||Applications of Deep Learning in Supply Chain Management: A Systematic Literature Review Approach
||Department of Transportation & Communication Management Science
Artificial Intelligence (AI)
Machine Learning (ML)
Deep Learning (DL)
Supply Chain Management (SCM)
Systematic Literature Review (SLR)
深度學習（DL）被廣泛應用於圖像識別，醫療診斷，自動駕駛系統及影音設計等多元領域，其源自商業行銷所包裝的深度（多層）神經網路，依據學術資料庫（Web of Science）的統計數據，深度學習在過去的兩年中盛行於供應鏈管理（SCM）之領域，應用深度學習之文獻量大幅增加。實際上，多數應用相似概念之文獻因未使用「深度學習」一詞而受到忽視，本項研究欲通過系統性文獻回顧法（SLR）有系統地辨識與篩選出具深度學習理念的文獻。有別以往供應鏈管理之回顧文獻，本研究將供應鏈的每一個階段視為一個獨立任務並歸類為微觀流程，再利用宏觀流程建構全景圖。藉由清楚地定義微觀和宏觀流程，本研究得以定位深度學習的核心與相應應用，提供企業一個適當應用深度學習來開發及改善其供應鏈之指導方針。最後，深度學習的發展趨勢顯示了眾多研究具有利用多層神經網絡來解決供應鏈管理議題的潛力。
Deep learning (DL) has been widely used in the fields of image recognition, medical diagnosis, self-driving systems, audio-visual design and so forth. The provenance of DL is derived from deep (multi-layer) neural network packaged by commercial marketing. Pursuant to the statistics from a prestigious database (Web of Science), it has prevailed over the past two years in the field of supply chain management (SCM). The amount of literature applying DL has increased significantly. In fact, some literature applying similar concepts has been neglected since they did not use the term "deep learning" as a topic or in content. This research aims to systematically identify and screen out documents possessing the concepts of DL by applying systematic literature review (SLR). Discrepant from those reviews in SCM, this research considers each stage of the supply chain as an independent task classified as micro processes, and utilised macro processes to complete panorama. Through clearly defining micro and macro processes, this research locates major and corresponding applications of DL for each task. The endeavour constructs guidance for enterprises to develop and improve their supply chain by appropriately applying DL. Eventually, the tendency of DL presents that a multitude of research has the potential of utilising multilayer neural network to cope with issues in SCM.
LIST OF TABLES vii
LIST OF FIGURES ix
CHAPTER 1 Introduction 1
1.1 Motivation and Research Background 1
1.2 Research Preparation 2
1.3 Research Structure 2
CHAPTER 2 Review of AI and Applications 6
2.1 Machine Learning (ML) 7
2.1.1 Definition of ML 8
2.1.2 ML in SCM 13
2.2 Artificial Neural Networks (ANNs) 15
2.2.1 Definition of ANNs 17
2.2.2 ANNs in SCM 19
2.3 Deep Learning (DL) 20
2.3.1 Definition of DL 20
2.3.2 DL in SCM 21
2.4 Evolution Timeline 22
2.5 Summary 23
CHAPTER 3 Research Methodology 24
3.1 Terminology 25
3.1.1 Literature Review 25
3.1.2 Topic 25
3.1.3 Neural Networks 25
3.2 SLR Structure 26
3.2.1 Evidence-Based Practice 28
3.2.2 About PICO 30
3.3 Examination and Adjustment 31
3.3.1 Question Checking 31
3.3.2 Bias Handling 31
3.4 Develop Review Protocol 33
CHAPTER 4 Conduct SLR for DL in SCM 36
4.1 Formulate Review Question 36
4.2 Document 36
4.3 Locate Resources 37
4.3.1 Supply Chain Macro Processes 43
4.3.2 Supply Chain Micro Processes 45
4.3.3 Distribution of SC Macro & Micro Processes 58
4.4 Exclude Improper Resources 58
4.4.1 Supply Chain Macro Processes 59
4.4.2 Supply Chain Micro Processes 61
4.4.3 Distribution of Inclusive Data 68
4.5 Make Appraisal for Adoptable Sources 68
4.6 Data Extraction 70
4.6.1 Supply Chain Macro Processes 70
4.6.2 Supply Chain Micro Processes 71
CHAPTER 5 Analysis and Output 76
5.1 Synthesis 76
5.1.1 Supply Chain Macro Processes 76
5.1.2 Supply Chain Micro Processes 77
5.2 Results and Tendency 82
CHAPTER 6 Conclusions and Suggestions 85
APPENDIX Ⅰ - PROTOCOL 104
APPENDIX Ⅱ - PICO Worksheet and Search Strategy 105
APPENDIX Ⅲ- Author relationship 106
1. Abbasi, B., Babaei, T., Hosseinifard, Z., Smith-Miles, K. and Dehghani, M. (2020), “Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management,” Computers & Operations Research, Vol. 119.
