A knowledge transfer-based co-evolutionary algorithm for dynamic large-scale crude oil scheduling

Abstract

Confronted with the intricacies arising from expanding production scales and numerous uncertain events, large-scale dynamic scheduling has emerged as a practical approach in industries. However, within the framework of predictive–reactive scheduling, few works have explored the trade-off between the optimality and stability of the solutions generated. To fill this gap, this paper investigates dynamic scheduling applied to the large-scale crude oil scheduling problem, which is critical for the petroleum industry. Specifically, we develop a model that incorporates multiple sources of uncertainty: vessel arrival delays; tank malfunctions; fluctuations in feed flowrates to the distillation columns; and intermediate product demand. To solve this problem effectively, a knowledge transfer-based cooperative co-evolutionary algorithm (KT-CCEA) is proposed, where specific knowledge from the predictive stage is transferred to the reactive stage. Specifically, multiple subpopulations are generated around distinct reactive points, evolving across diverse search dimensions, to balance optimality and stability. Discretized differential operators are designed to overcome the limitations of standard evolutionary operators in integer-coded matrix representation. Empirical results over a set of 15 benchmark instances validate the superiority of the proposed KT-CCEA over four state-of-the-art algorithms (RSCO-SAGA, VLCEA, DMDE, and RCI-PSO). Ablation experiments on seven algorithm variants further confirm the efficacy of its core components.