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Prediction-Driven Sequential Optimization for Refined Oil Production-Sales-Stock Decision-Making

Author

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  • Jindai Zhang

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

  • Jinlou Zhao

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

Abstract

This paper proposes a prediction-driven sequential optimization methodology for joint decision-making problems of production-sales-stock in refined oil enterprises. In the proposed prediction-driven sequential optimization methodology, three dynamic nonlinear programming models are first constructed to model the production-sales-stock decision-making problems in refined oil enterprises. Then, the analytical solutions to sequential optimization for production-sales-stock decision-making issues are presented by using the inverse inference method in dynamic programming. Finally, the impact of price and demand prediction of refined oil products on sequential optimization for production-sales-stock decision-making are analyzed using a numerical analysis method. Numerical results demonstrated the significant impact of forecasting results of price and demand of refined oil products on sequential optimization decision-making, indicating that the prediction-driven sequential optimization methodology can be used as an effective tool for joint decision-making of production-sales-stock.

Suggested Citation

  • Jindai Zhang & Jinlou Zhao, 2022. "Prediction-Driven Sequential Optimization for Refined Oil Production-Sales-Stock Decision-Making," Energies, MDPI, vol. 15(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4222-:d:834143
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    References listed on IDEAS

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