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Managing rail-truck intermodal transportation for hazardous materials with random yard disruptions

  • S.I.: Data-Driven OR in Transportation and Logistics
  • Published:
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Abstract

Combining multiple transportation modes, intermodal transportation has been widely used in shipping hazardous materials (hazmat). But the relevant research on intermodal transportation for hazmat is still limited, especially when the planning environment contains possible system disruptions. This study develops a scenario-based robust optimization model for a rail-truck intermodal transportation network that ships regular and multiple hazmat freights with random disruptions at intermodal yards. To be specific, three operational level and one strategic level recovery mechanisms are proposed to maintain network connectivity during disruptions. Then, embedding various yard disruption scenarios with recovery plans, the expected risk and corresponding variability are minimized simultaneously, considering an additional augmented constraint to ensure the reliability in cost. Numerical experiments based on a real-world intermodal network of CSX, a leading rail-based freight transporter in North America, are conducted to find the optimal robust network structure and routing plan. A series of sensitivity analyses, in terms of recovery mechanisms and key parameter values, reveal relationships among the robustness and reliability of the intermodal transportation system. Further managerial insights can be used to assist intermodal carrier in seeking contingency plans for disruptions.

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Abbreviations

V :

Set of terminals

Z :

Set of O/D pairs for demand, indexed by z

H :

Set of products being shipped, indexed by h. To be specific, \(h=0\) indicates non-hazmat products, while \(h\ge 1\) represents different hazmat products

W :

Set of intermodal train services, indexed by w

S :

Set of terminal disruption scenarios, indexed by s

\(V_s\) :

Set of disrupted intermodal terminals under disruption scenario s, indexed by v. Hence, the set of undisrupted terminals is \(V\backslash V_s\), indexed by \(v'\)

\(P_{z}\) :

Set of intermodal paths available for demand z before disruption, indexed by p

\(P_{v}\) :

Set of intermodal paths using disrupted terminal v under scenario s

\(P_{s}\) :

Set of intermodal paths using undisrupted terminal \(v'\) under scenario s

\(P_{w}\) :

Set of intermodal paths using intermodal train service w

\(D_{zh}\) :

Number of hazmat containers in demand z of product h

\(C_{pzh}\) :

Transportation cost per hazmat container on path p for demand z of product h

\(CN_{w}\) :

Fixed cost of operating intermodal train service w

\(CR_k\) :

Cost to repair disrupted terminal v under scenario s

\(CA_v\) :

Cost of adding one unit capacity to terminal v

\(CB_{zh}\) :

Third party fulfillment cost per hazmat container for demand z of product h

\(U_{w}\) :

Capacity of intermodal train service w

\(Cap_v\) :

Capacity of terminal v without any disruption

\(DT_{zh}\) :

Due date for demand z of product h

\(CD_{zh}\) :

Delay cost for demand z of product h

\(T_{pz}\) :

Transportation time for a container of demand z using path p that passes through undisrupted terminal

\(TR^s_v\) :

Repair time for a disrupted terminal v under scenario s

\(E_{pzh}\) :

Transportation risk per hazmat container on path p for demand z of product h

\(EQ_{vh}\) :

Terminal risk per product h container on disrupted terminal v

\(\alpha ^s_v\) :

Proportion of capacity lost from disruption of terminal v under scenario s

\(\phi ^s\) :

Probability of disruption scenario s

\(\beta \) :

Maximum percentage of demand that can be fulfilled by using a third party

\(X^s_{pzh}\) :

Number of containers for demand z of product h that is shipped on path p passing through undisrupted terminal under scenario s

\(Y_{pzh}^{s}\) :

Number of containers for demand z of product h that is shipped on path p that passes through disrupted terminal under scenario s

\(XB^s_{pzh}\) :

1, if intermodal path p is used to meet demand z of product h under scenario s; 0, otherwise

\(YB_{pzh}^{s}\) :

1, if intermodal path p is using a disrupted terminal to meet demand z of product h; 0, otherwise

\(XQ^s_{v'h}\) :

Number of product h containers of passing through terminal v under scenario s

\(YQ_{vh}^{s}\) :

Number of product h containers passing through undisrupted terminal v

\(B^s_{zh}\) :

Number of containers for demand z of product h shipped by a third party after the due date under scenario s

\(R^s_v\) :

1, if terminal \(v\in V_s\) is repaired under scenario s; 0, otherwise

\(A_v\) :

Number of unit capacity (containers) added to yard v

\(N_{w}\) :

Number of intermodal train service of type w

\(DL^s_{zh}\) :

Delay in delivery demand z with product h under scenario s

References

  • Aerde, M. V., Shortreed, J., Stewart, A. M., & Matthews, M. (1989). Assessing the risks associated with the transport of dangerous goods by truck and rail using the RISKMOD model. Canadian Journal of Civil Engineering, 16(3), 326–334.

