4.6 Article

Multi-objective optimization of crude oil-supply portfolio based on interval prediction data

Journal

ANNALS OF OPERATIONS RESEARCH
Volume 309, Issue 2, Pages 611-639

Publisher

SPRINGER
DOI: 10.1007/s10479-020-03701-w

Keywords

Energy supply security; Country risk; Decomposition hybrid methodology; Interval prediction; Multi-objective programming

Funding

  1. National Natural Science Foundation of China [71771206, 71425002]
  2. President's Youth Foundation of the Institutes of Science and Development, CAS [Y7X111Q01]

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This paper investigates the optimization of crude oil-supply composition and introduces the decomposition hybrid interval prediction method and a multi-objective programming model. By setting optimization parameters and risk preference factors, the study achieves the minimum cost and risk of importing crude oil. The results show that the decomposition hybrid prediction method outperforms single prediction methods, and increasing the optimization parameter significantly reduces costs and risks. The total cost of imported crude oil fluctuates sharply, while the total risk decreases with the increase of risk preference factors. The fluctuation of price and risk adjustment factors affects the optimization scheme for oil-supply portfolio.
The optimization of crude oil-supply portfolio is a hot research issue in energy security, which is closely related to the implementation of national strategy and development of economy. Forecasting the demand of crude oil is the basis for portfolio optimization. Therefore, this paper innovatively introduces the decomposition hybrid interval prediction method and proposes a multi-objective programming model in order to provide decision-making support for the formulation of crude oil-supply portfolio scheme. Under the constraints of volume, price and risk, the minimum cost and risk of importing crude oil are achieved. Furthermore, by introducing optimization parameters and risk preference factors, and setting different scenarios for numerical simulation, the results show that (1) decomposition hybrid prediction methods perform better than single prediction methods. (2) As the optimization parameter increases, costs and risks are significantly decreased. Decision-makers can set large parameters to achieve significant optimization of the objective function. (3) The total cost of imported crude oil fluctuates sharply, while the total risk decreases with the increase of risk preference factors under the different scenarios. (4) The fluctuation of price and risk adjustment factors will cause the change of oil-supply portfolio optimization scheme.

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