algorithm with scheduling strategies for the flexible job-shop scheduling problem. Neurocomputing. 2020;14:313-332.
  • Malyusz L. Learning Curve Effect on Project Scheduling. Procedia Engineering. 2016;164:90-97.
  • Mályusz L, Varga A. An Estimation of the Learning Curve Effect on Project Scheduling with Calendar Days Calculation. Procedia Engineering. 2017;196:730-737.
  • Tosselli L, Bogado V, Martínez E. A repeated-negotiation game approach to distributed (re)scheduling of multiple projects using decoupled learning. Simulation Modelling Practice and Theory. 2020;98:101980.
  • Li Z, Ye Y, Wu Z, Wang B. Learning Curve Analysis of Laparoscopic Kasai Portoenterostomy. Journal of Laparoendoscopic & Advanced Surgical Techniques. 2017;27.
  • Feldman L, Cao SJ, Andalib A, Fraser S, Fried GM. A method to characterize the learning curve for performance of a fundamental laparoscopic simulator task: Defining “learning plateau” and “learning rate”. Surgery. 2009;146(2): 381-386.
  • Son K, Lee KB. Prediction of learning curves of 2 dental CAD software programs, part 2: Differences in learning effects by type of dental personnel. The Journal of Prosthetic Dentistry. 2020;123(5):747-752.
  • Alikhanov R. An Invited Commentary on: “Comparison of the learning curves for robotic left and right hemiheparectomy: A prospective cohort study”. International journal of surgery. 2020;81:19-25.
  • Liu Q, Zhang T, Hu M, Zhao Z, Zhao G, Li C, Zhang X, Lau WY, Liu R. Comparison of the learning curves for robotic left and right hemihepatectomy: A prospective cohort study. International Journal of Surgery. 2020;81: 19-25.
  • Ahn Y, Lee S, Son S, Kim H, Kim JE. Learning Curve for Transforaminal Percutaneous Endoscopic Lumbar Discectomy: A Systematic Review. World Neurosurgery. 2020;143:471-479.
  • Zhou D, Ding H, Zhou P, Wang Q. Learning curve with input price for tracking technical change in the energy transition process. Journal of Cleaner Production. 2019;235: 997-1005.
  • Ramirez R, Bhatti Y, Tapinos E. Exploring how experience and learning curves decrease the time invested in scenario planning interventions. Technological Forecasting and Social Change, 2020;151:19785.
  • Ören K, Erol M, Learning Curves, Usage Of Learning Curves in Reducing Workforce Costs As A Tool and An Emprıical Study. Fırat University Journal of Social Science. 2009;19(1):133-141.
  • Moore JR, A Comparative Study of Learning Curve Models in Defense Airframe Cost Estimating, in Air Force Institute of Technology. Department of The Air Force Air University: Wright-Patterson Air Force Base Ohio. 2015;156.
  • Mark G, Rauch E, Matt and DT. Study of the impact of projection-based assistance systems for improving the learning curve in assembly processes. Procedia CIRP, 2020;88:98-103.
  • Hong S, Yang T, Chang HJ, Hong S. The effect of switching renewable energy support systems on grid parity for photovoltaics: Analysis using a learning curve model. Energy Policy. 2020;138:111233.
  • Francis S, Kolil V, Achuthan K. Learning curve analysis for virtual laboratory experimentation. 2016.
  • Abdelkhalek HA, Refaie HS, Aziz RF. Optimization of time and cost through learning curve analysis. Ain Shams Engineering Journal. 2020.
  • Thomas HR, Mathews CT, Ward JG. Learning Curve Models of Construction Productivity. Journal of Construction Engineering and Management. 1986;112(2):245-258.
  • Asher H. Cost-Quantity Relationships in the Airframe industry. The Rand Corporation: Santa Monica; 1956.
  • DeJong JR. The Effects of Increasing Skill on Cycle Time And Its Consequences for Time Standards. Ergonomics. 1957;1(1):51-60.
  • Levy FK. Adaptation in the Production Process. Management Science. 1965;11(6):B136-B154.
  • Glover JH. Manufacturing progress functions I. An alternative model and Its comparison with existing functions. International Journal of Production Research. 1965;4(4):279-300.
