Quantum-Inspired Genetic Programming Algorithm for the Crude Oil Scheduling of a Real-World Refinery

Refinery scheduling comprises a group of decisions that aims to optimize asset allocation, activity sequencing, and the time-related realization of those activities. This scheduling must achieve multiple objectives while considering different types of constraints. Uninterrupted processing unit operation, on-time crude oil batch receipts, and tank switchover minimization coexist in the everyday reasoning of a scheduler. However, it is not usual that works encompassing many operational aspects, such as multiple operational objectives, settling time, and an unlimited number of crudes, to blend in any tank. This article proposes a new algorithm that integrates linear and grammar-guided genetic programming concepts with a quantum-inspired approach to create programs that represent a crude oil refinery scheduling solution. The fitness function comprises four objectives that guide the evolution based on importance predefined by the decision maker. We propose a success ratio to evaluate the algorithm performance considering 50 runs for each case. A final solution is considered a success if two more important objectives are optimized. We assessed our approach with five different scenarios of a real refinery and three of them achieved a 100% success ratio.