The oil and gas networks are overlapped because of the inclusion of associated gas in crude oil. This necessitates the integration and planning of oil and gas supply chain together. In recent years, hydrocarbon market has experienced high fluctuation in demands and prices which leads to considerable economic disruptions. Therefore, planning of oil and gas supply chain, considering market uncertainty is a significant area of research. In this regard, this study develops a multi-objective stochastic optimization model for tactical planning of downstream segment of oil and natural gas supply chain under uncertainty of price and demand of petroleum products. The proposed model was formulated based on a two-stage stochastic programming approach with a finite number of realizations. The proposed model helps to assess various trade-offs among the selected goals and guides decision maker(s) to effectively manage oil and natural gas supply chain. The applicability and the utility of the proposed model has been demonstrated using the case of Saudi Arabia oil and gas supply chain. The model is solved using the improved augmented ε-constraint algorithm. The impact of uncertainty of price and demand of petroleum products on the obtained results was investigated. The Value of Stochastic Solution (VSS) for total cost, total revenue, and service level reached a maximum of 12.6%, 0.4%, and 6.2% of wait-and see solutions, respectively. Therefore, the Value of the Stochastic Solution proved the importance of using stochastic programming approach over deterministic approach. In addition, the obtained results indicate that uncertainty in demand has higher impact on the oil and gas supply chain performance than the price.
Keywords: Oil and gas supply chain, optimization under uncertainty, tactical decision making, Pareto efficient solution, multi-objective optimization
@article{RO_2021__55_6_3427_0,
author = {Ghaithan, Ahmed M. and Attia, Ahmed M. and Duffuaa, Salih O.},
title = {A multi-objective model for an integrated oil and natural gas supply chain under uncertainty},
journal = {RAIRO. Operations Research},
pages = {3427--3446},
year = {2021},
publisher = {EDP-Sciences},
volume = {55},
number = {6},
doi = {10.1051/ro/2021158},
mrnumber = {4338794},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2021158/}
}
TY - JOUR AU - Ghaithan, Ahmed M. AU - Attia, Ahmed M. AU - Duffuaa, Salih O. TI - A multi-objective model for an integrated oil and natural gas supply chain under uncertainty JO - RAIRO. Operations Research PY - 2021 SP - 3427 EP - 3446 VL - 55 IS - 6 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2021158/ DO - 10.1051/ro/2021158 LA - en ID - RO_2021__55_6_3427_0 ER -
%0 Journal Article %A Ghaithan, Ahmed M. %A Attia, Ahmed M. %A Duffuaa, Salih O. %T A multi-objective model for an integrated oil and natural gas supply chain under uncertainty %J RAIRO. Operations Research %D 2021 %P 3427-3446 %V 55 %N 6 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2021158/ %R 10.1051/ro/2021158 %G en %F RO_2021__55_6_3427_0
Ghaithan, Ahmed M.; Attia, Ahmed M.; Duffuaa, Salih O. A multi-objective model for an integrated oil and natural gas supply chain under uncertainty. RAIRO. Operations Research, Tome 55 (2021) no. 6, pp. 3427-3446. doi: 10.1051/ro/2021158
[1] , , and , Supply chain optimization of petroleum organization under uncertainty in market demands and prices. Eur. J. Oper. Res. 189 (2008) 822–840. | MR | Zbl | DOI
[2] and , Multisite refinery and petrochemical network design: optimal integration and coordination. Ind. Eng. Chem. Res. 48 (2008) 814–826. | DOI
[3] and , Robust planning of multisite refinery networks: optimization under uncertainty. Comput. Chem. Eng. 34 (2010) 985–995. | DOI
[4] , , and , Impact of crude oil production on the petrochemical industry in Saudi Arabia. Energy 16 (1991) 1089–1099. | DOI
[5] , and , A multi-objective optimization model for tactical planning of upstream oil & gas supply chains. Comput. Chem. Eng. 128 (2019) 216–227. | DOI
[6] , and , Data on upstream segment of a hydrocarbon supply chain in Saudi Arabia. Data Brief 27 (2019) 104804. | DOI
[7] , and , A multi-objective fuzzy linear programming model for optimization of natural gas supply chain through a greenhouse gas reduction approach. J. Nat. Gas. Sci. Eng. 26 (2015) 702–710. | DOI
[8] , , , and , Optimum integrated design of crude oil supply chain by a unique mixed integer nonlinear programming model. Ind. Eng. Chem. Res. 56 (2017) 5734–5746. | DOI
[9] and , Introduction to Stochastic Programming. Springer Science & Business Media (2011). | MR | Zbl | DOI
[10] , and , Risk management in the oil supply chain: a CVaR approach. Ind. Eng. Chem. Res. 49 (2010) 3286–3294. | DOI
[11] and , Making Hard Decisions with Decision Tools Suite Update Edition. Cengage Learning, Pacific Grove, CA (2004).