  • Yelle LE. Estimating learning curves for potential products. Industrial Marketing Management. 1976;5(2):147-154.
  • Wang L, Ni H, Yang R, Pardalos PM, Du X, Fei M. An adaptive simplified human learning optimization algorithm. Information Sciences. 2015;320:126-139.
  • Wang L, Yang R, Ni H, Ye W, Fei M, Pardalos PM. A human learning optimization algorithm and its application to multi-dimensional knapsack problems. Applied Soft Computing. 2015;34: 736-743.
  • Wang L, Pei J, Wen Y, Pi J, Fei M, Pardalos PM. An improved adaptive human learning algorithm for engineering optimization. Applied Soft Computing; 2018;71:894-904.
  • Ralli P, Panas A, Pantouvakis JP, Karagiannakidis D. Investigation and Comparative Analysis of Learning Curve Models on Construction Productivity: The Case of Caisson Fabrication Process. Journal of Engineering, Project, and Production Management. 2020;10(3):219-230.
Year 2021, , 119 - 129, 25.06.2021
https://doi.org/10.46810/tdfd.833452

Abstract

References

  • Ebbinghaus H, Memory: a contribution to experimental psychology. Ann Neurosci. 2013;20(4): 155-156.
  • Pegels CC, On Startup or Learning Curves: An Expanded View. A I I E Transactions. 1969;1(3): 216-222.
  • Wright TP, Factors affecting the cost of airplanes.Journal of the Aeronautical Sciences. 1936;3: 122-128.
  • Hirsch WZ, Manufacturing Progress Functions. The Review of Economics and Statistics. 1952;34(2): 143-155.
  • Andress FJ, The Learning Curve as a Production Tool, Harvard University;1954
  • Lundberg RH, Learning Curve Theory as Applied to Production Costs. SAE International. 1956; 64: 775-781.
  • Carlson JG, How Management Can Use the Improvement Phenomenon. California Management Review. 1961;3(2): 83-94.
  • Badiru AB, Manufacturing cost estimation: A multivariate learning curve approach. Journal of Manufacturing Systems. 1991;10(6):431-441.
  • Badiru AB, Multivariate Learning Curve Model for Manufacturing Economic Analysis, in Economics of Advanced Manufacturing Systems. Springer: Boston, MA; 1992
  • Thomopoulos NT, Lehman M, The Mixed Model Learning Curve. A I I E Transactions, 1969;1(2):127-132.
  • Argote L, Insko C, Yovetich N, Romero A, Group Learning Curves: The Effects of Turnover and Task Complexity on Group Performance1. Journal of Applied Social Psychology. 1995;25: 512-529.
  • Peltokorpi J, Jaber and MY. A group learning curve model with motor, cognitive and waste elements. Computers & Industrial Engineering. 2020;146:106621.
  • Biskup D. Single-machine scheduling with learning considerations. European Journal of Operational Research. 1999;115(1):173-178.
  • Lee WC. Scheduling with general position-based learning curves. Information Sciences. 2011;181(24):5515-5522.
  • Wang L, Ni H, Yang R, Fei M, Ye W. A Simple Human Learning Optimization Algorithm. 2014;462:56-65.
  • Wang L, Pei J, Menhas MI, Pi J, Fei M, Pardalos P.M. A Hybrid-coded Human Learning Optimization for mixed-variable optimization problems. Knowledge-Based Systems. 2017;127: 114-125.
  • Shoja A, Molla-Alizadeh-Zavardehi S, Niroomand S. Hybrid adaptive simplified human learning optimization algorithms for supply chain network design problem with possibility of direct shipment. Applied Soft Computing. 2020;96:106594.
  • Wei Z, Zhang Y, Xu X, Shi L, Feng L. A task scheduling algorithm based on Q-learning and shared value function for WSNs. Computer Networks. 2017;126: 141-149.
  • Wei Z, Liu F, Zhang Y, Xu J, Ji J, Lyu Z. A Q-learning algorithm for task scheduling based on improved SVM in wireless sensor networks.Computer Networks. 2019;161:138-149.
