Recently tremendous advances have been made in the performance of AI planning systems. However increased performance is only one of the prerequisites for bringing planning into the realm of real applications; advances in the scope of problems that can be represented and solved must also be made. In this paper we address two important representational features, concurrently executable actions with varying durations, and metric quantities like resources, both essential for modeling real applications. We show how the forward chaining approach to planning can be extended to allow it to solve planning problems with these two features. Forward chaining using heuristics or domain specific information to guide search has shown itself to be a very promising approach to planning, and it is sensible to try to build on this success. In our experiments we utilize the TLPLAN approach to planning, in which declaratively represented control knowledge is used to guide search. We show that this extra knowledge can be intuitive and easy to obtain, and that with it impressive planning performance can be achieved.
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