The encyclopedia · Engineering & Operations · Operational decision · 2010–2014
Norfolk Southern sized its locomotive fleet with an optimizing simulator.
Norfolk Southern used approximate dynamic programming to plan locomotive fleet size and mix, a balance of simulation and optimization.
Norfolk Southern Railway
the move
Norfolk Southern plans locomotives for a large rail network, and it wanted a model to help decide how big its fleet should be.
Exact integer-programming models were beyond solver reach and needed assumptions that made the fleet look smaller than it needed to be.
Researchers built an approximate-dynamic-programming model that mixes simulation and optimization with feedback learning, calibrating to historical metrics so it can plan fleet size and mix and test many operating scenarios.
why it works
- Fleet sizing required a highly detailed model
- Integer programming forced simplifications that understated the fleet
- Simulation plus learning let the model match real history
- The result plans fleet size and mix under many scenarios
what transfers
When a real problem is too large or too detailed for an exact model, approximate dynamic programming can learn from simulation to size expensive assets accurately.
what came after
The model became the way Norfolk Southern studies locomotive fleet size and mix and tests operating changes, a notable use of approximate dynamic programming in freight rail.
references
- Locomotive Planning at Norfolk Southern: An Optimizing Simulator Using Approximate Dynamic Programming
- Operations Research at Norfolk Southern Railway Company
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