A two-stage stochastic model for intermodal terminal location and freight distribution under facility disruptions

References

• Agamez-Arias, A., & Moyano-Fuentes, J. (2017). Intermodal transport in freight distribution: A literature review. Transport Reviews, 37(6), 782–807. https://doi.org/10.1080/01441647.2017.1297868
   Web of Science ®Google Scholar
• Arnold, P., Peeters, D., & Thomas, I. (2004). Modelling a rail/road intermodal transportation system. Transportation Research Part E: Logistics and Transportation Review, 40(3), 255–270. https://doi.org/10.1016/j.tre.2003.08.005
   Web of Science ®Google Scholar
• Badyal, V., W. G. Ferrell Jr, Huynh, N., & Padmanabhan, B. (2020). Multi-period optimization model for siting capacitated intermodal facilities. Transportation Research Record, 2674(7), 135–147. https://doi.org/10.1177/0361198120921165
   Web of Science ®Google Scholar
• Barbarosoǧlu, G., & Arda, Y. (2004). A two-stage stochastic programming framework for transportation planning in disaster response. Journal of the Operational Research Society, 55(1), 43–53. https://doi.org/10.1057/palgrave.jors.2601652
   Web of Science ®Google Scholar
• Bertsimas, D., & Tsitsiklis, J. N. (1997). Introduction to linear optimization (Vol. 6) Athena Scientific.
   Google Scholar
• Birge, J. R. (1982). The value of the stochastic solution in stochastic linear programs with fixed recourse. Mathematical Programming, 24(1), 314–325. https://doi.org/10.1007/BF01585113
   Web of Science ®Google Scholar
• Birge, J. R., & Louveaux, F. V. (1988). A multicut algorithm for two-stage stochastic linear programs. European Journal of Operational Research, 34(3), 384–392. https://doi.org/10.1016/0377-2217(88)90159-2
   Web of Science ®Google Scholar
• Bontekoning, Y. M., Macharis, C., & Trip, J. J. (2004). Is a new applied transportation research field emerging? – A review of intermodal rail – truck freight transport literature. Transportation Research Part A: Policy and Practice, 38(1), 1–34. https://doi.org/10.1016/S0191-2615(02)00074-7
   Web of Science ®Google Scholar
• Bureau of Transportation Statistics (2018). Transportation statistics annual report. Retrieved April 28, 2022, from https://www.bts.gov/sites/bts.dot.gov
   Google Scholar
• Bureau of Transportation Statistics (2021). Transportation statistics annual report. Retrieved April 28, 2022, from https://www.bts.gov/sites/bts.dot.gov
   Google Scholar
• Burgholzer, W., Bauer, G., Posset, M., & Jammernegg, W. (2013). Analysing the impact of disruptions in intermodal transport networks: A micro simulation-based model. Decision Support Systems, 54(4), 1580–1586. https://doi.org/10.1016/j.dss.2012.05.060
   Web of Science ®Google Scholar
• Chang, M. S., Tseng, Y. L., & Chen, J. W. (2007). A scenario planning approach for the flood emergency logistics preparation problem under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 43(6), 737–754. https://doi.org/10.1016/j.tre.2006.10.013
   Web of Science ®Google Scholar
• Corporal-Lodangco, I. L., Richman, M. B., Leslie, L. M., & Lamb, P. J. (2014). Cluster analysis of North Atlantic tropical cyclones. Procedia Computer Science, 36, 293–300. https://doi.org/10.1016/j.procs.2014.09.096
   Google Scholar
• Delbart, T., Molenbruch, Y., Braekers, K., & Caris, A. (2021). Uncertainty in intermodal and synchromodal transport: Review and future research directions. Sustainability, 13(7), 3980. https://doi.org/10.3390/su13073980
   Web of Science ®Google Scholar
• Demir, E., Burgholzer, W., Hrušovský, M., Arıkan, E., Jammernegg, W., & Van Woensel, T. (2016). A green intermodal service network design problem with travel time uncertainty. Transportation Research Part B: Methodological, 93, 789–807. https://doi.org/10.1016/j.trb.2015.09.007
   Web of Science ®Google Scholar
• Fotuhi, F., & Huynh, N. (2017). Reliable intermodal freight network expansion with demand uncertainties and network disruptions. Networks and Spatial Economics, 17(2), 405–433. https://doi.org/10.1007/s11067-016-9331-0
   Web of Science ®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
   Web of Science ®Google Scholar
• Grenzeback, L. R., Lukman, A. T., & Systematics, C. (2008). Case study of the transportation sector's response to and recovery from hurricane's katrina and rita. Transportation Research Board.