    Article  Google Scholar 

  • Assadipour, G., Ke, G. Y., & Verma, M. (2015). Planning and managing intermodal transportation of hazardous materials with capacity selection and congestion. Transportation Research Part E: Logistics and Transportation Review, 76, 45–57.

    Article  Google Scholar 

  • Assadipour, G., Ke, G. Y., & Verma, M. (2016). A toll-based bi-level programming approach to managing hazardous materials shipments over an intermodal transportation network. Transportation Research Part D: Transport and Environment, 47, 208–221.

    Article  Google Scholar 

  • Azad, N., Hassini, E., & Verma, M. (2016). Disruption risk management in railroad networks: An optimization-based methodology and a case study. Transportation Research Part B: Methodological, 85(Supplement C), 70–88.

    Article  Google Scholar 

  • Azizi, N. (2019). Managing facility disruption in hub-and-spoke networks: Formulations and efficient solution methods. Annals of Operations Research, 272, 159–185. https://doi.org/10.1007/s10479-017-2517-0.

    Article  Google Scholar 

  • Batta, R., & Chiu, S. (1988). Optimal obnoxious paths on a network: Transportation of hazardous materials. Operations Research, 36(1), 84–92.

    Article  Google Scholar 

  • Bergqvist, R. (2008). Evaluating road-rail intermodal transport services—A heuristic approach. International Journal of Logistics Research and Applications, 11(3), 179–199.

    Article  Google Scholar 

  • Bubbico, R., Maschio, G., Mazzarotta, B., Milazzo, M. F., & Parisi, E. (2006). Risk management of road and rail transport of hazardous materials in sicily. Journal of Loss Prevention in the Process Industries, 19(1), 32–38.

    Article  Google Scholar 

  • Carosso, G., Luceri, C., & Oreste, P. (2012). The international multimodal transport of hazardous goods and waste. American Journal of Environmental Sciences, 8(4), 443–453.

    Article  Google Scholar 

  • Chang, E., Floros, E., & Ziliaskopoulos, A. (2007). Dynamic fleet management: Concepts, systems, algorithms & case studies (pp. 113–132). Boston, MA: Springer. Chap An Intermodal Time-Dependent Minimum Cost Path Algorithm.

  • Chen, L., & Miller-Hooks, E. (2012). Resilience: An indicator of recovery capability in intermodal freight transport. Transportation Science, 46(1), 109–123. https://doi.org/10.1287/trsc.1110.0376.

    Article  Google Scholar 

  • Clausen, J., Larsen, A., Larsen, J., & Rezanova, N. J. (2010). Disruption management in the airline industry-concepts, models and methods. Computers & Operations Research, 37(5), 809–821.

    Article  Google Scholar 

  • Coco, A. A., Duhamel, C., & Santos, A. C. (2020). Modeling and solving the multi-period disruptions scheduling problem on urban networks. Annals of Operations Research, 285, 427–443. https://doi.org/10.1007/s10479-019-03248-5.

    Article  Google Scholar 

  • CSX (2019) CSX system map. https://www.csx.com/index.cfm/library/files/customers/maps/printable-system-map. Accessed 02 October 2019

  • Erkut, E., & Verter, V. (1998). Modeling of transport risk for hazardous materials. Operations Research, 46(5), 625–642.

    Article  Google Scholar 

  • Ghaderi, A., & Burdett, R. L. (2019). An integrated location and routing approach for transporting hazardous materials in a bi-modal transportation network. Transportation Research Part E: Logistics and Transportation Review, 127, 49–65. https://doi.org/10.1016/j.tre.2019.04.011.

    Article  Google Scholar 

  • Hu, C., Lu, J., Liu, X., & Zhang, G. (2018). Robust vehicle routing problem with hard time windows under demand and travel time uncertainty. Computers & Operations Research, 94, 139–153. https://doi.org/10.1016/j.cor.2018.02.006.