2. Agre, G., Thierry D., and van Genabith, J. (2018), “Artificial Intelligence. Methodology, Systems, and Applications: 18th International Conference, AIMSA 2018, Varna, Bulgaria, September 12-14, 2018 : proceedings," Lecture notes in artificial intelligence,” Springer International Publishing.
3. Aksoy, A. and Ozturk, N. (2011), “Supplier selection and performance evaluation in just-in-time production environments,” Expert Systems with Applications, Vol. 38, Iss. 5, pp. 6351-6359.
4. Alonso, S., Cabrerizo, F.J. Herrera-Viedma, E., Herrera, F. (2009), “H-Index: A Review Focused in Its Variants, Computation and Standardization for Different Scientific Fields.” Journal of Informetrics, Vol. 3, No. 4, pp. 273–289.
5. Alpaydin, E. (2010), Introduction to Machine Learning. 2nd ed. MIT Press, pp. 1-3.
6. Ambrose, G. and Harris, P. (2011), Packaging the brand : the relationship between packaging design and brand identity. AVA Academia.
7. Angra, S. and Ahuja, S. (2017), “Machine learning and its applications: A review,” 2017 International Conference on Big Data Analytics and Computational Intelligence.
8. Ansari, A. and Riasi, A. (2016), “Modelling and evaluating customer loyalty using neural networks: Evidence from startup insurance companies,” Future Business Journal, Vol. 2, Iss. 1, pp. 15-30.
9. Arrowsmith, S., and Hartley, K. (2002), Public Procurement, The International Library of Critical Writings in Economics.
10. Baalousha, Y. and Celik, T. (2011), “integrated web-based data warehouse and artificial neural networks system for unit price analysis with inflation adjustment,” Journal of Civil Engineering and Management, Vol.17, Iss. 2, pp. 157-167.
11. Bahrami, A., Lynch, M. and Dagli, C.H. (1995), “Intelligent design retrieval and packaging system - application of neural networks in design and manufacturing,” International Journal of Production Research, Vol. 33, Iss. 2, pp. 405-426.
12. Baryannis, G., Validi, S., Dani, S. and Antoniou, G. (2018), “Supply chain risk management and artificial intelligence: state-of-the-art and future research directions,” International Journal of Production Research, Vol. 57, No. 7, pp. 2179–2202.
13. Battaglia, A. J. (1994), “Beyond Logistics: Supply Chain Management,” Chief Executive (U.S.), Iss. 99, pp. 48-50.
14. Bendana, R., del Cano, A. and de la Cruz, M.P. (2008), “Contractor selection: fuzzy-control approach,” Canadian Journal of Civil Engineering, Vol. 35, Iss. 5, pp. 473-486.
15. Benmahdi, D., Rasolofondraibe, L., Chiementin, X., Murer, S. and Felkaoui, A. (2019), “RT-OPTICS: real-time classification based on OPTICS method to monitor bearings faults,” Journal of Intelligent Manufacturing, Vol. 30, Iss. 5, pp. 2157-2170.
16. Bennett, C.J., Stewart, R.A. and Lu, J.W. (2014), “Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system,” Energy, Vol. 67, pp. 200-212.
17. Berson, A and Smith S.J. (1997), Data Warehousing, Data Mining, and OLAP, McGraw-Hill.
18. Bottani, E., Centobelli, P., Gallo, M., Kaviani, M. A., Jain, V., and Murino, T.,(2019), “Modelling Wholesale Distribution Operations: An Artificial Intelligence Framework,” Industrial Management & Data Systems, Vol. 119, No. 4, pp. 698-718.
19. Bratko, I. (1993), “Application of Machine Learning: Towards Knowledge Synthesis,” New Generation Computing, Vol. 11, pp.343-360.
20. Breuer, J.B., Kumar, V. and Suresh, S.G. (2015), “Inter-Temporal Purchasing Power Parity,” Open Economies Review, Vol. 26, Iss. 5, pp. 869-891.
21. Briggs, A.J. (1960), Warehouse operations planning and management, New York : Wiley.
22. Buyukozkan, G., Ertay, T., Kahraman, C. and Ruan, D. (2004), “Determining the Importance Weights for the Design Requirements in the House of Quality Using the Fuzzy Analytic Network Approach,” International Journal of Intelligent Systems, Vol. 19, No. 5, pp. 443–461.
23. Cavallo, D.P., Cefola, M., Pace, B., Logrieco, A.F. and Attolico, G. (2018), “Non-destructive automatic quality evaluation of fresh-cut iceberg lettuce through packaging material,” Journal of Food Engineering, Vol. 223, pp. 46-52.
