Doctoral Thesis Defense: Huaining Tian

Speaker: Huaining Tian

Supervisor: Dr. B. Jaumard

Supervisory Committee: Drs. J.-F. Cordeau, K. Demirli, T. Fevens, J. Opatrny, A. Awasthi (Chair)

Title: Optimization of Locomotive Management and Fuel Consumption in Rail Freight Transport 

Date: Wednesday, January 24, 2018

Time: 10:00 a.m.

Place: EV 3.309

ABSTRACT

For the enormous capital investment and high operation expense of locomotives, the locomotive management/assignment and fuel consumption are two of the most important areas for railway industry, especially in freight train transportation.

Several algorithms have been developed for locomotive assignment problem (LAP), including exact mathematics models and approximate dynamic programming and heuristics. These previously published optimization algorithms suffer from scalability or solution accuracy issues. In addition, each of the optimization models lacks part of the constraints that are necessary in real-world train/locomotive operation, e.g., maintenance/shop constraints or consist busting avoidance. Furthermore, there are rarely research works for the reduction of total train energy consumption on the locomotive assignment level.

The thesis is organized around our three main contributions. Firstly we propose a “consist travel plan” based LAP optimization model, which covers the necessary constraints mentioned above and which can efficiently be solved using large scale optimization techniques, namely column generation (CG) decomposition. Our key contribution is that our LAP model can evaluate the occurrence of consist busting by the number of consist travel plans, and allows locomotive status transformation in flow conservation constraints.

In addition, a new column generation acceleration architecture is developed, that allow the subproblem, i.e., column generator to create multiple columns in each iteration, that

each is an optimal solution for a reduced sub-network. This new CG architecture reduces computational time greatly comparing to our original LAP model.

For train fuel consumption, we derive, linearize and integrate a train fuel consumption model into our LAP model. In addition, we establish a conflict-free pre-process for time windows for train rescheduling without touching train-meet time and position. The new LAP-fuel consumption model works fine for the optimization of the train energy exhaustion on the locomotive assignment level.

For the optimization models above, the numerical results are conducted on the railway network infrastructure of Canada Pacific Railway (CPR), with up to 1,750 trains and 9 types of locomotives over a two-week time period in the entire CPR railway network.