Networks and Spatial Economics

, Volume 19, Issue 3, pp 929–952 | Cite as

Reliable Routing of Road-Rail Intermodal Freight under Uncertainty

  • Majbah Uddin
  • Nathan HuynhEmail author


Transportation infrastructures, particularly those supporting intermodal freight, are vulnerable to natural disasters and man-made disasters that could lead to severe service disruptions. These disruptions can drastically degrade the capacity of a transportation mode and consequently have adverse impacts on intermodal freight transport and freight supply chain. To address service disruption, this paper develops a model to reliably route freight in a road-rail intermodal network. Specifically, the model seeks to provide the optimal route via road segments (highway links), rail segments (rail lines), and intermodal terminals for freight when the network is subject to capacity uncertainties. To ensure reliability, the model plans for reduced network link, node, and intermodal terminal capacity. A major contribution of this work is that a framework is provided to allow decision makers to determine the amount of capacity reduction to consider in planning routes to obtain a user-specified reliability level. The proposed methodology is demonstrated using a real-world intermodal network in the Gulf Coast, Southeastern, and Mid-Atlantic regions of the United States. It is found that the total system cost increases with the level of capacity uncertainty and with increased confidence levels for disruptions at links, nodes, and intermodal terminals.


Intermodal freight network Road-rail freight Reliable routing Uncertainty 



  1. Agrawal B, Ziliaskopoulos A (2006) Shipper–carrier dynamic freight assignment model using a variational inequality approach. Transp Res Rec 1966:60–70Google Scholar
  2. Ayar B, Yaman H (2012) An intermodal multicommodity routing problem with scheduled services. Comput Optim Appl 53:131–153Google Scholar
  3. Barbarosoglu G, Arda Y (2004) A two-stage stochastic programming framework for transportation planning in disaster response. J Oper Res Soc 55(1):43–53Google Scholar
  4. Barnhart C, Ratliff H (1993) Modeling intermodal routing. J Bus Logist 14(1):205–223Google Scholar
  5. Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52(1):35–53Google Scholar
  6. Boardman B, Malstrom E, Butler D, Cole M (1997) Computer assisted routing of intermodal shipments. Comput Ind Eng 33(1–2):311–314Google Scholar
  7. Bureau of Transportation Statistics (2015) Freight facts and figures. U.S. Department of Transportation. Accessed 5 March 2017
  8. Cambridge Systematics, Inc. (1995) Characteristics and changes in freight transportation demand: a guidebook for planners and policy analysts. Bureau of Transportation Statistics. Accessed 5 March 2017
  9. Cambridge Systematics, Inc. (2007) National rail freight infrastructure capacity and investment study. Association of American Railroads, Washington, DCGoogle Scholar
  10. Cantillo V, Macea L, Jaller M (2018) Assessing vulnerability of transportation networks for disaster response operations. Netw Spat Econ. in press
  11. Cappanera P, Scaparra M (2011) Optimal allocation of protective resources in shortest path networks. Transp Sci 45(1):64–80Google Scholar
  12. Chang M-S, Tseng Y-L, Chen J-W (2007) A scenario planning approach for the flood emergency logistics preparation problem under uncertainty. Transp Res Part E 43:737–754Google Scholar
  13. Chen A, Yang C, Kongsomsaksakul S, Lee M (2007) Network-based accessibility measures for vulnerability analysis of degradable transportation networks. Netw Spat Econ 7:241–256Google Scholar
  14. Chen L, Miller-Hooks E (2012) Resilience: an indicator of recovery capability in intermodal freight transport. Transp Sci 46(1):109–123Google Scholar
  15. Corman F, Viti F, Negenborn R (2017) Equilibrium models in multimodal container transport systems. Flex Serv Manuf J 29:125–153Google Scholar
  16. Cui T, Ouyang Y, Shen Z (2010) Reliable facility location design under the risk of disruptions. Oper Res 58(4):998–1011Google Scholar
  17. D’Amico E (2002) West coast port lockout creates problems for chemical shippers. Chem Week 164(40):10Google Scholar
  18. Darayi M, Barker K, Santos J (2017) Component importance measures for multi-industry vulnerability of a freight transportation network. Netw Spat Econ 17:1111–1136Google Scholar
  19. Daskin M (1983) A maximum expected covering location model: formulation, properties and heuristics solution. Trans Sci 17(1):48–70Google Scholar
  20. Federal Highway Administration (2016) Freight analysis framework. U.S. Department of Transportation. Accessed 15 April 2017
  21. Fotuhi F, Huynh N (2017) Reliable intermodal freight network expansion with demand uncertainties and network disruptions. Netw Spat Econ 17:405–433Google Scholar
  22. Friesz T, Gottfried J, Morlok E (1986) A sequential shipper-carrier network model for predicting freight flows. Transp Sci 20(2):80–91Google Scholar
  23. Garg M, Smith J (2008) Models and algorithms for the design of survivable multicommodity flow networks with general failure scenarios. Omega 36:1057–1071Google Scholar
  24. Gedik R, Medal H, Rainwater C, Pohl E, Mason S (2014) Vulnerability assessment and re-routing of freight trains under disruptions: a coal supply chain network application. Transp Res Part E 71:45–57Google Scholar
  25. Godoy LA (2007) Performance of storage tanks in oil facilities damaged by hurricanes Katrina and Rita. J Perform Constr Facil 21:441–449Google Scholar