24. Celikoglu, H.B. (2006), “Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modelling,” Mathematical and Computer Modelling, Vol.44, Iss. 7-8, pp. 640-658.
25. Chadegani, A.A., Salehi, H., Yunus, M.M., Farhadi, H., Fooladi, M., Farhadi, M. and Ebrahim, N.A. (2013), “A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases,” Asian Social Science, Vol. 9, No. 5, pp. 18-26.
26. Chaloulakou, A., Grivas, G. and Spyrellis, N. (2003), “Neural network and multiple regression models for PM10 prediction in Athens: A comparative assessment,” Journal of The Air & Waste Management Association, Vol. 53, Iss. 10, pp. 1183-1190.
27. Chiappelli, F. (2010). Evidence-based practice: toward optimizing clinical outcomes. Springer-Verlag Berlin Heidelberg.
28. Chopra, S. and Meindl, P. (2007), Supply chain management: strategy, planning, and operation. 3rd ed, Pearson Prentice Hall, pp. 15-16.
29. Chou S.F., Horng J.S., Sam Liu C.H. and Lin J.Y. (2020), “Identifying the critical factors of customer behavior: An integration perspective of marketing strategy and components of attitudes,” Journal of Retailing and Consumer Services, Vol. 55.
30. Choy, K.L., Lee, W.B. and Lo, V. (2002), “An intelligent supplier management tool for benchmarking suppliers in outsource manufacturing,” Expert Systems with Applications, Vol. 22, Iss. 3, pp. 213-224.
31. Choy, K.L., Lee, W.B. and Lo, V. (2002), “Design of an intelligent supplier relationship management system: a hybrid case based neural network approach,” Expert Systems with Applications, Vol. 24, Iss. 2, pp. 225-237.
32. Christenbery, T.L. (2017), Evidence-based practice in nursing: foundations, skills, and roles. Springer Publishing Company. pp. 7-9.
33. Cochrane, A. L. (1999), Effectiveness and efficiency: Random reflections on health services, London: Royal Society of Medicine Press Ltd.
34. Corbato, C.H., Bharatheesha, M. Van Egmond, J. Ju, J. and Wisse, M. (2018), “Integrating Different Levels of Automation: Lessons From Winning the Amazon Robotics Challenge 2016,” IEEE Transactions on Industrial Informatics, Vol. 14, Iss. 11, pp. 4916-4926.
35. Dalton, J. and Deshmane, A. (1991), “Artificial Neural Networks,” IEEE Potentials, Vol. 10, Iss. 2, pp. 33-36.
36. David, P.A. and Steward, R.D. (2010), International Logistics: the management of international trade operations – 3rd edition, Cicero Books, pp. 28-30.
37. Davies, H. T. O., Nutley, S. M., & Smith, P. C. (1999), “Viewpoint: Editorial: What works? The role of evidence in public sector policy and practice,” Public Money & Management, Vol. 19, Issues 1, pp. 3–5.
38. Dechter, R. (1986), “Learning while searching in constraint-satisfaction problems,” National Conference on Artificial Intelligence. pp. 178-183.
39. Dimitri, N., Piga, G. and Spagnolo, G. (2006), Handbook of procurement, Cambridge University Press. pp. 4-5.
40. Dreyfus, G. (2005), Neural Networks : Methodology and Applications, Springer-Verlag Berlin Heidelberg.
41. Duda, R.O. and Hart, P.E. (1973), Pattern Classification and Scene Analysis, Wiley, New York.
42. Durach, C. F., Kembro, J & Wieland, A. (2017), “A New Paradigm for Systematic Literature Reviews in Supply Chain Management,” Journal of Supply Chain Management, Vol. 53, No. 4, pp. 67–85.
43. Emblem, A. and Emblem, H. (2012), Packaging technology : fundamentals, materials and processes, Woodhead Pub.
44. Fong, A.C.M. and Hui, S.C. (2001), “An intelligent online machine fault diagnosis system,” IEEE, Vol. 12, Iss. 5, pp. 217-223.
45. Fong, C.M., Wang, H.W., Kuo, C.H. and Hsieh, P.C. (2019), “Image quality assessment for advertising applications based on neural network,” Journal of Visual Communication and Image Representation, Vol. 63.
46. Garcia-Flores, R., Wang, X. Z., Goltz, G. E. (2000), “Agent-based Information Flow for Process Industries' Supply Chain Modelling,” Computers & Chemical Engineering, Vol. 24, No. 2, pp. 1135–1141.
47. Gergin, R.E. and Peker, I. (2019), “Literature review on success factors and methods used in warehouse location selection,” Pamukkale University Journal of Engineering Sciences, Vol. 25, No. 9, pp. 1062–1070.
