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• W4387581034 abstract "This paper addresses the liquefied natural gas (LNG) sales planning problem over a pipeline network with a focus on uncertain demands. Generically, the total profit is maximized by seeking optimal transportation and inventory decisions, and robust optimization (RO) has been a viable decision-making strategy to this end, which is however known to suffer from over-conservatism. To circumvent this, a new goal-oriented data-driven RO approach is proposed. First, we adopt data-driven polytopic uncertainty sets based on kernel learning, which yields a compact high-density region from data and assures tractability of RO problems. Based on this, a new goal-oriented RO formulation is put forward to satisfy to the greatest extent the target profit while tolerating slight constraint violations. In contrast to traditional min–max RO scheme, the proposed scheme not only ensures a flexible trade-off but also yields parameters with clear interpretation. The resulting optimization problem turns out to be equivalent to a mixed-integer linear program that can be effectively handled using off-the-shelf solvers. We illustrate the merit of the proposed method in satisfying a prescribed goal with optimized robustness by means of a case study." @default.
• W4387581034 created "2023-10-13" @default.
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• W4387581034 date "2023-12-01" @default.
• W4387581034 modified "2023-10-18" @default.
• W4387581034 title "Robust LNG sales planning under demand uncertainty: A data-driven goal-oriented approach" @default.
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• W4387581034 doi "https://doi.org/10.1016/j.dche.2023.100130" @default.
• W4387581034 hasPublicationYear "2023" @default.
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