  • Wene CO, Quantum modelling of the learning curve. Futures. 2018;103: 123-135.
  • Li Y, Yang X, Yang Z. Uncertain learning curve and its application in scheduling. Computers & Industrial Engineering. 2019;131:534-541.
  • Valsamis E, Sukeik M. Evaluating learning and change in orthopaedics: What is the evidence-base? World Journal of Orthopaedics. 2019;10.
  • Zhang L, Liu J, Luo M, Chang X, Zheng Q, Hauptmann AG. Scheduled sampling for one-shot learning via matching network. Pattern Recognition. 2019;96: 106962.
  • Palmanovich E, Ohana N, Atzmon R, Slevin O, Brin Y, Feldman V, Segal D. MICA: A Learning Curve. The Journal of Foot and Ankle Surgery. 2020;59(4):781-783.
  • Kang JN, Wei YM, Liu L, Han R, Chen H, Li J, Wang JW, Yu BY. The Prospects of Carbon Capture and Storage in China’s Power Sector under the 2 °C Target: A Component-based Learning Curve Approach. International Journal of Greenhouse Gas Control. 2020;101: 103149.
  • Zhou P, He X, Luo S, Yu H, Sun G. JPAS: Job-progress-aware flow scheduling for deep learning clusters. Journal of Network and Computer Applications. 2020;158: 102590.
  • Wang H, Wu Y, Min G, Xu J, Tang P. Data-driven dynamic resource scheduling for network slicing: A Deep reinforcement learning approach. Information Sciences, 2019;498: 106-116.
  • Luo S. Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning. Applied Soft Computing. 2020;91:106208.
  • Tong Z, Chen H, Deng X, Li K, Li K. A scheduling scheme in the cloud computing environment using deep Q-learning. Information Sciences. 2020;512:1170-1191.
  • Garg A, Milliman P. The aircraft progress curve modified for design changes. 1961.
  • Nadler G, Smith WD. Manufacturing Progress Functions for Types of Processes. International Journal of Production Research. 1963;2(2):115-135.
  • Hirschmann WB. Profit from the Learning Curve. Profit from the Learning Curve. 1964;2.
  • Keachie EC, Fontana RJ, Effects of Learning on Optimal Lot Size. Management Science. 1966;13(2): B-102-B-108.
  • Knecht GR. Costing, Technological Growth and Generalized Learning Curves. Journal of the Operational Research Society. 1974;25(3):487-491.
  • Montgomery, D. and G. Day, Diagnosing the Experience Curve. Journal of Marketing, 1983. 47.
  • Yelle L. Common Flaws in Learning Curve Analysis. Journal of Purchasing and Materials Management. 1985;21:10-15.
  • Fine CH. Quality Improvement and Learning in Productive Systems. Management Science, 1986;32(10):1301-1315.
  • Reis DA. Learning Curves in Food Services. The Journal of the Operational Research Society. 1991;42(8):623-629.
  • Jordan RB. How to Use the Learning Curve. Materials Management Institute;1965.
  • Baloff N. The Learning Curve--Some Controversial Issues. The Journal of Industrial Economics. 1966;14(3):275-282.
  • Baloff N. Extension of the Learning Curve-Some Empirical Results. Journal of the Operational Research Society. 1971;22(4):329-340.
  • Badiru AB. Quality improvement through learning curve analysis. In: Handbook of Total Quality Management. Springer: Boston, MA;1998.
  • Jaber MY, Glock CH, Zanoni S. A Learning Curve with Improvement in Process Quality. IFAC-PapersOnLine. 2018;51(11):681-685.
  • Kemerer C. How the Learning Curve Affects CASE Tool Adoption. IEEE Software. 1992;9:23-28.
  • Heng TM, Low L. Estimating and comparing learning curves in three Asian economies. Asia Pacific Journal of Management. 1995;12(1):21-35.
  • Klenow P. Learning Curves and the Cyclical Behavior of Manufacturing Industries. Review of Economic Dynamics. 1998;1:531-550.
  • Zangwill W, Kantor P. Toward a Theory of Continuous Improvement and the Learning Curve. Management Science. 1998;4:910-920.
