Large-scale crude oil scheduling: A framework of hybrid optimization based on plan decomposition

Abstract

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 nonlinear 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.

Data-Driven Heuristic Optimization for Complex Large-Scale Crude Oil Operation Scheduling

“…In the literature, a specially designed heuristic approach for the SCOO problem has not been found. For much smaller problems, specific cases, with strong assumptions and by ignoring some of the constraints, are presented [2,3,8,12,13,18].…”

“…The scheduling of crude oil operations (SCOO) problem is defined by the combination of both discrete events and continuous processes. The fact that the SCOO problem includes both discrete events and continuous processes makes it an interesting problem in terms of both theoretical research and practical application [3]. The combinatorial structure of this problem causes the high complexity of the problem [4] and allows for each decision to create a different probability tree, and therefore, this makes the problem an NP-Hard problem [5].…”