Approximate dynamic programming for rail operations

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. For over 10 years we have been developing a series of models for optimizing ...

• Type: Conference
• Year: 2007
• Where: ATMOS
• Authors: Warren B. Powell, Belgacem Bouzaïene-Ayari
• Keywords: Approximate Dynamic Programming, ATMOS 2007, Math Programming, Optimization, Rail Operations