Robust LNG sales planning under demand uncertainty: A data-driven goal-oriented approach

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.