  With the scaling up of crude oil scheduling in modern refineries, large-scale
crude oil scheduling problems (LSCOSPs) emerge with thousands of binary
variables and non-linear constraints, which are challenging to be optimized by
traditional optimization methods. To solve LSCOSPs, we take the practical crude
oil scheduling from a marine-access refinery as an example and start with
modeling LSCOSPs from crude unloading, transportation, crude distillation unit
processing, and inventory management of intermediate products. On the basis of
the proposed model, a dual-stage evolutionary algorithm driven by heuristic
rules (denoted by DSEA/HR) is developed, where the dual-stage search mechanism
consists of global search and local refinement. In the global search stage, we
devise several heuristic rules based on the empirical operating knowledge to
generate a well-performing initial population and accelerate convergence in the
mixed variables space. In the local refinement stage, a repair strategy is
proposed to move the infeasible solutions towards feasible regions by further
optimizing the local continuous variables. During the whole evolutionary
process, the proposed dual-stage framework plays a crucial role in balancing
exploration and exploitation. Experimental results have shown that DSEA/HR
outperforms the state-of-the-art and widely-used mathematical programming
methods and metaheuristic algorithms on LSCOSP instances within a reasonable
time.
