Integrated optimization and simulation models for the locomotive refueling system configuration problem

Locomotives in the U.S. use over 3 billion gallons of fuel each year and faster refueling can increase rail network capacity without the infrastructure cost associated with new terminals or tracks. This thesis introduces the locomotive refueling system configuration problem (LRSCP), which seeks to improve efficiency in refueling yards through new technologies or policies. This research also creates two new methods to solve LRSCP. The first method uses an integer program to solve the off-line LRSCP and develop a static refueling policy. The train refueling integer program, TRIP, maximizes the weighted number of train combinations that can be refueled without delay. TRIP is optimized and its outputs are used as inputs to a simulation developed in Simio for testing and validation. The second method creates an integrated integer program and simulation to solve the online LRSCP and produces a dynamic refueling policy. This tool, built in Python, incorporates a different integer program, the strike line integer program (SLIP), into the simulation. SLIP determines the optimal refueling assignment for each incoming train. The simulation incorporates SLIP’s solution for testing and validation. This tool is truly integrated and requires approximately 300 instances of SLIP to simulate a single day. Based on experimental results, solving either TRIP or SLIP and incorporating the optimal refueling policy improves railyard operations by 10 to 30%. This impact is statistically significant and increases the capacity of a railyard. Additionally, it impacts other important parameters such as time spent in the yard and the maximum queue for the railyard. Furthermore, there is a significant decrease in wasted time and an improvement to railyard efficiency. Implementing either method should increase a railyard’s capacity and significantly increase revenue opportunities.

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