A Knowledge Transfer Based Scheduling Algorithm for Large-Scale Refinery Production

Decomposition algorithms for large-scale refinery scheduling problems commonly adopt the spatially dividing method. When the scheduling horizon further enlarges, the sizes of the resulting subproblems exponentially grow, leading to the performance degradation of the decomposition algorithms. In this article, to solve this issue about subproblem size caused by the long-time scheduling horizon, a knowledge transfer based algorithm is proposed, where the size of subproblems is constant and a novel concept of product flowrate is introduced to transfer operation knowledge from tractable small-scale short-time problems to intractable large-scale long-time problems to quickly obtain a satisfactory solution for large-scale problems. Experimental results show that the proposed algorithm can achieve better solutions within 10−44 s for large-scale refinery scheduling problems with 50−200 time slots (the existing model is with less than 30 time slots), compared to the CPLEX solver with a running time of 3600 s and the existing evolutionary algorithm with a running time of 100−500 s. The results also show that, at present, the heuristic method (the essence of the proposed algorithm) is the main tool to solve large-scale combinatorial optimization problems, and even in the CPLEX solver, the heuristic is also the most active part.