طراحی مدل زنجیره فولاد و برآورد میزان مصرف با رویکرد مدل‌سازی عاملبنیان | ||
| چشم‌انداز مدیریت صنعتی | ||
| دوره 11، شماره 1 - شماره پیاپی 41، فروردین 1400، صفحه 33-52 اصل مقاله (446.98 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.52547/jimp.11.1.33 | ||
| نویسندگان | ||
| عادل آذر* 1؛ مهدی مشایخی2؛ مجتبی امیری3؛ حسین صفری4 | ||
| 1استاد، دانشگاه تربیت مدرس. | ||
| 2دانشجوی دکتری، دانشگاه تهران. | ||
| 3دانشیار، دانشگاه تهران. | ||
| 4استاد، دانشگاه تهران. | ||
| چکیده | ||
| این پژوهش با هدف ارائه مدلی عاملبنیان که بتواند با درنظرگرفتن عوامل کلیدی صنعت فولاد، زنجیره تأمین فولاد را شبیه‌سازی و میزان تولید و مصرف آن را برآورد کند، اجرا شده است. رویکرد پژوهش حاضر آمیخته (کمّی و کیفی) است که در این راستا در بخش نخست پژوهش (کیفی) عوامل تعیین‌کننده مدل برآورد میزان مصرف زنجیره فولاد از طریق مصاحبه با خبرگان از روش تحلیل تم بهدست آمدند، در بخش دوم پژوهش(کمّی) برای درک و بررسی روابط علّی و معلولی عوامل استخراج‌شده از مصاحبه‌ها و روش تحلیل تم از پرسشنامه استفاده شده و سپس مدل روابط به روش دیمتل آزمون شده است. در انتها با استفاده از نرم‌افزار AnyLogic و کدگذاری به زبان جاوا مدل برآورد میزان مصرف زنجیره فولاد با رویکرد مدل‌سازی عاملبنیان طراحی شد و طبق نظر خبرگان شبیه‌سازی عاملبنیان، فرایند تبیین مدل نیز موردتأیید قرار گرفت. با توجه به نتایج این شبیه‌سازی، مدل ارائه‌شده می‌تواند برآورد مناسبی از آتیه زنجیره تأمین فولاد و میزان مصرف زنجیره را ارائه دهد؛ همچنین ترکیب عامل‌های شناسایی و معرفی‌‌شده در این پژوهش منطبق بر تأثیر عوامل بر تولید، مصرف، واردات و صادرات زنجیره فولاد در مدل ساختاری است. | ||
| کلیدواژه‌ها | ||
| مدیریت زنجیره تأمین؛ برآورد میزان مصرف زنجیره فولاد؛ روش تحلیل تم؛ روش دیمتل؛ مدل‌سازی عاملبنیان | ||
| عنوان مقاله [English] | ||
| Modeling Steel Supply Chain and Estimating Its Consumption through ABM Methodology | ||
| نویسندگان [English] | ||
| Adel Azar1؛ Mahdi Mashayekhi2؛ Mojataba Amiri3؛ Hossein Safari4 | ||
| 1Professor, Tarbiat Modares University. | ||
| 2Ph.D Candidate, University of Tehran. | ||
| 3Associate Professor, University of Tehran. | ||
| 4Professor, University of Tehran. | ||
| چکیده [English] | ||
| The purpose of this study was to develop an agent based model that could simulate the steel supply chain and estimate its production and consumption, taking into account the key factors of the steel industry. The approach of the present study is mixed (quantitative and qualitative). In the first part of the research (qualitative), the agents of the steel chain consumption model were obtained through interviews with experts using thematic analysis method. In the second part of the research (quantitative), a questionnaire was used to survey the causal relationships of the factors extracted from the interviews and the thematic analysis method, and then the relationship model was tested by the DEMATEL method. Finally, by using AnyLogic software and coding in Java language, a model of steel supply chain and its consumption was designed throughan agent-based approach, and according to the opinion of steel industry experts, the model explanation process was also approved. The combination of agents identified in this study is consistent with the influence of factors on production, consumption, import and export of the steel chain in the proposed structural model. | ||
| کلیدواژه‌ها [English] | ||
| Supply Chain Management, Steel Chain Consumption, Thematic Analysis Method, DEMATEL Method, Agent Based Modeling | ||
| مراجع | ||
|
1. Aghaei, Milad, Fazli, zero. (2012). Applying the combined approach of DEMATEL and ANP to select the appropriate maintenance strategy (Case study: Work Vehicle Industry). Journal of Industrial Management Perspective, 2(2), 89-107. (In Persian) 2. Afshar Kazem, M.A., Makoei, A., Darman, Z. (2009). Developing the Supply Chain Strategy of Iran Steel Industry Using Systems Dynamics Analysis, Iranian journal of trade studies, 13(50), 201-224. (In Persian) 3. Azar,A, & Sadeghi A.(2015). Agent based modeling, a new approach in modeling complex ethical problems. Ethics in Science. & Technology, 7(1), 11-19.(In Persian) 4. Azar,A, Abedini Nayini, M. (2015). Designing a hybrid order planning model in the supply chain. Ministry of Science, Research and Technology - Tarbiat Modarres University.(In Persian) 5. Azimifard, A., Moosavirad, S. H., & Ariafar, S. (2018). Selecting sustainable supplier countries for Iran's steel industry at three levels by using AHP and TOPSIS methods. Resources Policy, 57, 30-44. 6. Bafandeh, A. & Nemat Abad, N. (2015). Agent-Baesd modeling is the basis of a new approach for analyzing consumer preferences. 4th National Conference on Management, Economics and Accounting, Tabriz, East Azarbaijan Industrial Management Organization, Tabriz University.(In Persian) 7. Bates, H., & Slack, N. (1998). What happens when the supply chain manages you?: A knowledge-based response. European Journal of Purchasing & Supply. Management, 4(1), 63-72. 8. Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the national academy of sciences, 99(suppl 3), 7280-7287. 