48. Ghiassi, M., Zimbra, D.K. and Saidane, H. (2006), “Medium term system load forecasting with a dynamic artificial neural network model,” Electric Power Systems Research, Vol. 76, Iss. 5, pp. 302-316.
49. Ghiassi, M., Zimbra, D.K. and Saidane, H. (2008), “Urban water demand forecasting with a dynamic artificial neural network model,” Journal of Water Resources Planning and Management-Asce, Vol. 134, Iss. 2, pp. 138-146.
50. Giannoccaro, I. and Pontrandolfo, P. (2002), “Inventory Management in Supply Chains: A Reinforcement Learning Approach,” International Journal of Production Economics, Vol. 78, Issue 2, pp. 153-161.
51. Giefer, L.A., Castellanos, J.D.A., Babr, M.M. and Freitag, M. (2019), “Deep Learning-Based Pose Estimation of Apples for Inspection in Logistic Centers Using Single-Perspective Imaging,” PROCESSES, Vol. 7, Iss. 7.
52. Gough, D., et al. (2012), An Introduction to Systematic Reviews, Sage, London.
53. Guan, F., Peng, Z., Wang, K., Song, X. and Gao, J. (2016), “Multi-Step Hybrid Prediction Model of Baltic Supermax Index Based on Support Vector Machine,” Neural Network World, Vol. 26, Iss. 3, pp 21-232.
54. Gundlach, G.T. and Wilkie, W.L. (2009), “The American Marketing Association's New Definition of Marketing: Perspective and Commentary on the 2007 Revision,” Journal of Public Policy & Marketing, Vol. 28, Iss. 2, pp. 259-264.
55. Hall, H. R. and Roussel, L. (2014), Evidence-based practice: an integrative approach to research, administration, and practice. Jones & Bartlett Learning.
56. Hasenauer, H., Merkl, D. and Weingartner, M. (2001), “Estimating tree mortality of Norway spruce stands with neural networks,” Advances in Environmental Research, Vol. 5, Iss. 4, pp. 405-414.
57. Hershey, P. (2012), Machine learning: concepts, methodologies, tools and applications, IGI Global.
58. Hochreiter, S., Bengio, Y., Frasconi, P. and Schmidhuber, J. (2001), Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, IEEE press.
59. Hochreiter, S. and Jurgen S. (1997), “Long Short-Term Memory,” Neural Computation, Vol. 9, No. 8, pp.1735-1780.
60. Homburg, C., Jensen, O. and Krohmer, H. (2008), “Configurations of marketing and sales: A taxonomy,” Journal of Marketing, Vol. 72, No. 2, pp. 133-154.
61. Howe, J. (2006), “The Rise of Crowdsourcing,” retrieved June 10, 2020, Website: https://www.wired.com/2006/06/crowds/.
62. Hurwitz, J and Kirsch, D. (2018), Machine Learning for dummies, John Wiley & Sons, Inc, pp. 4.
63. Icarte, G. (2016), “Applications of artificial intelligence in supply chain process: a systematic review,” Revista chilena de ingeniería, Vol. 24, No. 4, pp. 663–679.
64. Jesson, J. K., Matheson, L. and Lacey, F. M. (2011), Doing Your Literature Review, Sage, London.
65. Jiang, P., Ishihara, Y., Sugiyama, N., Oaki, J., Tokura, S., Sugahara, A. And Ogawa, A. (2020), “Depth Image-Based Deep Learning of Grasp Planning for Textureless Planar-Faced Objects in Vision-Guided Robotic Bin-Picking,” SENSORS, Vol. 20, Iss. 3.
66. Jiang, Y., Yang, C., Na, J., Li, G. Li, Y. and Zhong, J. (2019), “A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots,” Complexity Hindawi, Vol. 2017, pp. 14.
67. Kamaruddin, N., Wahab, A. and Quek, C. (2012), “Cultural dependency analysis for understanding speech emotion,” Expert Systems with Applications, Vol. 39, Iss. 5, pp. 5115-5133.
68. Kanimozhiselvi, C.S. and Pratapy, A. (2016), “Possibilistic LVQ Neural Network - An Application to Childhood Autism Grading,” Neural Network World, Vol. 26, Iss. 3, pp. 253-269.
69. Kar, A.K. (2014), “Revisiting the supplier selection problem: An integrated approach for group decision support,” Expert Systems with Applications, Vol. 41, Iss. 6, pp. 2762-2771.
70. Kawakami, W., Kanai, K., Wei, B. and Katto, J. (2019), “A Highly Accurate Transportation Mode Recognition Using Mobile Communication Quality,” IEICE Transactions on Communications, Vol. E102B, Issue: 4, pp. 741-750.
