بررسی زنجیره تأمین حلقه‌باز و حلقه‌بسته تحت شرایط عدم‌قطعیت (موردمطالعه: شرکت ایران ترانسفو) | ||
| چشم‌انداز مدیریت صنعتی | ||
| مقاله 2، دوره 10، شماره 2 - شماره پیاپی 38، تیر 1399، صفحه 33-53 اصل مقاله (535.23 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.52547/jimp.10.2.33 | ||
| نویسندگان | ||
| مهسا محمدی1؛ حامد سلیمانی* 2 | ||
| 1کارشناسی ارشد مهندسی صنایع، دانشکده مهندسی مکانیک و صنایع، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران. | ||
| 2دانشیار گروه مهندسی صنایع، دانشکده مهندسی مکانیک و صنایع، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران | ||
| چکیده | ||
| یکی از عوامل اصلی رقابت در فضای رقابتی کنونی «زنجیره تأمین» است؛ به‌طوری‌که کارخانه‌ها و سازمان‌ها برای افزایش کارایی و اثربخشی، و دستیابی به اهداف و آرمان‌های خود، ملزم به داشتن یک زنجیره تأمین مناسب هستند. از طرفی به‌دلیل افزایش روز‌افزون آلودگی‌های زیستمحیطی و الزامات دولت‌ها برای مقابله با آلودگی‌های زیستمحیطی، سازمان‌ها ملزم به به‌کارگیری زنجیره‌های تأمین سبز هستند که مسائل زیستمحیطی را در کنار مسائل مالی مدنظر قرار می‌دهد؛ ازاین‌رو در این پژوهش یک مدل دوهدفه زنجیره تأمین حلقه‌بسته سبز تحت شرایط عدم‌قطعیت ارائه شده است که جنبه‌های زیستمحیطی را در کنار جنبه‌های اقتصادی در نظر می‌گیرد. جنبه بسیار مهم دیگر در طراحی شبکه زنجیره تأمین، عدم‌قطعیت است. به دلیل تحولات اجتماعی و سیاسی و کمیابی مواد اولیه برای تولید، عدم‌قطعیت عامل مهمی در مدل‌های شبکه زنجیره تأمین است. به همین دلیل در این مدل با درنظرگرفتن عدم‌قطعیت سعی در انطباق مدل با شرایط واقعی شده است. نتایج حاکی از آن است که طراحی زنجیره‌های تأمین سبز با درنظرگرفتن عدم‌قطعیت باعث انعطاف‌پذیری هرچه بیشتر مدل در برابر نوسانات بازار، تحولات اجتماعی و سیاسی و غیره می‌شود. به‌منظور بررسی کارایی و عملکرد مدل ارائه‌شده، مدل مذکور بر روی شرکت ایران ترانسفو پیاده سازی شده است. | ||
| کلیدواژه‌ها | ||
| زنجیره تأمین حلقه‌بسته؛ بهینه‌سازی استوار؛ عدم‌قطعیت؛ بهینه‌سازی چندهدفه؛ زنجیره تأمین سبز؛ اپسیلون محدودیت | ||
| عنوان مقاله [English] | ||
| Investigating Open Loop and Closed-Loop Supply Chain under Uncertainty (Case Study: Iran Teransfo Company) | ||
| نویسندگان [English] | ||
| mahsa mohammadi1؛ Hamed Soleimani2 | ||
| 1M.Sc., Department of industrial engineering, Faculty of industrial and Mechanical engineering, Qazvin Branch, Islamic Azad University (IAU), Qazvin, Iran. | ||
| 2Associate professor of Department of industrial engineering, Faculty of industrial and Mechanical engineering, Qazvin Branch, Islamic Azad University (IAU), Qazvin, Iran. | ||
| چکیده [English] | ||
| One of the main components of competition in the current competitive environment is supply chain; therefore, organizations need to have a reliable supply chain to increase efficiency and effectiveness. Moreover, due to the increase in environmental pollution and the requirements imposed by the governments to harness polluting activities, organizations are obliged to follow green supply chain practices that account for environmental considerations along with economic aspects. hence, in this study, a bi-objective model for a green, closed-loop supply chain under demand uncertainty is proposed which takes into account environmental consideration and economic aspects. Another important aspect of the supply chain network design is the concept of uncertainty. Due to societal and political evolutions and the scarcity of raw materials in the decision-making horizon, uncertainty is a significant measure in the models of supply chain. Indeed, in this study, the model was developed for a supply chain under uncertainty so that more compatibility with real-world conditions would be achieved. The results show that considering uncertainties makes the model more flexible. The advancement of technology and unpredictable behaviors of customers in markets have created a very complex competitive atmosphere. To evaluate the performance of the developed model, the case study of the Iran Transfo Company is considered. | ||
| کلیدواژه‌ها [English] | ||
| Closed-Loop Supply Chain, Robust Optimization, Uncertainty, Multi-Objective Optimization, Green Supply Chain, ɛ-Constraint Method | ||
| مراجع | ||
|
