### Abstract

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.

Original language | English (US) |
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Title of host publication | 7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems, ATMOS 2007 |

Pages | 191-208 |

Number of pages | 18 |

State | Published - Dec 1 2007 |

Event | 7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems, ATMOS 2007 - Seville, Spain Duration: Nov 15 2007 → Nov 16 2007 |

### Publication series

Name | OpenAccess Series in Informatics |
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Volume | 7 |

ISSN (Print) | 2190-6807 |

### Other

Other | 7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems, ATMOS 2007 |
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Country | Spain |

City | Seville |

Period | 11/15/07 → 11/16/07 |

### All Science Journal Classification (ASJC) codes

- Geography, Planning and Development
- Modeling and Simulation

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## Cite this

*7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems, ATMOS 2007*(pp. 191-208). (OpenAccess Series in Informatics; Vol. 7).