    Article  Google Scholar 

  • Huang, M., Hu, X., & Zhang, L. (2011). A decision method for disruption management problem in intermodal freight transport. Intelligent Decision Technologies, 10, 13–21.

    Article  Google Scholar 

  • Ishfaq, R. (2012). Resilience through flexibility in transportation operations. International Journal of Logistics Research and Applications, 15(4), 215–229. https://doi.org/10.1080/13675567.2012.709835.

    Article  Google Scholar 

  • Jabbarzadeh, A., Azad, N., & Verma, M. (2019). An optimization approach to planning rail hazmat shipments in the presence of random disruptions. Omega. https://doi.org/10.1016/j.omega.2019.06.004.

  • Kang, Y., Batta, R., & Kwon, C. (2014). Value-at-risk model for hazardous material transportation. Annals of Operations Research, 222(1), 361–387. https://doi.org/10.1007/s10479-012-1285-0.

    Article  Google Scholar 

  • List, G. F., Mirchandani, P. B., Turnquist, M. A., & Zografos, K. G. (1991). Modeling and analysis for hazardous materials transportation: Risk analysis, routing/scheduling and facility location. Transportation Science, 25(2), 100–114.

    Article  Google Scholar 

  • Marufuzzaman, M., Eksioglu, S. D., Li, X., & Wang, J. (2014). Analyzing the impact of intermodal-related risk to the design and management of biofuel supply chain. Transportation Research Part E: Logistics and Transportation Review, 69, 122–145. https://doi.org/10.1016/j.tre.2014.06.008.

    Article  Google Scholar 

  • Mazzarotta, B. (2002). Risk reduction when transporting dangerous goods: Road or rail? Risk, Decision and Policy, 7(01), 45–56.

    Article  Google Scholar 

  • Milazzo, M., Lisi, R., Maschio, G., Antonioni, G., Bonvicini, S., & Spadoni, G. (2002). Hazmat transport through messina town: From risk analysis suggestions for improving territorial safety. Journal of Loss Prevention in the Process Industries, 15(5), 347–356.

    Article  Google Scholar 

  • Miller-Hooks, E., Zhang, X., & Faturechi, R. (2012). Measuring and maximizing resilience of freight transportation networks. Computers & Operations Research, 39(7), 1633–1643. https://doi.org/10.1016/j.cor.2011.09.017.

    Article  Google Scholar 

  • Mohammadi, M., Jula, P., & Tavakkoli-Moghaddam, R. (2017). Design of a reliable multi-modal multi-commodity model for hazardous materials transportation under uncertainty. European Journal of Operational Research, 257(3), 792–809. https://doi.org/10.1016/j.ejor.2016.07.054.

    Article  Google Scholar 

  • Moret, S., Babonneau, F., Bierlaire, M., & Maréchal, F. (2020). Decision support for strategic energy planning: A robust optimization framework. European Journal of Operational Research, 280(2), 539–554. https://doi.org/10.1016/j.ejor.2019.06.015.

    Article  Google Scholar 

  • Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations Research, 43(2), 264–281. https://doi.org/10.1287/opre.43.2.264.

    Article  Google Scholar 

  • Narayanaswami, S., & Rangaraj, N. (2013). Modelling disruptions and resolving conflicts optimally in a railway schedule. Computers & Industrial Engineering, 64(1), 469–481.

    Article  Google Scholar 

  • Panetta, A. (2020). With rail blockades lifted, effort begins to measure economic damage. https://www.cbc.ca/news/canada/with-rail-blockades-lifted-effort-begins-to-measure-economic-damage-1.5487874. Accessed 29 March 2020.

  • Paul, S. K., Asian, S., Goh, M., & Torabi, S. A. (2019). Managing sudden transportation disruptions in supply chains under delivery delay and quantity loss. Annals of Operations Research, 273(1), 783–814. https://doi.org/10.1007/s10479-017-2684-z.

    Article  Google Scholar 

  • Revelle, C., Cohon, J., & Shobrys, D. (1991). Simultaneous siting and routing in the disposal of hazardous wastes. Transportation Science, 25(2), 138–145.

    Article  Google Scholar 

  • Sarhadi, H., Tulett, D. M., & Verma, M. (2015). A defender-attacker-defender approach to the optimal fortification of a rail intermodal terminal network. Journal of Transportation Security, 8(1), 17–32.

