Scheduling of multi-product fungible liquid pipelines using genetic algorithms
Proceedings of the 1999 ACM symposium on Applied computing
Introduction to Constraint Logic Programming
Introduction to Constraint Logic Programming
Eighteenth national conference on Artificial intelligence
Integrated Methods for Optimization (International Series in Operations Research & Management Science)
CSE '08 Proceedings of the 2008 11th IEEE International Conference on Computational Science and Engineering
A combined CLP-MILP approach for scheduling commodities in a pipeline
Journal of Scheduling
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Brazilian petrobrasis one of the world largest oil companies. Recurrently, it faces a very difficult over-constrained planning challenge: how to operate a large pipeline network in order to adequately transport oil derivatives and biofuels from refineries to local markets. In spite of being more economical and environmentally safer, the use of a complex pipeline network poses serious operational difficulties. The network has a complex topology, with around 30 interconnecting pipelines, over 30 different products in circulation, and about 14 distribution depots which harbor more than 200 tanks, with a combined capacity for storing up to 65 million barrels. The problem is how to schedule individual pumping operations, given the daily production and demand of each product, at each location in the network, over a given time horizon. We describe a solution based on a two-phase problem decomposition strategy. A novel Constraint Programming (CP) model plays a key role in modeling operational constraints that are usually overlooked in literature, but that are essential in order to guarantee viable solutions. The use of CP was crucial, since it allowed the modeling of complex constraints, including nonlinearities. The full strategy was implemented and produced very adequate results when tested over large real instances. In contrast, other approaches known from the literature failed, even when applied to much less complex networks.