ارائه یک مدل بهینه‌سازی استوار برای طراحی استراتژیک و عملیاتی زنجیره تأمین نفت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشگاه صنعتی شیراز.

2 دانشجوی کارشناسی ارشد، دانشگاه صنعتی شیراز.

چکیده

صنعت نفت در ساختار انرژی و اقتصاد جهانی سهم بسزایی دارد و برنامه‌ریزی سطوح استراتژیک و عملیاتی زنجیره تأمین آن با هدف ارتقای موقعیت رقابتی کشورها در سطح جهانی و توسعه اقتصادی صورت می‌گیرد. در این پژوهش یک مدل ریاضی برای طراحی زنجیره تأمین نفت خام با در­ نظر ­گرفتن مسائل مربوط به مکان‌یابی تسهیلات، تخصیص تقاضا، برنامه‌ریزی حمل‌ونقل و توزیع ارائه می‌شود. در مدل پیشنهادی، الزامات زیست‌محیطی مربوط به انتشار گازهای گلخانه‌ای در نظر گرفته خواهد شد و به‌موجب آن میزان انتشار گازهای گلخانه‌ای ناشی از حمل‌ونقل نفت نمی‌تواند از یک مقدار مشخص فراتر رود. نظر به اینکه در دنیای واقعی به ‌ندرت می‌توان مقدار دقیق پارامترها را مشخص کرد، عدم‌قطعیت پارامتر­های بودجه، ظرفیت حمل­ونقل، ظرفیت واحدهای بهره‌برداری، میزان صادرات، مقدار استخراج و تولید نفت خام، تقاضای محصولات پالایشگاهی و میزان تولید آن‌ها در مدل پیشنهادی لحاظ می‌شود. برای برخورد با عدم‌قطعیت موجود در پارامترهای مدل از رویکرد بهینه‌سازی استوار استفاده می‌شود. نتایج عددی کارایی مدل پیشنهادی را تأیید می‌کنند و نشان می‌دهند با افزایش سطح عدم‌قطعیت سودآوری کاهش می­یابد؛ اما می­توان با مهار عدم‌قطعیت پارامترها و مدیریت مناسب تولید و توزیع سودآوری زنجیره تأمین نفت را تضمین کرد.

