| Control and Optimization in Applied Mathematics | ||
| Article 7, Volume 11, Issue 2 - Serial Number 22, May 2026, Pages 121-152 PDF (2.05 M) | ||
| Document Type: بنیادی - نظری | ||
| DOI: 10.30473/coam.2026.76472.1359 | ||
| References | ||
|
[1] Adeel, A., Gogate, M., Hussain, A. (2020). “Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments”. Information Fusion, 59, 163–170. DOI: https://doi.org/10.1016/j.inffus.2019.08.008 [2] AIAG (2008). “Potential failure mode and effect analysis (FMEA)”, 4th Edition. Automotive Industry Action Group. https://www.aiag.org/training-and-resources/manuals/details/ FMEA-4. [3] Amrit, C., Paauw, T., Aly, R., Lavric, M. (2017). “Identifying child abuse through text mining and machine learning”. Expert Systems with Applications. 88, 402–418. DOI: https://doi.org/10. 1016/j.eswa.2017.06.035 [4] Ayers, J.B. (Ed.). (2000). “Handbook of supply chain management (1st ed.)”. CRC Press. DOI: https://doi.org/10.1201/9781420025705 [5] Bowersox, D.J., Closs, D.J. (1996). “Logistical management: The integrated supply chain process”. McGraw-Hill Companies Inc., New York. [6] Dalal, N., Triggs, B. (2005). “Histograms of oriented gradients for human detection”. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), 1, 886– 89. DOI: https://doi.org/10.1109/CVPR.2005.177 [7] De Brito, M.P., Dekker, R. (2003). “Modelling product returns in inventory control—exploring the validity of general assumptions”. International Journal of Production Economics, 81, 225-241. [8] Fattah, M., Haq, M.A. (2024). “Tweet prediction for social media using machine learning, engineering”, Technology & Applied Science Research, 14(3), .14698-14703 DOI: https://doi. org/10.48084/etasr.7524 [9] Fazlollahtabar, H. (2018). “Operations and inspection cost minimization for a reverse supply chain”. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 91–107. DOI: https://doi.org/10.31181/oresta19012010191f [10] Flygansvær, B., Dahlstrom, R., Nygaard, A. (2018). “Exploring the pursuit of sustainability in reverse supply chains for electronics”. Journal of Cleaner Production, 189, 472–484. DOI: https: //doi.org/10.1016/j.jclepro.2018.04.014 [11] Garg, C.P., Sharma, A. (2020). “Sustainable outsourcing partner selection and evaluation using an integrated BWM–VIKOR framework”. Environment, Development and Sustainability, 22, 1529– 1557. DOI: https://doi.org/10.1007/s10668-018-0261-5 [12] Gattorna, J. (1998). “Strategic supply chain alignment: Best practice in supply chain management (1st ed.)”. Routledge. DOI: https://doi.org/10.4324/9781315242262 [13] Ghasemi, P., Goodarzian, F., Abraham, A., Khanchehzarrin, S. (2022). “A possibilistic-robustfuzzy programming model for designing a game theory based blood supply chain network”. Applied Mathematical Modelling, 112, 282–303. DOI: https://doi.org/10.1016/j.apm.2022.08. [14] Olugu, E.U., Wong, K.Y., Shaharoun, A.M. (2010). “A comprehensive approach in assessing the performance of an automobile closed-loop supply chain”. Sustainability, 2(4), 871-889. DOI: https://doi.org/10.3390/su2040871 [15] Goodarzian, F., Navaei, A., Ehsani, B., Ghasemi, P., Muñuzuri, J., (2023). “Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: Artificial intelligence-based solutions”. Annals of Operations Research, 328, 531–575. DOI: https: //doi.org/10.1007/s10479-022-04713-4 [16] Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y. (2016). “Deep learning”. MIT Press, Cambridge, MA. [17] Han, J., Kamber, M., Pei, J. (2012). “Data mining: Concepts and techniques (3rd ed.)”. Waltham: Morgan Kaufmann Publishers. [18] Hand, D.J., Manila H., Smyth, P. (2001). “Principles of data mining”. A Bradford Book, The MIT Press, Cambridge, Massachusetts, London, England. [19] Abbasi, S., Damavandi, S., RadmanKian, A.H. Zeinolabedinzadeh, K., Kazancoglu, Y. (2025). “Designing a green forward and reverse logistics network with an IoT approach considering backup suppliers and special disposal for epidemics management”. Results in Engineering, 26. DOI: https://doi.org/10.1016/j.rineng.2025.104770 [20] Haykin, S.S. (2009). “Neural networks and learning machines”. Volume 10, Prentice Hall. [21] Heaton, J. “Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning”. Genetic Programming and Evolvable Machines, 19, 305–307 (2018). DOI: https://doi.org/10.1007/ s10710-017-9314-z [22] Jiawei, H. Kamber, M., Pie, J. (2001). “Data mining: Concepts and techniques”, Morgan Kaufmann Publishers Inc., Elsevier. DOI: https://dl.acm.org/doi/10.5555/1972541. [23] Kattenborn, T., Leitloff, J., Schiefer, F., Hinz, S. (2021). “Review on convolutional neural networks (CNN) in vegetation remote sensing”. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 24–49. DOI: https://doi.org/10.1016/j.isprsjprs.2020.12.010 [24] Keith, R.( 2021). “So Why Do We Call it a ’Supply Chain’ Anyway?”, Industry Week, emphasis added. https://www.industryweek.com/supply-chain/article/22007454/so-why-do-we-call-it-a-supply-chain-anyway. [25] Kulkarni, A., Shivananda, A.