1. Abdallah, T., Diabat, A., & Simchi-Levi, D. (2010). A carbon sensitive supply hain network problem with green procurement. Institute of Electrical and Electronics Engineers. 2. Akbari, A. A., & Karimi, B. (2015). A new robust optimization approach for integrated multi-echelon, multi-product, multi-period supply chain network design under process uncertainty. The International Journal of Advanced Manufacturing Technology, 79(1-4), 229-244. 3. Amaro, A. C. S., & Barbosa-Póvoa, A. P. F. (2009). The effect of uncertainty on the optimal closed-loop supply chain planning under different partnerships structure. Computers & Chemical Engineering, 33(12), 2144-2158. 4. Ambrosino, D., & Scutella, M. G. (2005). Distribution network design: New problems and related models. European journal of operational research, 165(3), 610-624. 5. Amirkhan, M., Norang, A., & Tavakkoli-Moghaddam, R. (2014). A Fuzzy Mathematical Programming Model for a Supply Network Design of Raw Materials under Uncertainty–A Case Study. International Journal of Industrial Engineering, 25(2), 217-235. 6. Azaron, A., Brown, K. N., Tarim, S. A., & Modarres, M. (2008). A multi-objective stochastic programming approach for supply chain design considering risk. International Journal of Production Economics, 116(1), 129-138. 7. Azar, Adel. Najafi, SAjad. (2011). Budget planning mathematical model in public section: Robust optimization approach. Journal of Governmental Management Perspective, 2(8), 83-94 (In Persian). 8. Beamon, B. M. (1998). Supply chain design and analysis: Models and methods. International journal of production economics, 55(3), 281-294. Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust optimization (Vol. 28). Princeton University Press. 9. Ben-Tal, A., Boyd, S., & Nemirovski, A. (2006). Extending scope of robust optimization: Comprehensive robust counterparts of uncertain problems. Mathematical Programming, 107(1-2), 63-89. 10. Drezner, Z., & Wesolowsky, G. O. (2003). Network design: selection and design of links and facility location. Transportation Research Part A: Policy and Practice, 37(3), 241-256. 11.Dutta, P., Das, D., Schultmann, F., & Fröhling, M. (2016). Design and planning of a closed-loop supply chain with three ways recovery and buy-back offer. Journal of Cleaner Production, 135, 604-619. Emadabadi, A., teymoori, E., & Pishvaee, M. (2019). Design of Multi-Periodical and MultiProduct Supply Chain Network with Regard to Disruption of Facilities and Communication Paths (Case Study: Subscription Plan for Publications). Journal of Industrial Management Perspective, (in Persian) 13. Farahani, R., Zanjirani, N., Asgari, & Davarzani, H. (2009). eds. Supply chain and logistics in national, international and governmental environment: concepts and models. Springer Science & Business Media, Springer-Verlag. 316p. (Contributions to Management Science) ISSN (print) 1431-1941 ISBN 9783790821550. 14. Ghomi-Avili, M., Naeini, S. G. J., Tavakkoli-Moghaddam, R., & Jabbarzadeh, A. (2018). A fuzzy pricing model for a green competitive closed-loop supply chain network design in the presence of disruptions. Journal of Cleaner Production, 188, 425-442. 15. Gladwin, T. N., Kennelly, J. J., & Krause, T. S. (1995). Shifting paradigms for sustainable development: Implications for management theory and research. Academy of management Review, 20(4), 874-907. 16. Govindan, K., & Soleimani, H. (2017). A review of reverse logistics and closed-loop supply chains: A Journal of Cleaner Production focus. Journal of Cleaner Production, 142, 371-384. 17. Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603-626. 18. Govindan, K., Kaliyan, M., Kannan, D., & Haq, A. N. (2014). Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. International Journal of Production Economics, 147, 555-568. 19. Govindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28. 20. Haddadsisakht, A., & Ryan, S. M. (2018). Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax. International Journal of Production Economics, 195, 118-131. 21. Gutiérrez, G. J., Kouvelis, P., & Kurawarwala, A. A. (1996). A robustness approach to uncapacitated network design problems. European Journal of Operational Research, 94(2), 362-376. 22. Heidari-Fathian, H., & Pasandideh, S. H. R. (2018). Green-Blood supply chain network design: Robust optimization, Bounded Objective Function & Lagrangian relaxation. Computers & Industrial Engineering. 23. Heidari-Fathian, H., & Pasandideh, S. H. R. (2017). Green Supply Chain Network Design under Multi mode Production and Uncertainty. Iranian Journal of Operations Research, 8(1), 44-60. 24. Ho, C. J. (1989). Evaluating the impact of operating environments on MRP system nervousness. The International Journal of Production Research, 27(7), 1115-1135. 25. Jabbarzadeh, A., Fahimnia, B., & Sheu, J. B. (2017). An enhanced robustness approach for managing supply and demand uncertainties. International Journal of Production Economics, 183, 620-631. 26. Jamshidi, R., Ghomi, S. F., & Karimi, B. (2012). Multi-objective green supply chain optimization with a new hybrid memetic algorithm using the Taguchi method. Scientia Iranica, 19(6), 1876-1886. 27. Khosrojerdi, A., Zegordi, S. H., Allen, J. K., & Mistree, F. (2016). A method for designing power supply chain networks accounting for failure scenarios and preventive maintenance. Engineering Optimization, 48(1), 154-172. 