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Motivated by the requirements of many real-life applications, recent research in AI planning has shown a growing interest in tackling problems that involve numeric constraints and complex optimization objectives. Applying Integer Programming (IP) to such domains seems to have a significant potential, since it can naturally accommodate their representational requirements. In this paper we explore the area of applying IP to AI planning in two different directions. First, we improve the domain-independent IP formulation of Vossen et al., by an extended exploitation of mutual exclusion relations between the operators, and other information derivable by state of the art domain analysis tools. This information may reduce the number of variables of an IP model and tighten its constraints. Second, we link IP methods to recent work in heuristic search for planning, by introducing a variant of {\tt FF}'s enforced hill-climbing algorithm that uses IP models as its underlying representation. In addition to extending the delete lists heuristic to parallel planning and the more expressive language of IP, we also introduce a new heuristic based on the linear relaxation.