Train dispatchers faces lots of challenges due to conflicts which causes delays of trains as a result of solving possible dispatching problems the network faces. The major challenge is for the train dispatchers to make the right decision and have reliable, cost effective and much more faster approaches needed to solve dispatching problems. This thesis work provides detail information on the implementation of different heuristic algorithms for train dispatchers in solving train dispatching problems. The library data files used are in xml file format and deals with both single and double tracks between main stations. The main objective of this work is to build different heuristic algorithms to solve unexpected delays faced by train dispatchers and to help in making right decisions on steps to take to have reliable and cost effective solution to the problems. These heuristics algorithms proposed were able to help dispatchers in making right decisions when solving train dispatching problems.

Each year the Railway Applications Section (RAS) of the Institution for Operations Research and the Management Sciences (INFORMS) posits a research problem to the world in the form of a competition. For 2012, the contest involved solving the Train Dispatching Problem (TDP) on a realistic 85 edge network for three different sets of input data. This work is an independent attempt to match or improve upon the results of the top three finishers in the contest using mixed integer programming (MIP) techniques while minimizing the use of heuristics. The primary focus is to partition the network in a manner that reduces the number of binary variables in the formulation as much as possible without compromising the ability to satisfy any of the contest requirements. This resulted in the ability to optimally solve this model for RAS Data Set 1 in 29 seconds without any problem-specific heuristics, variable restrictions, or variable fixing. Applying some assumptions about train movements allowed the same Data Set 1 solution to be found in 5.4 seconds. After breaking the larger Data Sets 2 and 3 into smaller sub-problems, solutions for Data Sets 2 and 3 were 28% and 1% better, respectively, than those of the competition winner. The time to obtain solutions for Data Sets 2 and 3 was 90 and 318 seconds, respectively.