In the last fifteen years, AI planning and scheduling techniques have been characterized by an impressive increase of their performances in terms of size and complexity of the solutions produced. These improvements are related to the definition of new data structures which can efficiently encode and make explicit constraints that are only implicitly defined in problem formulation, and to the definition of heuristics that allow one to visit only the most promising parts of the search space. Quite interesting, an increasing number of systems started to adopt planning and scheduling techniques in order to afford complex application contexts, and obtaining solutions that better fit the problem constraints and the users’ needs. This special issue contains extended versions of selected papers presented at IPS 2013, the 5th Italian Workshop on Planning and Scheduling held in Torino, Italy, December 4th, 2013.1 IPS 2013 was held within the XIII Conference of the Italian Association for Artificial Intelligence (AI*IA 2013), gathered together researchers interested in different aspects of planning and scheduling, and introduced new researchers to the community. For this edition of IPS, the call for papers solicited submissions of two different types: full papers and short papers. Full technical papers reported work in progress or completed work, while short papers reported views or