Currently, pipeline is the most effective way to transport large-volume products over long distance. To effectively satisfy market demands for multiple refined products by delivery due dates, the multi-product pipeline network usually transports several refined products in sequence from refineries to certain destinations. The integrated scheduling of multi-product pipeline network, including inventory management, transport routes planning, batch sequence, batch volume, et al., is one of the most strategic problems due to its large-scale, complexity as well as economic significance. This subject has been widely studied during the last decade. However, most researches focus on large-size scheduling models whose computational efficiency greatly decreases for a complex pipeline network or a long time horizon. Aiming at this problem, the paper develops an efficient decomposition approach, which is composed of two mixed integer linear programming (MILP) models. The first model divides the entire time horizon into several intervals according to delivery due dates and optimizes the transport routes and total transport volume during each interval with considering market demand, production campaigns and inventory limits. Then the solved results are used by the second model which sets the objective function as the membership function based on fuzzy delivery due dates. Besides, a series of operational constraints are also considered in the second model to obtain the optimal batch sequence, batch volume, delivery volume and delivery time in each node. Finally, the proposed approach is applied to a Chinese real-world pipeline network that includes 5 complex multi-product pipelines associated with 6 refineries and 2 depots. The results demonstrate that the proposed approach can provide a guideline for long-term pipeline network scheduling with delivery due dates.

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