Scheduling crude oil unloading, storage, and processing

This paper presents a solution algorithm and effective mathematical formulations for short-term scheduling of crude oil unloading, storage, and processing with multiple oil types, multiple berths, and multiple processing units, The problem can be formulated as a mixed-integer nonlinear programming (MINLP) model. However, solving a MINLP model directly results in inconsistency in solution quality and time. This paper proposes a solution algorithm that iteratively solves two mixed-integer linear programming (MIP) models and a nonlinear programming (NLP) model, resulting in better quality, stability, and efficiency than solving the MINLP model directly. The proposed MIP formulations disaggregate the three-dimensional binary decision variables in traditional formulations using two-dimensional binary variables, significantly reduce the total number of binary variables required, and hence improve the overall solution time. For handling large-scale problems, heuristics are included in the formulations to further reduce the solution time. Finally, an industrial case study is used to demonstrate the effectiveness of the proposed formulation and solution algorithm.