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

doi:10.1016/j.cjche.2019.06.008

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

Process Systems Engineering and Process Safety | Updated：2026-01-06

- 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
- 中国化学工程学报（英文版） 2020年28卷第3期页码：815-823
- Affiliations：
  
  State Key Laboratory of Chemical Engineering, Collaborative Innovation of Chemical Science and Engineering(Tianjin), School of Chemical Engineering and Technology, Tianjin University,Tianjin,China,300072
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2019.06.008
  中图分类号：
- 纸质出版：2020
- Accepted：
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Peixian Zang, Guoming Sun, Yongming Zhao, 等. Stochastic optimization based on a novel scenario generation method for midstream and downstream petrochemical supply chain[J]. 中国化学工程学报（英文版）, 2020,28(3):815-823.

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.
Peixian Zang, Guoming Sun, Yongming Zhao, 等. Stochastic optimization based on a novel scenario generation method for midstream and downstream petrochemical supply chain[J]. 中国化学工程学报（英文版）, 2020,28(3):815-823. DOI： 10.1016/j.cjche.2019.06.008.

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. DOI： 10.1016/j.cjche.2019.06.008.

摘要

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.

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