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13. Approximate dynamic programming for rail operations Powell, Warren Bouzaiene-Ayari, Belgacem Approximate dynamic programming offers a new modeling and algorithmic strategy for complex problems such as rail operations. Problems in rail operations are often modeled using classical math programming models defined over space-time networks. Even simplified models can be hard to solve, requiring the use of various heuristics. We show how to combine math programming and simulation in an ADP-framework, producing a strategy that looks like simulation using iterative learning. Instead of solving a single, large optimization problem, we solve sequences of smaller ones that can be solved optimally using commercial solvers. We step forward in time using the same flexible logic used in simulation models. We show that we can still obtain near optimal solutions, while modeling operations at a very high level of detail. We describe how to adapt the strategy to the modeling of freight cars and locomotives. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Warren Powell and Belgacem Bouzaiene-Ayari 2007 Is Part Of OASIcs, Volume 7, 7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems (ATMOS'07) (2007) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.ATMOS.2007.1180 urn:nbn:de:0030-drops-11804 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2007.1180 eng https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode