Considering market’s diversified demand and transport economy, large volumes of various refined products commonly move down the pipeline in batches, which are pumped at pump stations and delivered to respective delivery stations. The integrate detailed scheduling optimization is a sophisticated problem due to the characteristics of multi-product pipelines, such as market-oriented, fluctuated demand, various processing technique and complicated hydraulic calculation during batch migration. The integrate detailed scheduling optimization has been widely studied during the last decade, however, most of them studied pipeline scheduling and pump scheduling separately. Besides, the proposed methods are mathematical models, whose computational efficiency greatly decreases in large-scale pipeline scheduling, let alone in the problems coupling with pump scheduling.
Aiming at this problem, this paper presents a novel depth-first searching approach based on flowrate ratio to deal with the detailed scheduling of operations in a multi-product pipeline with multiple pump stations. As for each single time interval, the proposed method decides an ideal flowrate ratio according to current status, then solves out the optimal flowrate that mostly conforms to the ideal ratio and satisfies all operational constraints, and finally updates information for next time interval. However, during the computational procedure, backtracking method would be adopted to modify the previous flowrate ratios and recalculate new flowrate when the actual delivered products are insufficient. Finally, a case tested on a Chinese real-world pipeline with 6 delivery stations is given to demonstrate the veracity and practicability of the proposed method. From the results, computing time of the case is within 1 minute, and the solved detailed scheduling plans can fulfill demand with stable pump operations. Besides, the proposed approach is scarcely influenced by the scale of pipeline structure and time horizon, so it is also applicable to the long-term scheduling of a pipeline with many delivery stations.
Big Data Driven Agricultural Products Supply Chain Management: A Trustworthy Scheduling Optimization Approach