  26. Guelat J, Florian M, Crainic T (1990) A multimode multiproduct network assignment model for strategic planning of freight flows. Transp Sci 24(1):25–39Google Scholar
  27. Haghani A, Oh S (1996) Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations. Transp Res Part A 30(3):231–250Google Scholar
  28. Huang Y, Pang W (2014) Optimization of resilient biofuel infrastructure systems under natural hazards. J Energ Eng 140(2):1–11Google Scholar
  29. Jenelius E, Petersen T, Mattsson L-G (2006) Importance and exposure in road network vulnerability analysis. Transp Res Part A 40:537–560Google Scholar
  30. Li L, Negenborn R, Schutter B (2014) Receding horizon approach for container flow assignment in intermodal freight transport. Transp Res Rec 2410:132–140Google Scholar
  31. Li Q, Nie Y, Vallamsundar S, Lin J, Homem-de-Mello T (2016) Finding efficient and environmentally friendly paths for risk-averse freight carriers. Netw Spat Econ 16:255–275Google Scholar
  32. Lim H, Thill J-C (2008) Intermodal freight transportation and regional accessibility in the United States. Environ Plan A 40:2006–2025Google Scholar
  33. Mahmassani H, Zhang K, Dong J, Lu C-C, Arcot V, Miller-Hooks E (2007) Dynamic network simulation–assignment platform for multiproduct intermodal freight transportation analysis. Transp Res Rec 2032:9–16Google Scholar
  34. Marufuzzaman M, Eksioglu S, Li X, Wang J (2014) Analyzing the impact of intermodal related risk to the design and management of biofuel supply chain. Transp Res Part E 69:122–145Google Scholar
  35. Meng Q, Wang X (2010) Utility-based estimation of probabilistic port hinterland for networks of intermodal freight transportation. Transp Res Rec 2168:53–62Google Scholar
  36. Miller-Hooks E, Zhang X, Faturechi R (2012) Measuring and maximizing resilience of freight transportation networks. Comput Oper Res 39:1633–1643Google Scholar
  37. Ng M, Waller S (2012) A dynamic route choice model considering uncertain capacities. Comput Aided Civ Inf 27(4):231–243Google Scholar
  38. Ozdamar L, Ekinci E, Kucukyazici B (2004) Emergency logistics planning in natural disasters. Ann Oper Res 129:217–245Google Scholar
  39. Peng P, Snyder L, Lim A, Liu Z (2011) Reliable logistics networks design with facility disruptions. Transp Res Part B 45:1190–1211Google Scholar
  40. Peterson S, Church R (2008) A framework for modeling rail transport vulnerability. Growth Change 39(4):617–641Google Scholar
  41. Poudel S, Marufuzzaman M, Bian L (2016) Designing a reliable bio-fuel supply chain network considering link failure probabilities. Comput Ind Eng 91:85–99Google Scholar
  42. Qu Y, Bektas T, Bennell J (2016) Multimode multicommodity network design model for intermodal freight transportation with transfer and emission costs. Netw Spat Econ 16:303–329Google Scholar
  43. Rennemo S, Ro K, Hvattum L, Tirado G (2014) A three-stage stochastic facility routing model for disaster response planning. Transp Res Part E 62:116–135Google Scholar
  44. Rios M, Marianov V, Gutierrez M (2000) Survivable capacitated network design problem: new formulation and Lagrangian relaxation. J Oper Res Soc 51(5):574–582Google Scholar
  45. Rudi A, Frohling M, Zimmer K, Schultmann F (2016) Freight transportation planning considering carbon emissions and in-transit holding costs: a capacitated multi-commodity network flow model. EURO J Transp Logist 5(2):123–160Google Scholar
  46. Rupi F, Bernardi S, Rossi G, Danesi A (2015) The evaluation of road network vulnerability in mountainous areas: a case study. Netw Spat Econ 15:397–411Google Scholar
  47. Shen Z, Dessouky M, Ordez F (2009) A two-stage vehicle routing model for large-scale bioterrorism emergencies. Networks 54(4):255–269Google Scholar
  48. Snyder L, Daskin M (2005) Reliability models for facility location: the expected failure cost case. Transp Sci 39:400–416Google Scholar
  49. Torrey W, Murray D (2014) An analysis of the operational costs of trucking: 2014 update. ATRI report. Accessed 20 April 2017
  50. Uddin M, Huynh N (2015) Freight traffic assignment methodology for large-scale road-rail intermodal networks. Transp Res Rec 2477:50–57Google Scholar
  51. Uddin M, Huynh N (2016) Routing model for multicommodity freight in an intermodal network under disruptions. Transp Res Rec 2548:71–80Google Scholar
  52. Unnikrishnan A, Valsaraj V, Waller S (2009) Stochastic and dynamic shipper carrier network design problem. Netw Spat Econ 9:525–550Google Scholar
  53. Viljoen N, Joubert J (2018) The road most travelled: the impact of urban road infrastructure on supply chain network vulnerability. Netw Spat Econ 18:85–113Google Scholar
  54. Winebrake J, Corbett J, Falzarano A, Hawker J, Korfmacher K, Ketha S, Zilora S (2008a) Assessing energy, environmental, and economic tradeoffs in intermodal freight transportation. J Air Waste Manage Assoc 58(8):1004–1013Google Scholar
  55. Winebrake J, Corbett J, Hawker J, Korfmacher K (2008b) Intermodal freight transport in the great lakes: development and application of a great lakes geographic intermodal freight transport model. RIT Report. Accessed 20 April 2017
  56. Xiong G, Wang Y (2014) Best routes selection in multimodal networks using multiobjective genetic algorithm. J Comb Optim 28:655–673Google Scholar
  57. Zhang K, Nair R, Mahmassani H, Miller-Hooks E, Arcot V, Kuo A, Dong J, Lu C-C (2008) Application and validation of dynamic freight simulation–assignment model to large-scale intermodal rail network: pan-European case. Transp Res Rec 2066:9–20Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringUniversity of South CarolinaColumbiaUSA

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