Detailed scheduling of long-distance multiproduct pipelines has received growing attention in the past few years. It helps the planner to reduce the number of pump and segment switches between active and idle conditions to obtain savings on pump operating and maintenance costs. Most contributions on the detailed scheduling of multiproduct pipelines concern networks with a single straight line. Large-scale pipeline networks, however, usually have a treelike configuration, featuring a mainline and several secondary lines, transporting smaller volumes of refined petroleum products over shorter distances. This work addresses the scheduling of a multiproduct treelike pipeline through a continuous-time mixed integer linear programming (MILP) model that allows the execution of simultaneous deliveries from a unique refinery to multiple downstream terminals so as to get a substantial increase in transportation capacity. Contrary to previous contributions on treelike pipeline systems, the new model solves batch sizing and sequencing problems in a single step, generating a detailed delivery schedule. It also handles flow rate limitations in downstream pipeline segments originated from a lower diameter. Three case studies including one real life problem are used to illustrate the efficacy of the proposed model.

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Common carrier pipelines are one of the most economic modes for transportation of petroleum refined products over land, especially when huge amounts of these products have to be pumped toward long-distance terminals. This paper introduces a novel Mixed Integer Linear Programming (MILP)-based approach for the long-term scheduling of a real world multiproduct pipeline connecting a unique refinery to several distribution centers. This approach allows consideration of multiple due dates for demands at period ends, flow rate limitation on pipeline segments, and simultaneous deliveries at distribution centers. The proposed model results in substantial reduction in pump operation and maintenance costs in comparison with the available models. Computational results and data are reported.

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Linear optimization, Bi-parametric sensitivity analysis, Optimal partition, Invariancy region, Optimal value function,

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