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Qianqian Duan1, Genke Yang1, Guanglin Xu2

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This study introduces a new method for scheduling refinery crude oil operations under demand uncertainty. The approach effectively reduces operational costs by considering demand correlations, offering a more practical optimization strategy.

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Area of Science:

  • Operations Research
  • Stochastic Optimization
  • Petroleum Engineering

Background:

  • Crude oil refinery scheduling is complex, often facing demand uncertainty.
  • Existing deterministic models lack practicality in real-world scenarios with unpredictable demand.
  • Stochastic models offer a more realistic approach to optimizing refinery operations.

Purpose of the Study:

  • To develop an approximation method for scheduling refinery crude oil operations considering demand uncertainty.
  • To address the nonlinearity of joint chance constraints in stochastic models.
  • To improve the tractability and efficiency of optimizing crude oil operations.

Main Methods:

  • Modeling demand uncertainty using random variables with joint multivariate distributions and correlation structures.
  • Employing joint chance constraints to specify product demand satisfaction levels.
  • Utilizing a relax-and-tight technique to approximate nonlinear joint chance constraints with parameterized linear constraints for iterative solving.

Main Results:

  • The proposed approximation method effectively transforms complex nonlinear constraints into manageable linear ones.
  • Case studies demonstrate the practical application and effectiveness of the developed method.
  • Significant reduction in operation costs was observed when demand correlation was considered.

Conclusions:

  • The developed approximation method provides a practical and efficient approach for optimizing crude oil refinery scheduling under demand uncertainty.
  • Considering demand correlation in stochastic models leads to more effective cost reductions.
  • The relax-and-tight technique offers a viable solution for handling complex nonlinear constraints in optimization problems.