Crude Oil Scheduling for Coastal Refineries with Long-Distance Pipelines: Application of Mixed-Integer Programming and Supervised Learning

The transportation of crude oil in coastal refineries
via long-distance
pipelines is a crucial step in refinery scheduling plans. However,
existing studies oversimplify the issue by assuming either instantaneous
transmission of crude oil or fixed transportation times in long-distance
pipelines, disregarding the flow rate fluctuations of crude oil in
these pipelines. This oversimplification fails to capture significant
transport delays and crude holdups, which can significantly deteriorate
the operations in coastal refineries. To address this issue, we study
long-distance pipeline transportation under a discrete-time model.
We propose a mixed-integer programming model which can accurately
describe the nonuniform speed transportation process, and effectively
handle refinery scheduling problems involving long-distance pipelines.
In addition, we employ a supervised learning method to construct an
offline predictor which can reduce the online solution time by minimizing
the combinatorial search among discrete variables. In our numerical
experiments, we illustrate the proposed model using several real-world
coastal refineries as examples. The results show that the model can
accurately describe the realistic transportation characteristics of
long-distance pipelines, and the generated scheduling scheme can avoid
frequent pipeline switching in storage tanks, which can eventually
lead to an enhancement of overall refinery performance.