- خانجانی، داود. (1400). رکن چهارم دوره عالی ،چاپ نامعلوم، تهران :انتشارات مرکز آموزش پشتیبانی شهید فیضالله امانی نزاجا
- خرمورز، فاطمه. (1401). شناسایی و کاهش ریسک زنجیرهتامین با استفاده از چارچوب دادهکاوی،پایاننامه کارشناسی ارشد،دانشکده فنی و مهندسی،دانشگاه خوارزمی.https://ganj.irandoc.ac.ir/#/articles/7f610ed1cb2872915d44a7683eb30232
- دارابی، احسان. (1401). پیشبینی تقاضا با دادهکاوی و تعیین استراتژی سفارش دهی مطالعه موردی: شرکت خدمات و تجارت بم خودرو ،پایاننامه کارشناسی ارشد،دانشکده صنایع و مدیریت گروه مهندسی صنایع دانشگاه صنعتی سجاد. https://ganj.irandoc.ac.ir/#/articles/f620b8a0261a375ce044ba1b05971c48
- محمدی، شعبان، سعیدی، طالبی نجفآبادی، عبدالحسین، & الهی شیروان. (1401). ارائه مدلی برای پیشبینی سری زمانی قیمتهای نویز دار سهام با استفاده از تحلیل طیف تکین، رگرسیون بردار پشتیبان به همراه بهینهسازی ازدحام ذرات و مقایسه آن با عملکرد مدلهای تبدیل موجک، شبکه عصبی، فرآیند میانگین متحرک خود رگرسیون و رگرسیون چندجملهای. مهندسی مالی و مدیریت اوراق بهادار، https://www.noormags.ir/view/en/articlepage/2007008
- نامی، علی. (1399). بررسی ترکیبهای روشهای پیشبینی تقاضا در زنجیرههای تأمین و کاربردهای آن ،پایاننامه کارشناسی ارشد،دانشکده ریاضی و کامپیوتر،دانشگاه شهید باهنر کرمان.https://ganj.irandoc.ac.ir/#/articles/2f990821ca4833b8f487a17bca82618c
- نوبختی، سید عباس.(1401). بررسی مقایسهای روشهای منتخب برای پیشبینی تقاضا مطالعه موردی شرکت کرمان موتور،پایاننامه کارشناسی ارشد،دانشکده مهندسی پیشرفت،دانشگاه علم و صنعت. https://ganj.irandoc.ac.ir/#/articles/ec1dbd2734f043c452a37feb7763bc7e
-ضرغامی، حمیدرضا، فرهادی، علی و ستار پور، سعید . (1402). تعیین و اولویتبندی عوامل مزیت ساز لجستیک دفاعی ارتش جمهوری اسلامی ایران. علوم و فنون نظامی, 19(65), 87-111. https://doi.org/10.22034/qjmst.2024.561434.1768
-ضیغمی نژاد، پرویز، روشنی، رضا، طگانی، غلامرضا، فروزان، ایرج و ایجابی، ابراهیم . (1401). تدوین راهبردهای لجستیک (آماد و پشتیبانی) عملیات مشترک ارتش جمهوری اسلامی ایران. آیندهپژوهی دفاعی. 7(24), 81-107. doi: 10.22034/dfsr.2022.550231.1583
- فرهمند نژاد, علی , محمد زهرایی, سپهر و محمد زهرایی, سهیل . (1402). کاربرد اینترنت اشیاء در لجستیک ارتش جمهوری اسلامی ایران. فصلنامه مطالعات جنگ.5(19), 74-103. doi: 10.22034/qjws.2024.2014134.1177
- Alejandro-Chable, J. D., Salais-Fierro, T. E., Saucedo-Martínez, J. A., & Cedillo-Campos, M. G. (2024). A new Lean logistics management model for the modern supply chain. Mobile Networks and Applications, 29(1), 70-81.https://link.springer.com/article/10.1007/s11036-022-02018-1
- Armstrong, J. S. (Ed.). (2001). Principles of forecasting: a دستینه for researchers and practitioners (Vol. 30). Springer Science & Business Media. https://books.google.com/books?hl=fa&lr=&id=ezTaBwAAQBAJ&oi
- Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer. https://link.springer.com/book/9780387310732
- Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons. https://elib.vku.udn.vn/bitstream/123456789/2536/1/1994.%20Time%20Series%20Analysis-Forecasting%20and%20Control.pdf
- Chase, C. W. (2013). Demand-driven forecasting: a structured approach to forecasting. John Wiley & Sons. https://books.google.com/books?hl=fa&lr=&id=ZlzmFPRxNW4C&oi=fnd&pg=PR11&dq=