71. Keith, B., Kling, J., Manrodt, K. and Vitasek, K. (2016), Strategic sourcing in the new economy : harnessing the potential of sourcing business models for modern procurement, Palgrave Macmillan US pp. 7.
72. Kirchner, J., Heberte, A. and Löwe, W. (2015), “Classification vs. Regression - Machine Learning Approaches for Service Recommendation Based on Measured Consumer Experiences,” 2015 IEEE World Congress on Services. pp. 278-285.
73. Kuo, R.J., Hung, S.Y. and Cheng, W.C. (2014), “Application of an optimization artificial immune network and particle swarm optimization-based fuzzy neural network to an RFID-based positioning system,” Information Sciences, Vol. 262, pp. 78-98.
74. Kuo, R.J., Tseng, W.L., Tien, F.C. and Liao, T.W. (2012), “Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system,” Computers & Industrial Engineering, Vol. 63, Iss. 4, pp. 943-956.
75. Ladyzynski, P., Zbikowski, K. and Gawrysiak, P. (2019), “Direct marketing campaigns in retail banking with the use of deep learning and random forests,” Expert Systems with Applications, Vol. 134, pp. 28-35.
76. Lai, W.H., Zhou, M.R., Li, D.T., Wang, Y., Hu, F., Zhao, S. and Gu, Y.L. (2019), “Application of Unsupervised Learning AE and MVO-DBSCAN Combined with LIF in Mine Water Inrush Recognition,” Spectroscopy and Spectral Analysis, Vol. 39, Iss. 8, pp. 2439-2442.
77. Leung, M. and Mao, J.Y. (2004), “Proactive task support enabled by a neural network: A prototype for telephone triage,” International Journal of Human-Computer Interaction, Vol. 17, Iss. 3, pp. 309-332.
78. Li, R., Wang, X.D., Lai, J., Song, Y.F. and Lei, L. (2020), “Discriminative Auto-Encoder With Local and Global Graph Embedding,” IEEE Access, Vol. 8, pp. 28614-28623.
79. Li, S.G. and Kuo, X. (2008), “The inventory management system for automobile spare parts in a central warehouse,” Expert Systems with Applications, Vol. 34, Iss. 2, pp. 1144-1153.
80. Liu, C.X., Shu, T., Chen, S., Wang, S.Y., Lai, K.K. and Gan, L. (2016), “An improved grey neural network model for predicting transportation disruptions,” Expert Systems with Applications, Vol. 45, pp. 331-340.
81. Lecun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998), “Gradient-Based Learning Applied to Document Recognition,” Proceedings of the IEEE, Proc. IEEE, No. 11, pp. 2278.
82. Li, X.P., Iynkaran, K. and Nee, A.Y.C. (1999), “A hybrid machining simulator based on predictive machining theory and neural network modelling,” Journal of Materials Processing Technology, Vol. 90, Iss. SI, pp. 224-230.
83. Liang, Y.C., Yang, X.W., Zhon, C. and Wang, Z.S. (1996), “Application of neural networks to identification of nonlinear characteristics in cushioning packaging,” Mechanics Research Communications, Vol. 23, Iss. 6, pp. 607-613.
84. Liau, L.C.K., Yang, T.C.K., Huang, S.P. and Chang, C.C. (2003), “The search of optimal operation parameters of a belt furnace for microelectronic packaging process using artificial neural networks,” Journal of the Chinese Institute of Chemical Engineers, Vol. 34, Iss. 2, pp. 219-227.
85. Liu, X.Y., Zhao, Y.Z. and Sun, M.H. (2017), “An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P System with Promoters and Inhibitors,” Discrete Dynamics in Nature and Society, Vol. 2017.
86. Lopes, A.T., De Aguiar, E., De Souza, A.F. and Oliveira-Santos, T. (2017), “Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order,” Pattern Recognition, Vol.: 61, pp. 610-628.
87. Ma, C., Hao, W., Pan, F. and Xiang, W. (2018), “Road screening and distribution route multi objective robust optimization for hazardous materials based on neural network and genetic algorithm,” PLOS ONE, Vol. 13, Iss. 6.
88. Mahdavi, I., Shirazi, B., Solimanpur, M. and Sahebjamnia, N. (2009), “Lot size approximation based on minimising total delay in a shop with multi-assembly products,” International Journal of Production Research, Vol. 47, Iss. 10, pp. 2685-2703.
89. Management Association, Information Reso. (2017), Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, IGI Global.
90. Martinez-Garcia, E.A., Torres-Cordoba, R., Carrillo-Saucedo, V.M. and Lopez-Gonzalez, E. (2018), “Neural control and coordination of decentralized transportation robots,” Proceedings of the Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering, Vol. 232, Iss. 5, pp. 519-540.