[12] , and , Decision Making Under Uncertainty in Electricity Markets. Springer (2010). | MR | Zbl | DOI
[13] , Price elasticity of demand for crude oil: estimates for 23 countries. OPEC Rev. 27 (2003) 1–8. | DOI
[14] , Linear programming under uncertainty. In: Stochastic Programming.. Springer (2010) 1–11. | MR | Zbl
[15] , and , Planning logistics operations in the oil industry. J. Oper. Res. Soc. 51 (2000) 1271–1288. | Zbl | DOI
[16] , , and , A linear programming model to evaluate gas availability for vital industries in Saudi Arabia. J. Oper. Res. Soc. 43 (1992) 1035–1045. | DOI
[17] , and , CORO, a modeling and an algorithmic framework for oil supply, transformation and distribution optimization under uncertainty. Eur. J. Oper. Res. 114 (1999) 638–656. | Zbl | DOI
[18] , and , Downstream petroleum supply chain planning under uncertainty. In: Vol. 37 of Computer Aided Chemical Engineering (2015) 1889–1894. | DOI
[19] , An optimization model for operational planning and turnaround maintenance scheduling of oil and gas supply chain. Appl. Sci. 10 (2020) 7531. | DOI
[20] , and , Multi-objective optimization model for a downstream oil and gas supply chain. Appl. Math. Model. 52 (2017) 689–708. | MR | DOI
[21] and , Optimal network design and storage management in petroleum distribution network under uncertainty. Eng. Appl. Artif. Intell. 22 (2009) 796–807. | DOI
[22] , An interactive multiobjective model for the strategic maritime transportation of petroleum products: risk analysis and routing. Saf. Sci. 39 (2001) 19–29. | DOI
[23] , and , Stochastic refinery planning with risk management. Pet. Sci. Technol. 26 (2008) 1726–1740. | DOI
[24] , , and , Optimizing the supply chain of petrochemical products under uncertain operational and economical conditions. jdt 1 (2003).
[25] , , and , Optimizing the supply chain of a petrochemical company under uncertain operating and economic conditions. Ind. Eng. Chem. Res. 43 (2004) 63–73. | DOI
[26] , and , Strategic planning of integrated multirefinery networks: a robust optimization approach based on the degree of conservatism. Ind. Eng. Chem. Res. 49 (2010) 9970–9977. | DOI
[27] , , and , Literature review of oil refineries planning under uncertainty. Int. J. Oil Gas Coal Technol. 4 (2011) 156–173. | DOI
[28] , , and , Refinery planning under uncertainty. Ind. Eng. Chem. Res. 43 (2004) 6742–6755. | DOI
[29] , and , Two-stage stochastic programming for the refined oil secondary distribution with uncertain demand and limited inventory capacity. IEEE Access 8 (2020) 119487–119500. | DOI
[30] , and , Downstream oil supply chain management: a critical review and future directions. Comput. Chem. Eng. 92 (2016) 78–92. | DOI
[31] , and , Stochastic programming approach for the optimal tactical planning of the downstream oil supply chain. Comput. Chem. Eng. 108 (2018) 314–336. | DOI
[32] and , Two-stage stochastic model for petroleum supply chain from the perspective of carbon emission. In: Vol. 117 of Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science (2015) 926–930.
[33] , Ibm ilog cplex optimization studio. Version 12 (1987) 1987–2018.
[34] and , An improved version of the augmented -constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Appl. Math. Comput. 219 (2013) 9652–9669. | MR | Zbl
[35] , An operational planning model for petroleum products logistics under uncertainty. Appl. Math. Comput. 196 (2008) 744–751. | MR | Zbl
[36] , Energy to the World: The Story of Saudi ARAMCO, 1st edition. Vol. 2. Houston, Texas, USA (2011).
[37] and , Multiperiod optimization for production planning of petroleum refineries. Chem. Eng. Comm. 192 (2005) 62–88. | DOI
[38] and , Optimization of the petroleum product supply chain under uncertainty: a case study in northern brazil. Ind. Eng. Chem. Res. 51 (2012) 4279–4287. | DOI
[39] , , and , A Lagrangean decomposition approach for oil supply chain investment planning under uncertainty with risk considerations. Comput. Chem. Eng. 50 (2013) 184–195. | DOI
[40] , and , Optimization under uncertainty of the integrated oil supply chain using stochastic and robust programming. Int. Trans. Oper. Res. 17 (2010) 777–796. | MR | Zbl | DOI
[41] , and , Tactical planning of the oil supply chain: optimization under uncertainty. Pre-An XLIIISBPO (2011).
[42] , and , Strategic and tactical mathematical programming models within the crude oil supply chain context – A review. Comput. Chem. Eng. 68 (2014) 56–77. | DOI
[43] and , Surface Production Operations, Design of Oil Handling Systems and Facilities, 3rd edition. Vol. 1. Gulf Professional Publishing, Amsterdam, Boston, Houston, TX (2007).
[44] , and , Planning under demand and yield uncertainties in an oil supply chain. Ind. Eng. Chem. Res. 51 (2012) 814–834. | DOI
[45] , and , Supply chain optimization for refinery with considerations of operation mode changeover and yield fluctuations. Ind. Eng. Chem. Res. 49 (2010) 276–287. | DOI
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