Multi-product pipelines are a significant and extensive mean of transporting petroleum based products from refineries to distribution centers. Previous contributions on tree-like pipeline scheduling problem have considered a simple structure with a single refinery connected to a mainline and some secondary lines only emerged from the mainline. In practice, however, a tree-like pipeline may also have several branches on a secondary line resulting in a complex structure, the so called multi-level tree-like pipeline. This paper addresses the short-term scheduling of multi-level tree-like pipelines with multiple refineries through a continuous time mixed-integer linear programming (MILP) model that considers multiple intermediate due dates for product demands. The objective is to satisfy product demands on time at the minimum operational costs, such as pumping, interface and backorder costs. The proposed model performance's is shown by solving four examples.

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The operational scheduling of multiproduct pipelines has received increasing attention among researchers in the last decade. Most of the previous contributions rely on a continuous-time approach whose solution CPU time rapidly explodes when dealing with rather long time horizons. Discrete-time models, however, have not been explored to their full potential in the literature for complex pipeline networks. In this paper, we present a discrete-time mixed-integer linear programming model that can find the aggregate scheduling of pipelines with multiple refineries and depots. It is suitable for single-level tree-structured systems, in which all branches appear at different points of the mainline. The proposed model allows to consider multiple intermediate due dates for product demands, simultaneous injections, simultaneous input/output operations at dual-purpose terminals, and variable pump rates. Solutions to three benchmark example problems illustrate that the proposed model can generate high-quality solutions in a reasonable computational burden compared to previous works.

In the oil supply chain, the refined petroleum products are transported by various transportation modes, such as rail, road, vessel and pipeline. The latter provides one of the safest and cheapest ways to connect production areas to local markets. This paper addresses the operational scheduling of a multiproduct tree-like pipeline connecting several refineries to multiple distribution centers under demand uncertainty. A new deterministic mixed-integer linear programming (MILP) model is first presented, and then a two-stage stochastic model is proposed. The aim of this model is to meet depot requirements at the minimum total cost including pumping and stoppages costs. The efficiency and utility of the proposed model is shown by two numerical examples, which one of them uses the industrial and real data.