91. Mazid, M.M., Shawkat Ali, A.B.M. and Tickle, K.S. (2009), “A Comparison Between Rule Based and Association Rule Mining Algorithms,” 2009 Third International Conference on Network and System Security. pp. 452-455.
92. McCulloch, W. S., and Pitts, W. (1990), “A Logical Calculus of the Ideas Immanent in Nervous Activity. 1943,” Bulletin of Mathematical Biology, Vol. 52, No. 1/2, pp. 99-115.
93. Mello, R.F. de. (2018), Machine Learning: A Practical Approach on the Statistical Learning Theory, Springer.
94. Min, H. (2010), “Artificial intelligence in supply chain management: Theory and application,” International Journal of Logistics, Vol. 13, No. 1, pp. 13–39.
95. Miralles-Pechuan, L., Rosso, D., Jimenez, F. and Garcia, J.M. (2017), “A methodology based on Deep Learning for advert value calculation in CPM, CPC and CPA networks,” Soft Computing, Vol. 21, Iss. 3, pp. 651-665.
96. Mirjalili, S. (2019), Evolutionary algorithms and neural networks : theory and application, Springer International Publishing.
97. Mishra, M. and Srivastava, M. (2014), “A view of Artificial Neural Network,” 2014 International Conference on Advances in Engineering & Technology Research,
98. Moein, S. (2017), Medical Diagnosis Using Artificial Neural Networks, IGI Global.
99. Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L. A. and PRISMA-P Group. (2015), “Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement,” Revista Española de Nutrición Humana y Dietética, Vol. 20, No. 2, pp. 148.
100. Monirzadeh, Z., Habibzadeh, M. and Farajian, N. (2018), “Detection of Violations in Credit Cards of Banks and Financial Institutions based on Artificial Neural Network and Metaheuristic Optimization Algorithm,” International Journal of Advanced Computer Science and Applications, Vol. 9, Iss. 1, pp. 176-182.
101. Montoya, O.D., Grisales-Norena, L.F., Gil-Gonzalez, W., Alcala, G. and Hernandez-Escobedo, Q. (2020), “Optimal Location and Sizing of PV Sources in DC Networks for Minimizing Greenhouse Emissions in Diesel Generators,” Symmetry-Basel, Vol. 12, Iss. 2.
102. Moosmayer, D.C., Chong, A.Y.L., Liu, M.J. and Schuppar, B. (2013), “A neural network approach to predicting price negotiation outcomes in business-to-business contexts,” Expert Systems with Applications, Vol. 40, Iss. 8, pp. 3028-3035.
103. Nandy, A. (2018), Reinforcement learning: with Open AI, TensorFlow and Keras using Python, Abhishek Nandy and Manisha Biswas.
104. Newhart, D. D., Stott, K. l., and Vasko, F. J. (1993), “Consolidating Product Sizes to Minimize Inventory Levels for a Multistage Production and Distribution-System,” Journal of the Operational Research Society, Vol. 44, No. 7, pp. 637–644.
105. Ni, D., Xiao, Z. and Lim, M.K. (2019), “A systematic review of the research trends of machine learning in supply chain management,” International Journal of Machine Learning and Cybernetics.
106. Oztekin, A. (2018), “Creating a marketing strategy in healthcare industry: a holistic data analytic approach,” Annals of Operations Research, Vol. 270, Iss. 1-2, pp. 361-382.
107. Ozdemir, S., Demirtas, M. and Aydin, S. (2016), “Harmonic Estimation Based Support Vector Machine for Typical Power Systems,” Neural Network World, Vol. 26, Iss. 3, pp. 233-252.
108. Palaima, M.M. (2010), Evidence based practice: Clinical experiences of recent doctor of physical therapy graduates, Dissertation Abstracts International, pp. 1.
109. Pan, F.B. (2016), “Inventory Prediction Research Based on the Improved BP Neural Network Algorithm,” International Journal of Grid and Distributed Computing, Vol.9, Iss. 9, pp. 307-316.
110. Pise, N.N. and Kulkarni, P. (2008), “A Survey of Semi-Supervised Learning Methods,” 2008 International Conference on Computational Intelligence and Security.
111. Pooya, A. and Pakdaman, M. (2018), “A delayed optimal control model for multi-stage production-inventory system with production lead times,” International Journal of Advanced Manufacturing Technology, Vol. 94, Iss. 1-4, pp. 751-761.
112. Prestwich, S.D., Tarim, S.A., Rossi, R. and Hnich, B. (2012), “A neuroevolutionary approach to stochastic inventory control in multi-echelon systems,” International Journal of Production Research, Vol. 50, Iss. 8, pp. 2150-2160.