  • Mosheiov G, Sidney JB. Scheduling with general job-dependent learning curves. European Journal of Operational Research. 2003;147(3):665-670.
  • Lee WC, Chuang MC, Yeh WC. Uniform parallel-machine scheduling to minimize makespan with position-based learning curves. Computers & Industrial Engineering. 2012;63(4):813-818.
  • Senyigit E, Atici U. Scheduling with Job Dependent Learning Effect and Ergonomic Risk Deterioration. 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT 2018).
  • Şenyiğit E, Atici U, Şenol MB. Effects of OCRA parameters and learning rate on machine scheduling. Central European Journal of Operations Research, 2020.
  • Li K, Chen J, Fu H, Jia Z, Wu J. Parallel machine scheduling with position-based deterioration and learning effects in an uncertain manufacturing system. Computers & Industrial Engineering. 2020;149:106858.
  • Soleimani H, Ghaderi H, Tsai PW, Zarbakhshnia N, Maleki M. Scheduling of unrelated parallel machines considering sequence-related setup time, start time-dependent deterioration, position-dependent learning and power consumption minimization. Journal of Cleaner Production. 2020;249:119428.
  • Ding H, Gu X. Hybrid of human learning optimization algorithm and particle swarm optimization algorithm with scheduling strategies for the flexible job-shop scheduling problem. Neurocomputing. 2020;14:313-332.
  • Malyusz L. Learning Curve Effect on Project Scheduling. Procedia Engineering. 2016;164:90-97.
  • Mályusz L, Varga A. An Estimation of the Learning Curve Effect on Project Scheduling with Calendar Days Calculation. Procedia Engineering. 2017;196:730-737.
  • Tosselli L, Bogado V, Martínez E. A repeated-negotiation game approach to distributed (re)scheduling of multiple projects using decoupled learning. Simulation Modelling Practice and Theory. 2020;98:101980.
  • Li Z, Ye Y, Wu Z, Wang B. Learning Curve Analysis of Laparoscopic Kasai Portoenterostomy. Journal of Laparoendoscopic & Advanced Surgical Techniques. 2017;27.
  • Feldman L, Cao SJ, Andalib A, Fraser S, Fried GM. A method to characterize the learning curve for performance of a fundamental laparoscopic simulator task: Defining “learning plateau” and “learning rate”. Surgery. 2009;146(2): 381-386.
  • Son K, Lee KB. Prediction of learning curves of 2 dental CAD software programs, part 2: Differences in learning effects by type of dental personnel. The Journal of Prosthetic Dentistry. 2020;123(5):747-752.
  • Alikhanov R. An Invited Commentary on: “Comparison of the learning curves for robotic left and right hemiheparectomy: A prospective cohort study”. International journal of surgery. 2020;81:19-25.
  • Liu Q, Zhang T, Hu M, Zhao Z, Zhao G, Li C, Zhang X, Lau WY, Liu R. Comparison of the learning curves for robotic left and right hemihepatectomy: A prospective cohort study. International Journal of Surgery. 2020;81: 19-25.
  • Ahn Y, Lee S, Son S, Kim H, Kim JE. Learning Curve for Transforaminal Percutaneous Endoscopic Lumbar Discectomy: A Systematic Review. World Neurosurgery. 2020;143:471-479.
  • Zhou D, Ding H, Zhou P, Wang Q. Learning curve with input price for tracking technical change in the energy transition process. Journal of Cleaner Production. 2019;235: 997-1005.
  • Ramirez R, Bhatti Y, Tapinos E. Exploring how experience and learning curves decrease the time invested in scenario planning interventions. Technological Forecasting and Social Change, 2020;151:19785.
  • Ören K, Erol M, Learning Curves, Usage Of Learning Curves in Reducing Workforce Costs As A Tool and An Emprıical Study. Fırat University Journal of Social Science. 2009;19(1):133-141.
  • Moore JR, A Comparative Study of Learning Curve Models in Defense Airframe Cost Estimating, in Air Force Institute of Technology. Department of The Air Force Air University: Wright-Patterson Air Force Base Ohio. 2015;156.
  • Mark G, Rauch E, Matt and DT. Study of the impact of projection-based assistance systems for improving the learning curve in assembly processes. Procedia CIRP, 2020;88:98-103.
