Pipelines have an important role in oil and its derivatives transportation, since they are the most effective way to transport high volumes through long distances. The motivation for this work is that a non negligible part of the final price for those products are due to transportation costs. Few authors have addressed this problem, with most of the previous work using integer programming techniques. This work analyses the use of Artificial Intelligence techniques, discrete event simulators and software frameworks for building automated planners that are able to deal with real-world oil pipeline transportation instances. The first contribution of this thesis is the specification of a new planning domain called PIPESWORLD. This domain is inspired by the oil pipeline transportation problem, and is defined in PDDL. Due to its original structure, the PIPESWORLD domain has been incorporated to the 4th International Planning Competition benchmark. Even being a simplification of the original problem, PIPESWORLD instances in the benchmark are challenging to state of art solvers. It is also shown that decision problems based on PIPESWORLD configurations are NP-Hard. The second contribution of this thesis is the PLANSIM opensource framework. This framework incorporates a search engine that may use several different strategies, and defines a structure that facilitates the construction of automated planners based on heuristic forward search that use discrete event simulators as the model for the process to be planned. The third contribution of this thesis is a PLANSIM instantiation that results in an automated planner able to deal with real-world oil pipeline transportation instances, called PLUMBER 2. The use of discrete event simulation techniques for the model of the system to be planned allows this model to be very close to the original problem. This, in conjunction with PLANSIM usage, facilitates the construction of planners that are able to cope with real-world instances.