A Robust Optimization Model for the Strategic and Operational Design of the Oil Supply Chain

Document Type : Original Article


1 Assistant professor, Shiraz University of Technology.

2 M.Sc. student, Shiraz University of Technology.



The oil industry has a great share in the energy structure and the global economy, and the planning of strategic and operational levels of its supply chain is done with the objective of improving the competitive status of countries on the global level and economic development. In this paper, a mathematical model is presented for designing the crude oil supply chain through considering related facility location, demand allocation, transportation planning, and distribution. In the proposed model, environmental requirements for emitted greenhouse gas are considered such that the greenhouse gas emission from the transportation of oil may not be greater than a given limit. Since the exact values of parameters can rarely be determined in the real world, therefore, the uncertainty associated with parameters such as budget, transportation capacity, production units capacity, export volume, the amount of crude oil extraction and production, demand for refinery products and their production rate are considered in the proposed model. To handle the uncertainty of the model parameters, the robust optimization approach is applied. Numerical results verify the efficiency of the proposed model and show that the profitability of oil industry can be guaranteed by handling the uncertainties of parameters and appropriate production and distribution management.


Main Subjects

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 Perspective23, 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 Engineering123, 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 Production152, 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 Technology73, 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 Review122, 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 Systems48, 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 Modelling52, 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 Engineering34(3), 401-413.
23. Ho, C. (1989). Evaluating the impact of operating environments on MRP system nervousness. International Journal of Production Research27, 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 Engineering108, 314-336.
26. Manouchehri, S., Tajdin, A. & Shirazi, B. (2019). Robust integrated optimization for green closed loop supply chain. Journal of Industrial Management Perspective35, 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 Reviews15(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 Research49, 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 Modelling35(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 Research48(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 Design145, 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.
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