- Christopher, M. (2022). Logistics and supply chain management: logistics & supply chain management. Pearson UK. https://books.google.com/books?hl=fa&lr=&id=hRTQEAAAQBAJ&oi=fnd&pg=PP1&dq=-
- Darabi, E. (1401). Forecasting demand with data mining and determining the ordering strategy of a case study: Bam Khodro Service and Trade Company, Master's Thesis, Faculty of Industry and Management, Department of Industrial Engineering, Sajjad University of Technology. [InPersian]https://ganj.irandoc.ac.ir/#/articles/f620b8a0261a375ce044ba1b05971c48
- Deng, C., & Liu, Y. (2021). A deep learning‐based inventory management and demand prediction optimization method for anomaly detection. Wireless communications and mobile computing, 2021(1), 9969357.https://doi.org/10.1155/2021/9969357
- Farhamandenjad, Ali, Mohammad Zahraei, Sepehr and Mohammad Zahraei, Sohail. (1402). The use of Internet of Things in the logistics of the Islamic Republic of Iran Army. War Studies Quarterly, 5(19), 74-103[In Persian]..5(19), 74-103. doi: 10.22034/qjws.2024.2014134.1177
- Frisch, R., & Waugh, F. V. (1933). Partial time regressions as compared with individual trends. Econometrica: Journal of the Econometric Society, 387-401. https://www.jstor.org/stable/1907330
- Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International journal of forecasting, 20(1), 5-10.https://doi.org/10.1016/j.ijforecast.2003.09.015
- Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. https://books.google.com/books?hl=fa&lr=&id=_bBhDwAAQBAJ&oi=fnd&pg=PA7&dq
- Jałowiec, T., & Grala, D. (2020, April). The Effectiveness of Logistic Processes in Military Supply Chains. In Proceedings of the 35th International Business Information Management Association Conference (IBIMA) (pp. 1-2).
- Kagalwala, H., Radhakrishnan, G. V., Mohammed, I. A., Kothinti, R. R., & Kulkarni, N. (2025). Predictive analytics in supply chain management: The role of AI and machine learning in demand forecasting. Advances in Consumer Research, 2, 142-149. https://acr-journal.com/article/predictive-analytics-in-supply-chain-managemen
Khanjani, D. (2021). The fourth pillar of the higher course, unknown publication, Tehran: Publications of Shahid Faizullah Amani Nezaja Support Training Center. [In Persian]
Khorramvarz, F. (2022). Identifying and reducing supply chain risk using a data mining framework, Master's thesis, Technical and Engineering Faculty, Kharazmi University. [In Persian] https://ganj.irandoc.ac.ir/#/articles/7f610ed1cb2872915d44a7683eb30232
- Kohli, S., Godwin, G. T., & Urolagin, S. (2020). Sales prediction using linear and KNN regression. In Advances in Machine Learning and Computational Intelligence: Proceedings of ICMLCI 2019 (pp. 321-329). Singapore: Springer Singapore. https://link.springer.com/chapter/10.1007/978-981-15-5243-4_29
- Korkmaz, G. (2024). SUPPLY CHAIN RISKS AND MANAGEMENT STRATEGIES IN THE DEFENSE INDUSTRY. Journal of Defense Resources Management (JoDRM), 15(1), 35-60. https://www.ceeol.com/search/article-detail?id=1241727
- Kouvelis, P., Chambers, C., & Wang, H. (2006). Supply chain management research and production and operations management: Review, trends, and opportunities. Production and Operations Management, 15(3),449-469https://doi.org/10.1111/j.1937-5956.2006.tb00257.x.
