Bercher, PascalHaslum, PatrikMuise, Christian2025-06-122025-06-1297819567920411045-0823ORCID:/0000-0002-0795-4320/work/177885429http://www.scopus.com/inward/record.url?scp=85204295468&partnerID=8YFLogxKhttps://hdl.handle.net/1885/733759954Automated Planning deals with finding a sequence of actions that solves a given (planning) problem. The cost of the solution is a direct consequence of these actions, for example its number or their accumulated costs. Thus, in most applications, cheaper plans are preferred. Yet, finding an optimal solution is more challenging than finding some solution. So, many planning algorithms find some solution and then post-process, i.e., optimize it - a technique called plan optimization. Over the years many different approaches were developed, not all for the same kind of plans, and not all optimize the same metric. In this comprehensive survey, we give an overview of the existing plan optimization goals, their computational complexity (if known), and existing techniques for such optimizations.Pascal Bercher is the recipient of an Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA), project number DE240101245, funded by the Australian Government. Christian Muise gratefully acknowledges funding from the Natural Sciences and Engineering Research Council of Canada (NSERC).10enPublisher Copyright: © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.202410.24963/ijcai.2024/87985204295468