کلیدواژه‌ها

موضوعات


1. Al-Qahtani, K., Elkamel, A., & Ponnambalam, K. (2008). Robust optimization for petrochemical network design under uncertainty. Industrial & Engineering Chemistry Research47(11), 3912-3919.
2. Amiri, M., Barzegar, M., & Niknamfar, A.M.(2016). An integrated production– distribution planning via a robust set-based optimization approach in a three-level supply chain. Journal of Industrial Management Perspective, 23, 9-28. (In Persian)
3. Assis, L.S., Camponogara,E., Menezes,B.C., &Grossmann, I.E. (2019). An MINLP formulation for integrating the operational management of crude oil supply. Computers and Chemical Engineering, 123, 110-125.
4. Attia, A.M., Ghaithan, A.M., & Duffuaa, S.O. (2019). A multi-objective optimization model for tactical planning of upstream oil & gas supply chains. Computers & Chemical Engineering128, 216-227.
5. Azadeh, A., Shafiee, F., Yazdanparast, R., Heydari, J., & Mohammadi Fathabad, A. (2017). Evolutionary multi-objective optimization of environmental indicators of integrated crude oil supply chain under uncertainty. Journal of Cleaner Production, 152, 195-311.
6. Barari, S., Agarwal, G., Zhang, W.J., Mahanty, B. & Tiwari, M.K. (2012). A decision framework for the analysis of green supply chain contracts: an evolutionary game approach. Expert Systems with Applications, 39, 2965–2976.
7. Beiranvand, H., Ghazanfari, M., Sahebi, H., & Pishvaee. M.S.  (2018). A robust crude oil supply chain design under uncertain demand and market price: A case study. Oil & Gas Science and Technology, 73, 66.
8. Ben-Tal, A., El-Ghaoui, L., & Nemirovski, A. (2009). Robust optimization, Princeton university press.
9. Boschetto, S.N., Felizari, L.C., Yamamoto, L., Magatão, L., Stebel, S. L., Neves-Jr, F., Arruda, L.V.R., Lüders, R., Ribas, P.C., & Bernardo, L.F.G. (2008). An integrated framework for operational scheduling of a real-world pipeline network. Computer Aided Chemical Engineering25, 259-264.
10. Brink, J., & Marx, S. (2013). Harvesting of Hartbeespoort Dam micro-algal biomass through sand filtration and solar drying. Fuel, 106, 67-71.
11. Carneiro, M.C., Ribas, G.P., & Hamacher, S. (2010). Risk management in the oil supply chain: a CVaR approach. Industrial & Engineering Chemistry Research49(7), 3286-3294.
12. Chang, C.T., & Chang, C.C. (2000). A linearization method for mixed 0–1 polynomial programs. Computers & Operations Research27(10), 1005-1016.
13. Daryanto, Y., Wee, H.M., &Astanti, R.D. (2019) Three-echelon supply chain model considering carbon emission and item deterioration. Transportation Research Part E:Logistics and Transportation Review, 122, 368–383.
14. Emadabadi, A.A., Teimoury, E. & Pishvaee, M.S. (2019). Design of multi-periodical and multi-product supply chain network with regard to disruption of facilities and communication paths)Case study: subscription plan for publications(.Journal of Industrial Management Perspective, 35, 135-163. (In Persian)
15. Farahani, M., & Rahmani, D. (2017). Production and distribution planning in petroleum supply chains regarding the impacts of gas injection and swap. Energy, 141, 991-1003.
16. Feizollahi, S., Soltanpanah, H., Farughi, H., & Rahimzadeh, A. (2019). Development of multi objective multi period closed-loop supply chain network model considering uncertain demand and capacity. Journal of Industrial Management Perspective, 32, 61-95. (In Persian)
17. Fuselli, D., Angelis, F., Boaro, M., Squartini, S., Wei, Q., Liu, D., & Piazza, F. (2013). Action dependent heuristic dynamic programming for home energy resource scheduling. International Journal of Electrical Power & Energy Systems, 48, 148-160.
18. Ghaithan, A.M., Attia, A., & Duffuaa, S.O. (2017). Multi-objective Optimization Model for a Downstream Oil and Gas Supply Chain. Applied Mathematical Modelling, 52, 689-708.
19. Gupta, V., & Grossmann, I.E. (2012). An efficient multiperiod MINLP model for optimal planning of offshore oil and gas field infrastructure. Industrial & Engineering Chemistry Research51(19), 6823-6840.
20. Habibi, A., Delshad, M. & Bastami, A. (2009). Investigating the activities of oil engineering in the field of nanotechnology. Nanothechnology, 10(147), 12-15. (In Persian)
21. Hasani, A., Zegordi, S.H., & Nikbakhsh, E. (2012). Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty. International Journal of Production Research50(16), 4649-4669.
22. Herran, A., De la Cruz, J., & De Andres, B. (2010). A mathematical model for planning transportation of multiple petroleum products in a multi-pipeline system.  Computers & Chemical Engineering, 34(3), 401-413.
23. Ho, C. (1989). Evaluating the impact of operating environments on MRP system nervousness. International Journal of Production Research, 27, 1115–1135.
24. Lima, C., Relvas, S., & Barbosa-Póvoa, A.P.F.D. (2016). Downstream oil supply chain management: A critical review and future directions. Computers & Chemical Engineering92, 78-92.
25. Lima, C., Relvas, S., & Barbosa-Povoa, A. (2017). Stochastic programming approach for the optimal tactical planning of the downstream oil supply chain. Computers & Chemical Engineering, 108, 314-336.
26. Manouchehri, S., Tajdin, A. & Shirazi, B. (2019). Robust integrated optimization for green closed loop supply chain. Journal of Industrial Management Perspective, 35, 55-85. (In Persian)
27. Mirhosseini, M., Sharifi, F., & Sedaghat, A. (2011). Assessing the wind energy potential locations in province of Semnan in Iran. Renewable and Sustainable Energy Reviews, 15(1), 449-459.
28. Mohammed, F., Selim, S.Z., Hassan, A., & Syed, M.N. (2017). Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transportation Research Part D: Transport and Environment51, 146-172.
29. Oliveira, F., Gupta, V., Hamacher, S., & Grossmann, I.E. (2013). A Lagrangean decomposition approach for oil supply chain investment planning under uncertainty with risk considerations. Computers & Chemical Engineering, 50, 184-195.
30. Oliveira, F., Grossmann, I.E., & Hamacher, S. (2014). Accelerating benders stochastic decomposition for the optimization under uncertainty of the petroleum product supply chain. Computers & Operations Research, 49, 47-58.
31. Papi, A., Pishvaee, M.S., Jabbarzadeh, A. & Ghaderi, S.F. (2018). Optimal crude oil supply chain planning and oilfield development under uncertainty: Case study of the National Iranian South Oil Company. Quarterly Energy Economics Review, 58, 27-64. (In Persian)
32. 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.
33. Ribas, G.P., Hamacher, S., & Street, A. (2010). Optimization under uncertainty of the integrated oil supply chain using stochastic and robust programming.  International Transaction in Operational Research, 17(6), 777–796.
34. Sahebi, H., Nickel, S., & Ashayeri, J. (2014). Environmentally conscious design of upstream crude oil supply chain. Industrial & Engineering Chemistry Research53(28), 11501-11511.
35. Tarhan, B., Grossmann, I.E., & Goel, V. (2009). Stochastic programming approach for the planning of offshore oil or gas field infrastructure under decision-dependent uncertainty.  Industrial & Engineering Chemistry Research, 48(6), 3078-3097.
36. Vahdani, B., Tavakkoli-Moghaddam, R., Modarres, M., & Baboli, A. (2012). Reliable design of a forward/reverse logistics network under uncertainty: a robust-M/M/c queuing model. Transportation Research Part E: Logistics and Transportation Review48(6), 1152-1168.
37. Wang, B., Liang, Y., Zheng, T., Yuan, M., & Zhang, H. (2019). Optimisation of a downstream oil supply chain with new pipeline route planning. Chemical Engineering Research and Design, 145, 300-313.
38. Yuan, M., Zhang, H., Wang, B., Huang, L., Fang, K., & Liang, Y. (2020). Downstream oil supply security in China: Policy implications from quantifying the impact of oil import disruption. Energy Policy136, 111077.
39. Zhou, X., Zhang, H., Xin, S., Yan, Y., Long, Y., Yuan, M., & Liang, Y. (2020). Future scenario of China’s downstream oil supply chain: Low carbon-oriented optimization for the design of planned multi-product pipelines. Journal of Cleaner Production244, 118866.
42. https://niordc.ir/index.aspx?pageid=455&p=1