28. Kisomi, M. S., Solimanpur, M., & Doniavi, A. (2016). An integrated supply chain configuration model and procurement management under uncertainty: a set-based robust optimization methodology. Applied Mathematical Modelling, 40(17-18), 7928-7947. 29. Ma, H., & Li, X. (2018). Closed-loop supply chain network design for hazardous products with uncertain demands and returns. Applied Soft Computing, 68, 889-899. 30. Manoochehri, Saba. Tajedin, Ali. Shirazi, Babak. (2019). Robust Integrated Optimization for Green Closed Loop Supply Chain. Journal of Industrial Management Perspective, 8(4), 61-95 (in Persian) 31. Melkote, S., & Daskin, M. S. (2001). Capacitated facility location/network design problems. European journal of operational research, 129(3), 481-495. 32. Mirzapour Al-E-Hashem, S. M. J., Malekly, H., & Aryanezhad, M. B. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International Journal of Production Economics, 134(1), 28-42. 33. Mota, B., Gomes, M. I., Carvalho, A., & Barbosa-Povoa, A. P. (2018). Sustainable supply chains: An integrated modeling approach under uncertainty. Omega, 77, 32-57. 34. Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281. 35. Mulvey, J. M., & Ruszczyński, A. (1995). A new scenario decomposition method for large-scale stochastic optimization. Operations research, 43(3), 477-490. 36. Nurjanni, K. P., Carvalho, M. S., & Costa, L. (2017). Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model. International Journal of Production Economics, 183, 421-432. 37. Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683. 38. Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637-649. 39. Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2014). An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain. Transportation Research Part E: Logistics and Transportation Review, 67, 14-38. 40. Rad, R. S., & Nahavandi, N. (2018). A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. Journal of Cleaner Production.196, 1549-1565 41. Bozorgi Amiri, A. Mansouri, S, Pishvaeei, S. (2017). Multipurpose Relief Chain Network Design to Respond to Earthquake Under Uncertainty. Journal of Industrial Management Perspective, 7(1), 9-36 (In Persian) 42. Rahmani, D., Ramezanian, R., Fattahi, P., & Heydari, M. (2013). A robust optimization model for multi-product two-stage capacitated production planning under uncertainty. Applied Mathematical Modelling, 37(20-21), 8957-8971. 43. Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A robust design for a closed-loop supply chain network under an uncertain environment. The International Journal of Advanced Manufacturing Technology, 66(5-8), 825-843. 44. Ramudhin, A., Chaabane, A., & Paquet, M. (2010). Carbon market sensitive sustainable supply chain network design. International Journal of Management Science and Engineering Management, 5(1), 30-38. 45. Ruimin, M. A., Lifei, Y. A. O., Maozhu, J. I. N., Peiyu, R. E. N., & Zhihan, L. V. (2016). Robust environmental closed-loop supply chain design under uncertainty. Chaos, Solitons & Fractals, 89, 195-202. 46. Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28(5), 581-598. 47. Sahebjamnia, N., Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). Sustainable tire closed-loop supply chain network design: Hybrid metaheuristic algorithms for large-scale networks. Journal of cleaner production, 196, 273-296. 48. Saffar, M. Shakori Ganjavi, H. Razmi, J (2019) A green closed-lopp supply chin network design considering operational risk under uncertainty and solving the model with NSGAII algortithm. Journal of advances in industrial engineering, 49, 55-68. 49. Salema, M. I. G., Póvoa, A. P. B., & Novais, A. Q. (2009). A strategic and tactical model for closed-loop supply chains. OR spectrum, 31(3), 573-599. Schmitt, A. J., Sun, S. A., Snyder, L. V., & Shen, Z. J. M. (2015). Centralization versus decentralization: Risk pooling, risk diversification, and supply chain disruptions. Omega, 52, 201-212. 50. Shi, J., Zhang, G., & Sha, J. (2011). Optimal production and pricing policy for a closed loop system. Resources, Conservation and Recycling, 55(6), 639-647. 51. Soleimani, H., Govindan, K., Saghafi, H., & Jafari, H. (2017). Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Computers & Industrial Engineering, 109, 191-203. 52. Soleimani, H., Seyyed-Esfahani, M., & Shirazi, M. A. (2013). Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm. The International Journal of Advanced Manufacturing Technology, 68(1-4), 917-931. 53. Van Landeghem, H., & Vanmaele, H. (2002). Robust planning: a new paradigm for demand chain planning. Journal of operations management, 20(6), 769-783. 54. Wang, F., Lai, X., & Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, 51(2), 262-269. | ||
|
آمار تعداد مشاهده مقاله: 3,121 تعداد دریافت فایل اصل مقاله: 2,641 |
||