مدیریت استراتژیک هوشمند

مدیریت استراتژیک هوشمند

ارائه مدلی پیش‌بینانه برای مصرف انرژی در زنجیره تأمین چرخشی پایدار با استفاده از شبکه عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسنده
گروه مدیریت بازرگانی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبایی، تهران، ایران.
چکیده
افزایش روزافزون مصرف انرژی و گسترش زنجیره‌های تأمین با رویکردهای زیست‌محیطی، به یکی از چالش‌های اصلی مدیریت انرژی در نظام‌های صنعتی و اقتصادی تبدیل شده است. در این راستا، بهره‌گیری از مدل‌های پیش‌بین مبتنی بر هوش مصنوعی می‌تواند نقش مؤثری در بهینه‌سازی مصرف انرژی ایفا کند. پژوهش حاضر با هدف ارائه‌ی مدلی برای پیش‌بینی مصرف انرژی در زنجیره تأمین چرخشی پایدار انجام شده است. در این مدل، متغیرهایی نظیر حجم تولیدات، حجم محصولات بازگشتی، حجم محصولات بازیافتی، حجم محصولات تعمیری، حجم مشتریان و میزان اشتغال به‌عنوان ورودی‌های کلیدی در نظر گرفته شده‌اند. نتایج حاصل نشان داد که مدل پیشنهادی قادر است با دقتی معادل ۷۹ درصد میزان مصرف انرژی را پیش‌بینی نماید. همچنین یافته‌ها بیانگر آن است که شبکه عصبی مصنوعی (ANN) در مقایسه با سایر رویکردهای تحلیلی، بالاترین دقت و کمترین میزان خطا را در پیش‌بینی ارائه می‌دهد. تحلیل حساسیت مدل نیز نشان داد که افزایش حجم محصولات بازگشتی، بیشترین تأثیر را بر افزایش مصرف انرژی در زنجیره تأمین چرخشی پایدار دارد. بر این اساس، توجه به مدیریت بازگشت محصولات و بهینه‌سازی فرآیندهای مرتبط می‌تواند گامی مؤثر در جهت کاهش اتلاف انرژی و تحقق اهداف پایداری در زنجیره‌های تأمین نوین باشد.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Providing a predictive model for energy consumption in a sustainable circular supply chain using artificial neural networks

نویسنده English

Abolfazl Ghaderi
Department of Business Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.
چکیده English

The increasing energy consumption and the expansion of supply chains with environmental approaches have become one of the main challenges of energy management in industrial and economic systems. In this regard, the use of predictive models based on artificial intelligence can play an effective role in optimizing energy consumption. The present study aims to provide a model for predicting energy consumption in a sustainable circular supply chain. In this model, variables such as production volume, volume of returned products, volume of recycled products, volume of repaired products, volume of customers, and employment rate are considered as key inputs. The results showed that the proposed model is able to predict energy consumption with an accuracy of 79%. The findings also indicate that the artificial neural network (ANN) provides the highest accuracy and the lowest error rate in prediction compared to other analytical approaches. The sensitivity analysis of the model also showed that increasing the volume of returned products has the greatest impact on increasing energy consumption in a sustainable circular supply chain. Accordingly, paying attention to the management of returned products and optimizing related processes can be an effective step towards reducing energy waste and achieving sustainability goals in modern supply chains.

