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

Jindai Zhang and Jinlou Zhao
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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

Energies, 2022, vol. 15, issue 12, 1-19

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.

Keywords: sequential optimization; prediction-driven modeling; refined oil price forecasting; market demand forecasting; production-sales-stock decision-making (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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