113. Qiang, W. and Zhongli, Z. (2011), “Reinforcement learning model, algorithms and its application,” 2011 International Conference on Mechatronic Science, Electric Engineering and Computer.
114. Qin, Y., Ding, S.F., Wang, L.J. and Wang, Y.R. (2019), “Research Progress on Semi-Supervised Clustering,” Cognitive Computation, Vol. 11, Iss. 5, pp. 599-612.
115. Ravulapati, K.K., Rao, J. and Das, T.K. (2004), “A reinforcement learning approach to stochastic business games,” IIE Transactions, Vol. 36, Iss. 4, pp. 373-385.
116. Reyes-Aldasoro, C.C., Ganguly, A.R. Lemus, G. and Gupta, A. (1999), “A hybrid model based on dynamic programming, neural networks, and surrogate value for inventory optimisation applications,” Journal of The Operational Research Society, Vol. 50, Iss. 1. pp. 85-94.
117. Richards, G. (2014), Warehouse management : a complete guide to improving efficiency and minimizing costs in the modern warehouse, Kogan Page Ltd.
118. Rouziès, D., Anderson, E., Kohli, A.K., Michaels, R.E., Weitz, B.A. and Zoltners, A.A. (2005), “Sales and Marketing Integration: A Proposed Framework,” The Journal of Personal Selling and Sales Management, Vol. 25, No. 2, pp. 113-122.
119. Sabegh, M.H.Z., Mohammadi, M. and Naderi, B. (2017), “Multi-objective optimization considering quality concepts in a green healthcare supply chain for natural disaster response: neural network approaches,” International Journal of System Assurance Engineering and Management, Vol. 8, Iss. 2, pp. 1689-1703.
120. Sackett, D.L., Sackett D.L., Strauss, S.E., Richardson, W.S., Rosenberg, W. and Haynes, R.B. (2000), Evidence-based medicine : how to practice and teach EBM – 2nd edition. Churchill Livingstone.
121. Sahinbaskan, T. (2018), “Experimental and theoretical study of the color changes in OPP-based printing substrate on the gravure printing,” Neural Computing & Applications, Vol. 30, Iss. 4, pp. 1203-1210.
122. Samarasinghe, S., and Subana S. (2016), Artificial Neural Network Modelling, Springer International Publishing.
123. Samuel, A.L. (1959), “Some Studies in Machine Learning Using the Game of Checkers,” IBM Journal of Research and Development, Vol. 3, Iss. 3, pp. 210-229.
124. Sapkal, S.D., Kakarwal, S.N. and Revankar, P.S. (2007), “Analysis of Classification by Supervised and Unsupervised Learning,” International Conference on Computational Intelligence and Multimedia Applications.
125. Saviozzi, M., Massucco, S. and Silvestro, F. (2019), “Implementation of advanced functionalities for Distribution Management Systems: Load forecasting and modeling through Artificial Neural Networks ensembles,” Electric Power Systems Research, Vol. 167, pp. 230-239.
126. Schultz, E.B., Matney, T.G. and Koger, J.L. (1999), “A neural network model for wood chip thickness distributions,” Wood and Fiber Science, Vol. 31, Iss. 1, pp. 2-14.
127. Shao, J., Huang, C., Tang, Q. and Luo, G. (2016), “Reliable Semi-supervised Learning,” 2016 IEEE 16th International Conference on Data Mining.
128. Simunovic, K., Simunovic, G. and Saric, T. (2009), “Application of Artificial Neural Networks to Multiple Criteria Inventory Classification,” Strojarstvo, Vol. 51, Iss. 4, pp. 313-321.
129. Shin, H.W. and Sohn, S.Y. (2004), “Multi-attribute scoring method for mobile telecommunication subscribers,” Expert Systems with Applications, Vol. 26, Iss. 3, pp. 363-368.
130. Singh, A., Thakur, N. and Sharma, N. (2016), A review of supervised machine learning algorithms, 2016 3rd International Conference on Computing for Sustainable Global Development.
131. Skansi, S. (2018), Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence, Springer International Publishing.
132. Smykay, E.W. (1961), Physical distribution management, Macmillan.
133. Su, C.T. and Chiang, T.L. (2002), “Optimal design for a ball grid array wire bonding process using a neuro-genetic approach,” Ieee Transactions on Electronics Packaging Manufacturing, Vol. 25, Iss. 1, pp. 13-18.
134. Szathmary, L. (2018), “Finding frequent closed itemsets with an extended version of the Eclat algorithm,” Annales Mathematicae et Informaticae, Vol. 48, pp. 75-82.
135. Ten Hompel, M. (2007), Warehouse Management: Automation and Organisation of Warehouse and Order Picking Systems, Springer-Verlag Berlin Heidelberg.