کلیدواژه‌ها English

Forecasting
Energy
Sustainable Supply Chain
Machine Learning
Liu T, Guan ,X, Wang Z, Qin T, Sun R, Wang,Y Optimizing green supply chain circular economy in smart cities with integrated machine learning technology, Heliyon 10 (2024) e29825
Alijoyo,F,A AI-powered deep learning for sustainable industry 4.0 and internet of things: Enhancing energy management in smart buildings, Alexandria Engineering Journal 104 (2024) 409–422
Yang, X.; Wang, Z.; Zhang, H.; Ma, N.; Yang, N.; Liu, H.; Zhang, H.; Yang, L. A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas. Algorithms 2022, 15, 205. https://doi.org/10.3390/a15060205
Zhu ,J, Wu ,Y Liu ,Z Costa,C Sustainable Optimization in Supply Chain
Management Using Machine Learning, International Journal of Management Science Research, Vol.8, Issue 1, (Jan) ISSN 2536-6
Cerqueira-Streit, J.A.; Endo, G.Y.; Guarnieri, P.; Batista, L. Sustainable Supply Chain Management in the Route for a Circular Economy: An Integrative Literature Review. Logistics 2021, 5, 81. https://doi.org/10.3390/ logistics5040081
Alzoubi,A MACHINE LEARNING FOR INTELLIGENT ENERGYCONSUMPTION IN SMART HOMES, International Journal of Computations, Information and Manufacturing (IJCIM), Vol.2, Issue.1, 2022
Onukwulu ,E,C, Agho M,O and Eyo-Udo,N,L Developing a framework for AI-driven optimization of supply chains in energy sector, Global Journal of Advanced Research and Reviews, 2023, 01(02), 082-0101
Jampani,S, Avancha,S Mangal,A, , Machine Learning Algorithms for Supply Chain Optimisation, International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET) Vol.11 | Issue-4 | April-2023| ISSN: 2320-6586
. Pereraa, P.U., Vahid Nikb,W, Scartezzini,J,J, Machine learning methods to assist energy system optimization, Applied Energy · June 2019
Barrie ,I Agupugo ,CH,P, Iguare ,H,O and Folarin,A Leveraging machine learning to optimize renewable energy integration in developing economies, Global Journal of Engineering and Technology Advances, 2024, 20(03), 080–093
Malhotra,G Impact of circular economy practices on supply chain capability, flexibility and sustainable supply chain performance, The International Journal of Logistics Management © Emerald Publishing Limited 0957-4093 DOI 10.1108/IJLM-01-2023-0019
Rao, H.; Li, J.; Sun, X. Demand Forecasting and Allocation Optimization of Green Power Grid Supply Chain Based on Machine Learning Algorithm: A Study Based on the Whole-Process Data of Power Grid Materials. Sustainability 2025, 17, 1247. https://doi.org/10.3390/ su17031247
Mahmood1,SH Sun,H, Alhussan3,,A,AAsifa Iqbal4 & El‑Sayed M. El‑kenawy, Active learning‑based machine learning approach for enhancing environmental sustainability in green building energy consumption, Scientifc Reports | (2024) 14:19894 | https://doi.org/10.1038/s41598-024-70729-4
Duan,Y · Khokhar,M · · Anshuman Sharma,A,R · Tahir Islam, The role of digital technology and environmental sustainability in circular supply chains based on the fuzzy TOPSIS model, Environment, Development and Sustainability  https://doi.org/10.1007/s10668-024-05924-4
BAGWARI A, (Senior Member, IEEE), , An Enhanced Energy Optimization Modelfor Industrial Wireless Sensor Networks Using Machine Learning, Digital Object Identifier 10.1109/ACCESS.2023.3311854
Afroozi,M,A Gramifar,M| azratifar,B Keshvari4 M,M
Razavian,S,B,, Optimization of Lithium‐Ion Battery Circular Economy in Electric Vehicles in Sustainable Supply Chain, Battery Energy, 2025; 4:e20240057
 Liu, J.; Chen, J. Applications and Trends of Machine Learning in Building Energy Optimization: A Bibliometric Analysis. Buildings 2025, 15, 994. https://doi.org/10.3390/ buildings15070994
Theeraworawit, M.; Suriyankietkaew, S.; Hallinger, P. Sustainable Supply Chain Management in a Circular Economy: A Bibliometric Review. Sustainability 2022, 14, 9304. https://doi.org/ 10.3390/su14159304
de la Torre, R.; Onggo, B.S.; Corlu, C.G.; Nogal, M.; Juan, A.A. The Role of Simulation and Serious Games in Teaching Concepts on Circular Economy and Sustainable Energy. Energies 2021, 14, 1138. https://doi.org/10.3390/en14041138
Joshan. Advanced Machine Learning Techniques for Smart Grid Optimization and Energy Management. J. Eng. Ind. Res. 2025, 6 (2):142-156
Han CH, Zhang, Q,Optimization of supply chain efficiency management based on machine learning and neural network, Neural Computing and Applications https://doi.org/10.1007/s00521-020-05023-1
Liu Z, Xu SH Shihao Zhao a, Yuewen Li b, Meiling Zhou a, Shuosen Li b,
Fei Meng, Clean energy supply chain optimization: Steady-state natural gas transportation, Cleaner Logistics and Supply Chain 15 (2025) 100214
JAMIL  ,F IQBAL , N,IMRAN , AHMAD ,SH, Peer-to-Peer Energy Trading Mechanism Based on Blockchain and Machine Learning for Sustainable Electrical Power Supply in Smart Grid, Digital Object Identifier 10.1109/ACCESS.2021.3060457