(2019). “Natural language processing recipes: Unlocking text data with machine learning and deep learning using Python”. Apress Berkeley, CA. https://doi. org/10.1007/978-1-4842-7351-7. [26] Lambert, D.M., Enz, M. (2025). “Supply chain management: Processes, partnerships, performance”. Supply Chain Management Institute-5th Edition. [27] Lambert, D.M., Cooper, M.C., Pagh, J.D. (1998). “Supply chain management: implementation issues and research opportunities”. International Journal of Logistics Management, 9(2), 1–20. DOI: https://doi.org/10.1108/09574099810805807 [28] LeCun, Y., Bengio, Y. Hinton, G. (2015). “Deep learning”. Nature, 521(7553), 436–444. DOI: http://dx.doi.org/10.1038/nature14539 [29] Lee, J., Gen, M., Rhee, K. (2009). “Network model and optimization of reverse logistics by hybrid genetic algorithm”. Computers & Industrial Engineering, 56(3), 951-964. DOI: https://doi. org/10.1016/j.cie.2008.09.021 [30] Lee, S.J. and Siau, K. (2001). “A review of data mining techniques”. Industrial Management and Data System. 101(1), 41-46. DOI: https://doi.org/10.1108/02635570110365989 [31] Levine, S., Kumar, A., Tucker, G., Fu, J. (2020). “Offline reinforcement learning: Tutorial, review, and perspectives on open problems”. ArXiv preprint arXiv:2005.01643. DOI: https://doi.org/ 10.48550/arXiv.2005.01643 [32] Lu, Z., Bostel, N. (2007). “A facility location model for logistics systems including reverse flows: The case of remanufacturing activities”. Computers & Operations Research, 34(2), 299-323. DOI: https://doi.org/10.1016/j.cor.2005.03.002 [33] Luong, N.C., Hoang, D.T., Gong, S., Niyato, D., Wang, P., Liang, Y.C., Kim, D.I. (2019). “Applications of deep reinforcement learning in communications and networking: A survey”. IEEE communications surveys & tutorials, 21(4), 3133-3174. DOI: https://doi.org/10.48550/arXiv. 1810.07862 [34] Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G. (2001). “Defining supply chain management”. Journal of Business Logistics. 22(2), 1–25. DOI: https: //doi.org/10.1002/j.2158-1592.2001.tb00001.x [35] Minner, S. (2001). “Strategic safety stocks in reverse logistics supply chains”. International journal of production economics, 71(1-3), 417-428. DOI: https://doi.org/10.1016/ S0925-5273(00)00138-9 [36] Mousavi, S.M., Beroza, G.C. (2022). “Deep-learning seismology”. Science, 377(6607): eabm4470. DOI: https://doi.org/10.1126/science.abm4470. [37] Ngai, E.W.T., Hu, Y., Wong, Y.H., Chen, Y., Sun, X. (2011). “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature”. Decision Support Systems, 50(3), 559-569. DOI: https://doi.org/10.1016/j.dss. 2010.08.006 [38] Pang, Sh., Chen, Mu.Ch. (2024). “Investigating the impact of consumer environmental consciousness on food supply chain: The case of plant-based meat alternatives”. Technological Forecasting and Social Change. 201, 123190. DOI: https://doi.org/10.1016/j.techfore.2023. 123190 [39] Potok, T.E, Schuman, C., Young, S., Patton, R., Spedalieri, F., Liu, J., Yao, KT., Rose, G., Chakma, G. (2018). “A study of complex deep learning networks on high-performance, neuromorphic, and quantum computers”. The ACM Journal on Emerging Technologies in Computing Systems, 14(2), 1–21. DOI: https://doi.org/10.1145/3178454 [40] Rand, W.M. (1971). “Objective criteria for the evaluation of clustering methods”. Journal of the American Statistical Association. 66(336), 846-850. DOI: https://doi.org/10.2307/ 2284239 [41] Ross, J.W., Weill, P., Robertson, D. (2006). “Enterprise architecture as strategy: Creating a foundation for business execution”. Harvard Business School Press. [42] Song, X., Li, J., Cai, T., Yang, S., Yang, T., Liu, C. (2022). “A survey on deep learning-based knowledge tracing”. Knowledge-based Systems. 258, 110036. DOI: https://doi.org/10.1016/j. knosys.2022.110036 [43] Srinidhi, C.L., Ciga, O., Martel, A.L. (2021). “Deep neural network models for computational histopathology: A survey”. Medical Image Analysis, 67, 101813. DOI: https://doi.org/10. 1016/j.media.2020.101813 [44] Stamatis, D.H. (2003). “FMEA a general overview”. In: Failure Mode and Effect Analysis: FMEA from Theory to Execution, ASQ Quality Press, Milwaukee, WI, 488. [45] Stock, J.R. (1998). “Development and implementation of reverse logistics programs”. (Oak Brook, IL: Council of Logistics Management. [46] Terwiesch, C. and Cachon, G.P. (2018). “Matching supply with demand: An introduction to operations management”. McGraw-Hill Education. [47] Tibben-Lembke, R.S., Rogers, D.S. (2002). “Differences between forward and reverse logistics in a retail environment”. Supply Chain Management: An International Journal, 7(5), 271–282. DOI: https://doi.org/10.1108/13598540210447719 [48] Witten, I.H., Frank, E., Hall, M.A. (2016). “Data mining: Practical machine learning tools and techniques (4th ed.). Morgan Kaufmann. | ||
|
Statistics Article View: 134 PDF Download: 115 |
||