Disruption risk management in railroad networks: An optimization-based methodology and a case study

The authors propose an optimization-based methodology for recovery from random disruptions in service legs and train services in a railroad network. A network optimization model is solved for each service leg to evaluate a number of what-if scenarios. The solutions of these optimization problems are then used in a predictive model to identify the critical disruption factors and accordingly design a suitable mitigation strategy. A mitigation strategy, such as adding flexible or redundant capacity in the network, is an action that is deliberately taken by management in order to hedge against the cost and impact of disruption if it occurs. It is important that managers consider the trade-offs between the cost of mitigation strategy and the expected cost of disruption. The proposed methodology is applied to a case study built using the realistic infrastructure of a railroad network in the mid-west United States. The resulting analysis underscores the importance of accepting a slight increase in pre-disruption transportation costs, which in turn will enhance network resiliency by building dis-similar paths for train services, and by installing alternative links around critical service legs.

• Record URL:
  https://doi.org/10.1016/j.trb.2016.01.001
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0191261516000059
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Azad, Nader
  • Hassini, Elkafi
  • Verma, Manish
• Publication Date: 2016-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 70-88
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 85
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: Case studies; Mixed integer programming; Networks; Railroads; Risk management; Service disruption
• Uncontrolled Terms: Predictive models
• Geographic Terms: Midwestern States
• Subject Areas: Operations and Traffic Management; Planning and Forecasting; Railroads;

Filing Info

• Accession Number: 01597562
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 3 2016 1:39PM