  • Hong S, Yang T, Chang HJ, Hong S. The effect of switching renewable energy support systems on grid parity for photovoltaics: Analysis using a learning curve model. Energy Policy. 2020;138:111233.
  • Francis S, Kolil V, Achuthan K. Learning curve analysis for virtual laboratory experimentation. 2016.
  • Abdelkhalek HA, Refaie HS, Aziz RF. Optimization of time and cost through learning curve analysis. Ain Shams Engineering Journal. 2020.
  • Thomas HR, Mathews CT, Ward JG. Learning Curve Models of Construction Productivity. Journal of Construction Engineering and Management. 1986;112(2):245-258.
  • Asher H. Cost-Quantity Relationships in the Airframe industry. The Rand Corporation: Santa Monica; 1956.
  • DeJong JR. The Effects of Increasing Skill on Cycle Time And Its Consequences for Time Standards. Ergonomics. 1957;1(1):51-60.
  • Levy FK. Adaptation in the Production Process. Management Science. 1965;11(6):B136-B154.
  • Glover JH. Manufacturing progress functions I. An alternative model and Its comparison with existing functions. International Journal of Production Research. 1965;4(4):279-300.
  • Yelle LE. Estimating learning curves for potential products. Industrial Marketing Management. 1976;5(2):147-154.
  • Wang L, Ni H, Yang R, Pardalos PM, Du X, Fei M. An adaptive simplified human learning optimization algorithm. Information Sciences. 2015;320:126-139.
  • Wang L, Yang R, Ni H, Ye W, Fei M, Pardalos PM. A human learning optimization algorithm and its application to multi-dimensional knapsack problems. Applied Soft Computing. 2015;34: 736-743.
  • Wang L, Pei J, Wen Y, Pi J, Fei M, Pardalos PM. An improved adaptive human learning algorithm for engineering optimization. Applied Soft Computing; 2018;71:894-904.
  • Ralli P, Panas A, Pantouvakis JP, Karagiannakidis D. Investigation and Comparative Analysis of Learning Curve Models on Construction Productivity: The Case of Caisson Fabrication Process. Journal of Engineering, Project, and Production Management. 2020;10(3):219-230.
There are 81 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Uğur Atıcı 0000-0002-4389-9744

Publication Date June 25, 2021
Published in Issue Year 2021

Cite

APA Atıcı, U. (2021). Öğrenme Eğrilerinin Karşılaştırılması. Türk Doğa Ve Fen Dergisi, 10(1), 119-129. https://doi.org/10.46810/tdfd.833452
AMA Atıcı U. Öğrenme Eğrilerinin Karşılaştırılması. TDFD. June 2021;10(1):119-129. doi:10.46810/tdfd.833452
Chicago Atıcı, Uğur. “Öğrenme Eğrilerinin Karşılaştırılması”. Türk Doğa Ve Fen Dergisi 10, no. 1 (June 2021): 119-29. https://doi.org/10.46810/tdfd.833452.
EndNote Atıcı U (June 1, 2021) Öğrenme Eğrilerinin Karşılaştırılması. Türk Doğa ve Fen Dergisi 10 1 119–129.
IEEE U. Atıcı, “Öğrenme Eğrilerinin Karşılaştırılması”, TDFD, vol. 10, no. 1, pp. 119–129, 2021, doi: 10.46810/tdfd.833452.
ISNAD Atıcı, Uğur. “Öğrenme Eğrilerinin Karşılaştırılması”. Türk Doğa ve Fen Dergisi 10/1 (June 2021), 119-129. https://doi.org/10.46810/tdfd.833452.
JAMA Atıcı U. Öğrenme Eğrilerinin Karşılaştırılması. TDFD. 2021;10:119–129.
MLA Atıcı, Uğur. “Öğrenme Eğrilerinin Karşılaştırılması”. Türk Doğa Ve Fen Dergisi, vol. 10, no. 1, 2021, pp. 119-2, doi:10.46810/tdfd.833452.
Vancouver Atıcı U. Öğrenme Eğrilerinin Karşılaştırılması. TDFD. 2021;10(1):119-2.