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

doi:10.1016/j.cjche.2019.06.008

Chinese Journal of Chemical Engineering ›› 2020, Vol. 28 ›› Issue (3): 815-823.DOI: 10.1016/j.cjche.2019.06.008

• Process Systems Engineering and Process Safety • Previous Articles Next Articles

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

Peixian Zang, Guoming Sun, Yongming Zhao, Yiqing Luo, Xigang Yuan

State Key Laboratory of Chemical Engineering, Collaborative Innovation of Chemical Science and Engineering(Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China

Received:2019-01-12 Revised:2019-06-16 Online:2020-06-11 Published:2020-03-28
Contact: Xigang Yuan
Supported by:
We gratefully acknowledge the support from the National Natural Science Foundation of China (No. 21676183), and State Key Laboratory of Chemical Engineering, Collaborative Innovation of Chemical Science and Engineering (Tianjin).

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

Peixian Zang, Guoming Sun, Yongming Zhao, Yiqing Luo, Xigang Yuan

State Key Laboratory of Chemical Engineering, Collaborative Innovation of Chemical Science and Engineering(Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China

通讯作者: Xigang Yuan
基金资助:
We gratefully acknowledge the support from the National Natural Science Foundation of China (No. 21676183), and State Key Laboratory of Chemical Engineering, Collaborative Innovation of Chemical Science and Engineering (Tianjin).

Abstract

Abstract: A two-stage mixed integer linear programming model (MILP) incorporating a novel method of stochastic scenario generation was proposed in order to optimize the economic performance of the synergistic combination of midstream and downstream petrochemical supply chain. The uncertainty nature of the problem intrigued the parameter estimation, which was conducted through discretizing the assumed probability distribution of the stochastic parameters. The modeling framework was adapted into a real-world scale of petrochemical enterprise and fed into optimization computations. Comparisons between the deterministic model and stochastic model were discussed, and the influences of the cost components on the overall profit were analyzed. The computational results demonstrated the rationality of using reasonable numbers of scenarios to approximate the stochastic optimization problem.

Key words: Petroleum, Two-stage, Optimization, Scenario generation, Parameter estimation

摘要： A two-stage mixed integer linear programming model (MILP) incorporating a novel method of stochastic scenario generation was proposed in order to optimize the economic performance of the synergistic combination of midstream and downstream petrochemical supply chain. The uncertainty nature of the problem intrigued the parameter estimation, which was conducted through discretizing the assumed probability distribution of the stochastic parameters. The modeling framework was adapted into a real-world scale of petrochemical enterprise and fed into optimization computations. Comparisons between the deterministic model and stochastic model were discussed, and the influences of the cost components on the overall profit were analyzed. The computational results demonstrated the rationality of using reasonable numbers of scenarios to approximate the stochastic optimization problem.

关键词: Petroleum, Two-stage, Optimization, Scenario generation, Parameter estimation

Peixian Zang, Guoming Sun, Yongming Zhao, Yiqing Luo, Xigang Yuan. Stochastic optimization based on a novel scenario generation method for midstream and downstream petrochemical supply chain[J]. Chinese Journal of Chemical Engineering, 2020, 28(3): 815-823.

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

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

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