    Article  Google Scholar 

  • Sarhadi, H., Tulett, D. M., & Verma, M. (2017). An analytical approach to the protection planning of a rail intermodal terminal network. European Journal of Operational Research, 257(2), 511–525.

    Article  Google Scholar 

  • Shimizu, H., Tanabe, H., & Yamamoto, M. (2008). The proposal system for shinkansen using constraint programming. In Proceedings of the 8th World Congress of Railway Research.

  • Sohn, J. (2006). Evaluating the significance of highway network links under the flood damage: An accessibility approach. Transportation Research Part A: Policy and Practice, 40(6), 491–506. https://doi.org/10.1016/j.tra.2005.08.006.

    Article  Google Scholar 

  • Teodorović, D., & Guberinić, S. (1984). Optimal dispatching strategy on an airline network after a schedule perturbation. European Journal of Operational Research, 15(2), 178–182.

    Article  Google Scholar 

  • Thorsen, A., & Yao, T. (2017). Robust inventory control under demand and lead time uncertainty. Annals of Operations Research, 257, 207–236. https://doi.org/10.1007/s10479-015-2084-1.

    Article  Google Scholar 

  • Uddin, M., & Huynh, N. (2016). Routing model for multicommodity freight in an intermodal network under disruptions. Transportation Research Record, 2548(1), 71–80. https://doi.org/10.3141/2548-09.

    Article  Google Scholar 

  • Uddin, M., & Huynh, N. (2019). Reliable routing of road-rail intermodal freight under uncertainty. Networks and Spatial Economics, 19(3), 929–952. https://doi.org/10.1007/s11067-018-9438-6.

    Article  Google Scholar 

  • Ukkusuri, S. V., & Yushimito, W. F. (2009). A methodology to assess the criticality of highway transportation networks. Journal of Transportation Security, 2(1), 29–46. https://doi.org/10.1007/s12198-009-0025-4.

    Article  Google Scholar 

  • US DOT (2012) Office of the Assistant Secretary for Research and Technology, Bureau of Transportation Statistics, United States Department of Transportation: 2012 commodity flow survey hazardous materials. http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/ec12tcf-us-hm.pdf. Accessed 11 March, 2016.

  • US DOT (2016) Office of the Assistant Secretary for Research and Technology, Bureau of Transportation Statistics, United States Department of Transportation: National transportation statistics. http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/NTS_Entire_2016Q3.pdf. Accessed 11 March, 2016.

  • Verma, M., & Verter, V. (2008). Advanced Technologies and Methodologies for Risk Management in the Global Transport of Dangerous Goods, IOS Press, chap The trade-offs in rail-truck intermodal transportation of hazardous materials: an illustrative case study (pp. 148–168). NATO Science for Peace and Security Series - E: Human and Societal Dynamics.

  • Verma, M., & Verter, V. (2010). A lead-time based approach for planning rail-truck intermodal transportation of dangerous goods. European Journal of Operational Research, 202(3), 696–706.

    Article  Google Scholar 

  • Verma, M., Verter, V., & Zufferey, N. (2012). A bi-objective model for planning and managing rail-truck intermodal transportation of hazardous materials. Transportation Research Part E: Logistics and Transportation Review, 48(1), 132–149.

    Article  Google Scholar 

  • Wang, X. J., & Paul, J. A. (2020). Robust optimization for hurricane preparedness. International Journal of Production Economics, 221, 107464. https://doi.org/10.1016/j.ijpe.2019.07.037.

    Article  Google Scholar 

  • Xie, Y., Lu, W., Wang, W., & Quadrifoglio, L. (2012). A multimodal location and routing model for hazardous materials transportation. Journal of Hazardous Materials, 227–228, 135–141.

    Article  Google Scholar 

  • Yu, C. S., & Li, H. L. (2000). A robust optimization model for stochastic logistic problems. International Journal of Production Economics, 64(1), 385–397. https://doi.org/10.1016/S0925-5273(99)00074-2.

    Article  Google Scholar 

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Acknowledgements

This research has been supported by a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (Grant# RGPIN-2015-04013).

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  1. Faculty of Business Administration, Memorial University of Newfoundland, St. John’s, Canada

    Ginger Y. Ke

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Ke, G.Y. Managing rail-truck intermodal transportation for hazardous materials with random yard disruptions. Ann Oper Res 309, 457–483 (2022). https://doi.org/10.1007/s10479-020-03699-1

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