10. Casti, J.L. (1999). the computer as a laboratory: toward a theory of complex, adaptive systems. Complexity, 4(5), 12–14. 11. Chenery, H. B. (1960). Patterns of industrial growth. The American Economic Review, 50(4), 624-654. 12.Chopra, S., & Meindl, P. (2007). Supply Chain Management: Strategy, Planning, and Operation: Pearson Prentice Hall. 13. Cowling, P. (2003). A flexible decision support system for steel hot rolling mill scheduling. Computers & Industrial Engineering, 45(2), 307-321. 14.Cowling, P., & Rezig, W. (2000). Integration of continuous caster and hot strip mill planning for steel production. Journal of Scheduling, 3(4), 185-208. 15.Cowling, P. I., Ouelhadj, D., & Petrovic, S. (2004). Dynamic scheduling of steel casting and milling using multi-agents. Production Planning & Control, 15(2), 178-188. 16. Feliks, J., & Majewska, K. (2015, June). Agent-based modeling of steel production processes under uncertainty. In Proceedings of Abstracts from the 24th International Conf. on Metallurgy and Materials (Brno, Czech Republic, 6-10. 17. Fradkov, A. L., Miroshnik, I. V., & Nikiforov, V. O. (2013). Nonlinear and adaptive control of complex systems (Vol. 491). Springer Science & Business Media. 18. Ghaleban, M. Taheri, A. (2014). A fundamental operating model framework for simulating stakeholder behavior for water resources management. Journal of Water and Sustainable Development, 2, Issue 1, 87-94.(In Persian) 19. Jacobi, Sven & León-Soto, Esteban & Madrigal-Mora, Cristián & Fischer, Klaus. (2007). MasDISPO: A Multiagent Decision Support System for Steel Production and Control. 1707-1714. 20. Jafarnejad, Ahmad, Mohseni, Maryam, Abdollahi, Ali. (2014). Providing a fuzzy PROMETHEE-AHP hybrid approach to evaluate the supply chain performance (Case study: Hospitality industry). Journal of Industrial Management Perspective, 4(2), 69-92. (In Persian) 21. Jarras, I., & Chaib-Draa, B. (2002). Aperçu sur les systèmes multiagents (No. 2002s-67). Cirano. 22. Jennings, N. R., & Wooldridge, M. (1995). Applying agent technology. Applied Artificial Intelligence an International Journal, 9(4), 357-369. 23. Kolyaei M, Azar A, Rajabzadeh ghatari A.(2015). Design of An Integrated Robust Optimization Model for Closed-Loop Supply Chain and supplier and remanufacturing subcontractor selection. Journal of Decision Engineering, 2(7), 7-40. (In Persian) 24. Maciol, A., & Rebiasz, B. (2008). Agent-Based modelling and simulation in steel products market forecasting. Steel Research International, vol. 2, 863-870. 25. New, S. J., & Payne, P. (1995). Research frameworks in logistics: three models, seven dinners and a survey. International Journal of Physical Distribution & Logistics Management, 25(10), 60-77 26. O'Hare, G. M., Jennings, N. R., & Jennings, N. (1996). Foundations of distributed artificial intelligence (Vol. 9): John Wiley & Sons. 27. Rezaei Pendari, Abbas, (2014). Designing a service supply chain performance evaluation model; Cognitive mapping approach (Case study: Insurance industry in Iran. Journal of Industrial Management Perspective, 16, 388-404. (In Persian) 28. Russel S, Norvig P: Artificial Intelligence (2010). a Modern Approach. 2nd edition. Hong Kong: Pearson Education Asia Limited and Tsinghua Univ. Press; 2006. 29. Samuelson, D. A., & Macal, C. M. (2006). Agent-based simulation comes of age. OR MS TODAY, 33(4), 34-38. 30. Santa-Eulalia, L., D’Amours, S., Frayret, J., & Azevedo, R. (2009). On supply chain modelling and simulation techniques: A literature review taxonomy. Proceedings of the XI SIMPEP Simpósio de Engenharia de Produçao, Bauru, Brazil, Journal of Industrial Management ,4(4):624-668. 31. Srinivasan, S., Kumar, D., & Jaglan, V. (2010). Multi-agent system supply chain management in steel pipe manufacturing. IJCSI International Journal of Computer Science Issues, 7(4), 1694-0814. 32. Tang, L., Liu, J., Rong, A., & Yang, Z. (2001). A review of planning and scheduling systems and methods for integrated steel production. European Journal of Operational Research, 133(1), 1-20. 33. Tang, L., Luh, P. B., Liu, J., & Fang, L. (2002). Steel-making process scheduling using Lagrangian relaxation. International Journal of Production Research, 40(1), 55-70. 34. Vakili Fard, H. Foroughnejad, M, Khoshnoud, M. (2015). Agent-based modeling in financial markets. Journal of Investment Knowledge Third Year, 12. (In Persian) 35. Walton, L. W., & Miller, L. G. (1995). Moving toward LIS theory development: a framework of technology adoption within channels. Journal of Business Logistics, 16(2), 117. 36. Weiss, G. (Ed.). (1998). Multiagent Systems, A Modern Approach to Distributed Artificial Intelligence. Cambridge, Massachusetts: The MIT Press. 37. Yamamura, K., Matsuzaki, S., Toh, T., Yamada, W., & Nakagawa, J. (2012), Development of Mathematical Science in Steel Industry, Nippon Steel Technical Report, 101144-154, | ||
|
آمار تعداد مشاهده مقاله: 4,164 تعداد دریافت فایل اصل مقاله: 3,625 |
||