A self-learning approach for optimal detailed scheduling of multi-product pipeline

作者：

Highlights：

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• A self-learning algorithm is proposed for detailed optimal scheduling for product oil pipeline.

• Two novel discontinuous process constraints are taken into consideration.

• This algorithm can improve calculation speed and efficiency by itself.

• A real example with six cases is given to demonstrate the method’s practicality.

摘要

•A self-learning algorithm is proposed for detailed optimal scheduling for product oil pipeline.•Two novel discontinuous process constraints are taken into consideration.•This algorithm can improve calculation speed and efficiency by itself.•A real example with six cases is given to demonstrate the method’s practicality.

论文关键词：Multi-product pipeline,Self-learning approach,Detailed scheduling,Mixed-integer linear programming (MILP),Fuzzy clustering analysis,Ant colony optimization (ACO)

论文评审过程：Received 8 January 2017, Revised 22 February 2017, Available online 16 June 2017, Version of Record 27 June 2017.

论文官网地址：https://doi.org/10.1016/j.cam.2017.05.040