- Lupsa, M., Arnell, A., Malmström, M., & Mutic, E. (2024). From incoherence to coherence. Which languages are given (which) importance in the Swedish curricula from 1962 to 2022?. In Coherence and Fragmentation: The Languages of the Nordic Countries and their Interrelations Today. University of Florence 14-16 Nov 2024.
- Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons. https://books.google.com/books?hl=fa&lr=&id=JCFiBwAAQBAJ&oi=fnd&pg=PR11&dq
- Nami, A. (2019). Examining the combinations of demand forecasting methods in supply chains and their applications, Master's Thesis, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman.[InPersian] https://ganj.irandoc.ac.ir/#/articles/2f990821ca4833b8f487a17bca82618c
-
- Nobakhti, A. (2022). A comparative study of selected methods for demand forecasting, a case study of Kerman Motor Company, Master's Thesis, Advance Engineering College, University of Science and Technology.[In Persian] https://ganj.irandoc.ac.ir/#/articles/ec1dbd2734f043c452a37feb7763bc7e
- Parlier, G. H. (2016). Mission-Based Forecasting: Demand Forecasting for Military Operations. Foresight: The International Journal of Applied Forecasting, (43). https://openurl.ebsco.com/EPDB%3Agcd%3A7%3A5018391/detailv2?sid=
- Sani, S., Schaefer, D., & Milisavljevic-Syed, J. (2022). Strategies for achieving pre-emptive resilience in military supply chains. Procedia CIRP, 107, 1526-1532. https://doi.org/10.1016/j.procir.2022.05.186
- Shaaban,M. Talebi-Najafabadi,S. & Elahishirvan,A. (2021). Presenting a model for predicting the time series of noisy stock prices using singular spectrum analysis, support vector regression along with particle swarm optimization and comparing it with the performance of wavelet transform models, neural network, autoregressive moving average process and polynomial regression. Financial Engineering and Securities Management, 49(12), 399-421. [In Persian] https://www.noormags.ir/view/en/articlepage/2007008
- Shamami, N., Mehdizadeh, E., Yazdani, M., & Etebari, F. (2024). War game problem considering the mobility of weapons and targets. Journal of Engineering Research, 12(1), 214-225.https://doi.org/10.1016/j.jer.2023.11.021
- Sharma, A. (2023). Managing Complex Supply Chains: Lessons from Military Logistics. International Journal of Supply Chain Management, 12(6), 26-38.https://core.ac.uk/reader/595400010#:~:text=https%3A//doi.org/10.59160/ijscm.v12i6.6215
- Silver, E. A., Pyke, D. F., & Thomas, D. J. (2017). Inventory management and production planning and scheduling.
- Van der Auweraer, S., Boute, R. N., & Syntetos, A. A. (2019). Forecasting spare part demand with installed base information: A review. International Journal of Forecasting, 35(1), 181-196.https://doi.org/10.1016/j.ijforecast.2018.09.002
- Zaighminejad, Parviz, Roshni, Reza, Tahani, Gholamreza, Forozan, Iraj and Ijabi, Ibrahim. (1401). Compilation of logistics strategies (readiness and support) of joint operations of the Islamic Republic of Iran Army. defense future research, 7(24), 81-107. https://doi.org/10.22034/dfsr.2022.550231.1583
- Zangara, G., Corvello, V., & Filice, L. (2024). Are rules for social sustainability in supply chains strong enough? Defining the “nullification principle”.ProcediaComputerScience,232,87-96.https://doi.org/10.1016/j.procs.2024.01.009
- Zarghami, Hamidreza, Farhadi, Ali and Sattarpour, Saeed. (1402). Determining and prioritizing the advantageous factors of defense logistics of the Islamic Republic of Iran Army. Military Sciences and Techniques.[In Persian]. https://doi.org/10.22034/qjmst.2024.561434.1768
- Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2021). Understanding deep learning (still) requires rethinking generalization. Communications of the ACM, 64(3), 107-115.https://doi.org/10.1145/3446776