136. Thomopoulos, N.T. (2017), Statistical distributions : applications and parameter estimates. Springer International Publishing.
137. Trappey, A.J.C., Trappey, C.V., Chiang, T.A. and Huang, Y.H. (2013), “Ontology-based neural network for patent knowledge management in design collaboration,” International Journal of Production Research, Vol. 51, Iss. 7, pp. 1992-2005.
138. Wenzel, H., Smit, D. and Sardesai, S. (2019), “A literature review on machine learning in supply chain management,” Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 27, pp. 413-441.
139. Wibowo, S. and Deng, H.P. (2012), “A fuzzy rule-based approach for screening international distribution centres,” Computers & Mathematics with Applications, Vol. 64, Iss. 5, pp. 1084-1092.
140. Wittek, P. (2014), Quantum Machine Learning: What Quantum Computing Means to Data Mining, Elsevier Inc, pp. 57-62.
141. Wlodarczyk, M. and Dolinska-Zygmunt, G. (2019), “Searching for predictors of sense of quality of health: A study using neural networks on a sample of perimenopausal women,” PLOS ONE, Vol. 14, Iss. 1.
142. Wu, D.S. (2009), “Supplier selection: A hybrid model using DEA, decision tree and neural network,” Expert Systems with Applications, Vol. 36, Iss. 5, pp. 9105-9112.
143. Xiao, Y. and Watson, M. (2019), “Guidance on Conducting a Systematic Review,” Journal of Planning Education and Research, Vol. 39, Iss. 1, pp. 93-112.
144. Xie, Z.Y., Jia, L.M., Qin, Y. and Wang, L. (2013), “A Hybrid Temporal-Spatio Forecasting Approach for Passenger Flow Status in Chinese High-Speed Railway Transport Hub,” Discrete Dynamics in Nature and Society, No. 239039.
145. Xiang, J., Zhang, N., Pan, R.R. and Gao, W.D. (2019), “Fabric Image Retrieval System Using Hierarchical Search Based on Deep Convolutional Neural Network,” IEEE ACCESS, Vol. 7, pp. 35405-35417.
146. Yang, M., Chen, C., Wang, L., Yan, X.X. and Zhou L.P. (2016), “Bus Arrival Time Prediction Based on the GA-SVM Model,” Neural Network World. Vol. 26, Iss. 3, pp. 205-217.
147. Yeo, A.C., Smith, K.A., Willis, R.J. and Brooks, M. (2001), “Modelling the effect of premium changes on motor insurance customer retention rates using neural networks,” Computational Science -- Iccs 200, Proceedings Pt 2, Vol. 2074, pp. 390-399.
148. Yolcu, G., Oztel, I., Kazan, S., Oz, C. and Bunyak, F. (2020), “Deep learning-based face analysis system for monitoring customer interest,” Journal of Ambient Intelligence and Humanized Computing, Vol. 11, Iss. 1, pp. 237-248.
149. Yoo, J.S., Hong, S.R. and Kim, C.O., (2009), “Service level management of nonstationary supply chain using direct neural network controller,” Expert Systems with Applications, Vol. 36, Iss. 2, pp. 3574-3586.
150. Yu, B., Wang, Y.T., Yao, J.B. and Wang, J.Y. (2016), “A comparison of the performance of ANN and SVM for the prediction of traffic accident duration,” Neural Network World, Vol. 26, Iss. 3, pp. 271-287.
151. Yu, B. and Zhang, Y. (2016), “Machine Learning and Its Applications,” Neural Network World, Vol. 26, Iss. 3, pp. 203-204.
152. Zeng, Y., Yin, S.Q., Liu, J.Y. and Zhang, M. (2015), “Research of Improved FP-Growth Algorithm in Association Rules Mining,” Scientific Programming, Vol. 2015.
153. Zhou, L.C. (2020), “Product advertising recommendation in e-commerce based on deep learning and distributed expression,” Electronic Commerce Research, Vol. 20, Iss. 2, pp. 321-342.
154. Zhou, M. and Paik, J. (2000), “Damage prediction using neural networks,” International Journal of Industrial Engineering-Theory Applications and Practic E, Vol. 7, Iss. 2, pp. 140-146.
155. The University of Notre Dame Australia, (2018), Evidence-Based Practice, Retrieved January 05, 2020, website: https://library.nd.edu.au/c.php?g=419688&p=2862743#s-lg-box-8776034.
156. OpenAI, (2018), Kinds of RL algorithms, Retrieved April 23, 2020, website: https://spinningup.openai.com/en/latest/spinningup/rl_intro2.html.
157. Web of Science, (2020), Why choose the Web of Science Core Collection? Retrieved April 26, 2020, website: https://clarivate.com/webofsciencegroup/solutions/web-of-science-core-collection/