Solve Crude Oil Scheduling Problems by Using Quantum-Classical Hybrid Algorithms

TL;DR

This paper introduces a hybrid quantum-classical approach using Benders Decomposition to efficiently solve complex, large-scale crude oil scheduling problems that are NP-hard, outperforming traditional heuristics and solvers.

Contribution

It develops a novel hybrid framework that reformulates the discrete scheduling problem as a QUBO for quantum solving, enhancing scalability and solution quality.

Findings

Reduces operating costs by approximately 73--80%.

Achieves computational speeds comparable to Gurobi.

Outperforms traditional metaheuristics like Genetic Algorithms and Tabu Search.

Abstract

The optimization of front-end crude oil scheduling is a critical determinant of refinery profitability and operational stability. However, the coupling of discrete logistics events (e.g., vessel berthing) with continuous material flows (e.g., pipeline transfers) renders this problem an NP-hard Mixed-Integer Linear Programming (MILP) challenge, often intractable for classical solvers at industrial scales. This study proposes a novel hybrid quantum-classical framework to address these computational bottlenecks. We employ Benders Decomposition to decouple the monolithic model into a discrete Master Problem (MP) and a continuous Subproblem (SP). To exploit the search capabilities of quantum computing, the MP is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) model and solved via a hybrid quantum solver, while the SP enforces mass balance and quality constraints through iterative optimality and feasibility cuts. Extensive experiments on 15 multi-scale instances demonstrate that the proposed framework significantly outperforms traditional metaheuristics (e.g., Genetic Algorithms, Tabu Search), reducing total operating costs by approximately 73--80% and achieving computational speeds comparable to state-of-the-art commercial solvers (Gurobi). By effectively leveraging global optimality cuts, the method overcomes the tendency of heuristic approaches to trap in local optima, providing a robust and scalable solution for complex refinery logistics.