The purpose of this dissertation is to develop models and solution approaches to identify the critical hierarchies of railroad and surface transportation network infrastructures, and to facilitate re-routing options that will be necessary for traffic management decision makings in the event of a disaster. We focus on building mathematical models for routing/re-routing of traffic considering the congestion effects which are obvious in the disrupted networks due to disaster. Based on these models, the critical hierarchies of infrastructures are determined. For railroad, we develop two different models: the first one considers ‘unit’ train re-routing and the other one considers Train Design approach. For intermodal system, the optimization model facilitates optimal re-routing of traffic using three surface transportation modes: highway, railway and waterway, considering the congestion characteristics of each mode. For the first model of railroad routing, the optimization model optimally routes unit trains based on a minimum cost network flow formulation with nonlinear objective function. The nonlinear objective function is approximated with a piece-wise linear function to make the model computationally tractable. The second model, known as Train Design optimization, is a highly combinatorial and complex optimization problem. The developed model’s computational complexity suggests us to use heuristic solution procedures. We develop a special heuristic algorithm to route the traffic in the congested network. In this heuristic procedure, we divide the problem into two sub-problems (SPs): SP-1 is termed as Block-to-Train Assignment (BTA) problem, and SP-2 is termed as Train Routing (TR) problem. BTA problem provides a feasible solution that includes the minimum number of required trains with the pick-up and drop-off points of the blocks carried by these trains, and TR problem ensures the optimal routing of these trains. Similar to railroad, an optimization model is developed for optimal routing/rerouting of traffic using the intermodal network. It is a mixed integer programming (MIP) problem, which is not practical to solve for real-world problem instances within reasonable amount of time. Linear relaxation to this model provides a very good lower bound closer to optimal solution. Therefore, we implemented our case-study for a realworld intermodal transportation system of five U.S. states.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-2222 |
| Date | 11 May 2013 |
| Creators | Al Khaled, Abdullah |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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