Solve Crude Oil Scheduling Problems by Using Quantum-Classical Hybrid Algorithms
- URL: http://arxiv.org/abs/2604.26459v1
- Date: Wed, 29 Apr 2026 09:15:06 GMT
- Title: Solve Crude Oil Scheduling Problems by Using Quantum-Classical Hybrid Algorithms
- Abstract summary: Front-end crude oil scheduling is a critical determinant of refinery profitability and operational stability.<n>This study proposes a novel hybrid quantum-classical framework to address these computational bottlenecks.
- Score: 21.36249008423304
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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