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With the scaling up of crude oil scheduling in modern refineries, large-scalecrude oil scheduling problems (LSCOSPs) emerge with thousands of binaryvariables and non-linear constraints, which are challenging to be optimized bytraditional optimization methods. To solve LSCOSPs, we take the practical crudeoil scheduling from a marine-access refinery as an example and start withmodeling LSCOSPs from crude unloading, transportation, crude distillation unitprocessing, and inventory management of intermediate products. On the basis ofthe proposed model, a dual-stage evolutionary algorithm driven by heuristicrules (denoted by DSEA/HR) is developed, where the dual-stage search mechanismconsists of global search and local refinement. In the global search stage, wedevise several heuristic rules based on the empirical operating knowledge togenerate a well-performing initial population and accelerate convergence in themixed variables space. In the local refinement stage, a repair strategy isproposed to move the infeasible solutions towards feasible regions by furtheroptimizing the local continuous variables. During the whole evolutionaryprocess, the proposed dual-stage framework plays a crucial role in balancingexploration and exploitation. Experimental results have shown that DSEA/HRoutperforms the state-of-the-art and widely-used mathematical programmingmethods and metaheuristic algorithms on LSCOSP instances within a reasonabletime.

Knowledge-Assisted Dual-Stage Evolutionary Optimization of Large-Scale Crude Oil Scheduling