Optimal schedule generation for single-channel crude transfer using a multi-model approach

Abstract

This paper presents three techniques for scheduling for crude transfer between a port and a refinery on a single pipeline in the presence of stringent flow constraints. The three techniques are based on metaheuristics (business rules), mixed integer linear programming and reinforcement learning. In addition to comparing the three techniques, we also demonstrate how knowledge gleaned from one technique (in our case, the metaheuristics) can be used to design an algorithm based on another technique (in our case, reinforcement learning). A novel feature of our approach to reinforcement learning, in particular, is the use of low-fidelity, reduced-order simulators for training the scheduler and supporting it with a post-processor based either on business rules or on integer programming for ensuring compatibility with the constraints. The set of constraints considered here includes temporal restrictions on the use of the pipeline, flow constraints in the tanks that feed the column distillation units in the refinery, and the need to ensure a certain minimum residence time for crude in a given tank for dewatering.