Approximate dynamic programming for rail operations

Warren Buckler Powell, Belgacem Bouzaiene-Ayari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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 languageEnglish (US)
Title of host publication7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems, ATMOS 2007
Pages191-208
Number of pages18
StatePublished - 2007
Event7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems, ATMOS 2007 - Seville, Spain
Duration: Nov 15 2007Nov 16 2007

Publication series

NameOpenAccess Series in Informatics
Volume7
ISSN (Print)2190-6807

Other

Other7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems, ATMOS 2007
Country/TerritorySpain
CitySeville
Period11/15/0711/16/07

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Modeling and Simulation

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