Stochastic optimization based on a novel scenario generation method for midstream and downstream petrochemical supply chain

Chinese Journal of Chemical Engineering - Tập 28 - Trang 815-823 - 2020
Peixian Zang1, Guoming Sun1, Yongming Zhao1, Yiqing Luo1, Xigang Yuan1
1State Key Laboratory of Chemical Engineering, Collaborative Innovation of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China

Tài liệu tham khảo

Handfield, 1999 Sahebi, 2014, Strategic and tactical mathematical programming models within the crude oil supply chain context — A review, Comput. Chem. Eng., 68, 56, 10.1016/j.compchemeng.2014.05.008 Lima, 2018, Stochastic programming approach for the optimal tactical planning of the downstream oil supply chain, Comput. Chem. Eng., 108, 314, 10.1016/j.compchemeng.2017.09.012 Kim, 2008, An integrated model of supply network and production planning for multiple fuel products of multi-site refineries, Comput. Chem. Eng., 32, 2529, 10.1016/j.compchemeng.2007.07.013 Andersen, 2013, Multiscale strategic planning model for the design of integrated ethanol and gasoline supply chain, AIChE J., 59, 4655, 10.1002/aic.14229 Fernandes, 2014, Collaborative design and tactical planning of downstream petroleum supply chains, Ind. Eng. Chem. Res., 53, 17155, 10.1021/ie500884k Ben-Tal, 1999, Robust solutions of uncertain linear programs, Oper. Res. Lett., 25, 1, 10.1016/S0167-6377(99)00016-4 LUO, 2009, A strategy for the integration of production planning and scheduling in refineries under uncertainty, Chin. J. Chem. Eng., 17, 113, 10.1016/S1004-9541(09)60042-2 Gassmann, 2005, On stages and consistency checks in stochastic programming, Oper. Res. Lett., 33, 171, 10.1016/j.orl.2004.04.013 You, 2008, Design of responsive supply chains under demand uncertainty, Comput. Chem. Eng., 32, 3090, 10.1016/j.compchemeng.2008.05.004 Herrera, 1995, Three models of fuzzy integer linear programming, Eur. J. Oper. Res., 83, 581, 10.1016/0377-2217(93)E0338-X Liu, 1997, Process planning in a fuzzy environment, Eur. J. Oper. Res., 100, 142, 10.1016/S0377-2217(96)00025-2 Birge, 2011, Introduction to Stochastic Programming, 10.1007/978-1-4614-0237-4 Pongsakdi, 2006, Financial risk management in the planning of refinery operations, Int. J. Prod. Econ., 103, 64, 10.1016/j.ijpe.2005.04.007 Khor, 2008, Two-stage stochastic programming with fixed recourse via scenario planning with economic and operational risk management for petroleum refinery planning under uncertainty, Chem. Eng. Process. Process Intensif., 47, 1744, 10.1016/j.cep.2007.09.016 Azadeh, 2017, Evolutionary multi-objective optimization of environmental indicators of integrated crude oil supply chain under uncertainty, J. Clean. Prod., 152, 295, 10.1016/j.jclepro.2017.03.105 Lima, 2016, Downstream oil supply chain management: A critical review and future directions, Comput. Chem. Eng., 92, 78, 10.1016/j.compchemeng.2016.05.002 Al-Othman, 2008, Supply chain optimization of petroleum organization under uncertainty in market demands and prices, Eur. J. Oper. Res., 189, 822, 10.1016/j.ejor.2006.06.081 Leiras, 2013, Tactical and operational planning of multirefinery networks under uncertainty: An iterative integration approach, Ind. Eng. Chem. Res., 52, 8507, 10.1021/ie302835n Zhao, 2017, An optimization model for tactical decision-making level and uncertainty risk management in petroleum supply chain, Huagong Xuebao/CIESC J., 68, 746 Beale, 1955, On minimizing a convex function subject to linear inequalities, J. R. Stat. Soc. Ser. B, 17, 173 Danzig, 1955, Linear programming under uncertainty, Manag. Sci., 1, 197, 10.1287/mnsc.1.3-4.197 Gebreslassie, 2012, Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a comparison between CVaR and downside risk, AIChE J., 58, 2155, 10.1002/aic.13844 Wang, 2018, Determinants analysis of carbon dioxide emissions in passenger and freight transportation sectors in China, Struct. Chang. Econ. Dyn., 47, 127, 10.1016/j.strueco.2018.08.003 Jiang, 2013, Estimation and analysis of carbon dioxide emissions in refineries, Mod. Chem. Ind., 2 Nasab, 2016, Designing an integrated model for a multi-period, multi-echelon and multi-product petroleum supply chain, Energy., 114, 708, 10.1016/j.energy.2016.07.140 Yousefi-Babadi, 2017, Designing a reliable multi-objective queuing model of a petrochemical supply chain network under uncertainty: A case study, Comput. Chem. Eng., 100, 177, 10.1016/j.compchemeng.2016.12.012 Kostin, 2018, Optimization-based approach for maximizing profitability of bioethanol supply chain in Brazil, Comput. Chem. Eng., 115, 121, 10.1016/j.compchemeng.2018.04.001 Sepulveda, 2014, The use of global sensitivity analysis for improving processes: Applications to mineral processing, Comput. Chem. Eng., 66, 221, 10.1016/j.compchemeng.2014.01.008 Lucay, 2019, Improving milling operation using uncertainty and global sensitivity analyses, Miner. Eng., 131, 249, 10.1016/j.mineng.2018.11.020 Sobol, 1993, Sensitivity estimates for nonlinear mathematical models, Mathematical Modeling and Computational Experiment, 4, 407 Jansen, 1999, Analysis of variance designs for model output, Comput. Phys. Commun., 117, 35, 10.1016/S0010-4655(98)00154-4 Lilburne, 2009, Sensitivity analysis of spatial models, Int. J. Geogr. Inf. Sci., 23, 151, 10.1080/13658810802094995