Crude oil refinery scheduling: Addressing a real-world multiobjective problem through genetic programming and dominance-based approaches

This study presents the crude oil scheduling problem with four objectives divided in two different levels of importance. It comes from a real refinery where the scheduling starts on the offloading of ships, encompasses terminal and refinery tanks, a crude pipeline, and finishes on the output streams of the crude distillation units. We propose a new approach for the Quantum-Inspired Grammar-based Linear Genetic Programming (QIGLGP) evolutionary algorithm to handle the multiple objectives of the problem using the nondominance concept. The modifications are concentrated on the population updating and sorting steps of QIGLGP. We tackle difference of importance among the objectives using the principle of violation of constraints. The problem constraints define if an instruction will or not be executed but do not affect the violation equation of the objectives. The individuals which have objective values under a pre-defined upper limit are better ranked. Results from five scenarios showed that the proposed model was able to significantly increase the percentage of runs with acceptable solutions, achieving success ratio of 100% in 3 cases and over 70% in 2 other ones. They also show that the Pareto front of these accepted runs contains a set of non-dominated solutions that could be analyzed by the decision maker for his a posteriori decision.