   Google Scholar
• Groothedde, B., Ruijgrok, C., & Tavasszy, L. (2005). Towards collaborative, intermodal hub networks: A case study in the fast moving consumer goods market. Transportation Research Part E: Logistics and Transportation Review, 41(6), 567–583. https://doi.org/10.1016/j.tre.2005.06.005
   Web of Science ®Google Scholar
• Guimaraes, P., Hefner, F. L., & Woodward, D. P. (1993). Wealth and income effects of natural disasters: An econometric analysis of Hurricane Hugo. Review of Regional Studies, 23(2), 97–114. https://doi.org/10.52324/001c.9106
   Google Scholar
• Hrušovský, M., Demir, E., Jammernegg, W., & Van Woensel, T. (2021). Real-time disruption management approach for intermodal freight transportation. Journal of Cleaner Production, 280, Part 2, 124826. https://doi.org/10.1016/j.jclepro.2020.124826
   Web of Science ®Google Scholar
• Hurricane Research Division, NOAA (2020). Hurricane database. Retrieved September 15, 2020, from https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html
   Google Scholar
• Ishfaq, R. (2013). Intermodal shipments as recourse in logistics disruptions. Journal of the Operational Research Society, 64(2), 229–240. https://doi.org/10.1057/jors.2012.40
   Web of Science ®Google Scholar
• Lemaréchal, C., Nemirovskii, A., & Nesterov, Y. (1995). New variants of bundle methods. Mathematical Programming, 69(1), 111–147, https://doi.org/10.1007/BF01585555.
   Web of Science ®Google Scholar
• Maiyar, L. M., & Thakkar, J. J. (2019). Modelling and analysis of intermodal food grain transportation under hub disruption towards sustainability. International Journal of Production Economics, 217, 281–297. https://doi.org/10.1016/j.ijpe.2018.07.021
   Web of Science ®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
   Web of Science ®Google Scholar
• Mathisen, T. A., & Hanssen, T. E. S. (2014). The academic literature on intermodal freight transport. Transportation Research Procedia, 3, 611–620. https://doi.org/10.1016/j.trpro.2014.10.040
   Google Scholar
• Meng, Q., & Wang, X. (2011). Intermodal hub-and-spoke network design: Incorporating multiple stakeholders and multi-type containers. Transportation Research Part B: Methodological, 45(4), 724–742. https://doi.org/10.1016/j.trb.2010.11.002
   Web of Science ®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
   Web of Science ®Google Scholar
• Misni, F., & Lee, L. S. (2017). A review on strategic, tactical and operational decision planning in reverse logistics of green supply chain network design. Journal of Computer and Communications, 5(8), 83–104. https://doi.org/10.4236/jcc.2017.58007
   Google Scholar
• National Hurricane Center and Central Pacific Hurricane Center (2020). Saffir-simpson hurricane wind scale. Retrieved December 18, 2020, from https://www.nhc.noaa.gov/aboutsshws.php
   Google Scholar
• Poudel, S. R., Marufuzzaman, M., & Bian, L. (2016). Designing a reliable bio-fuel supply chain network considering link failure probabilities. Computers & Industrial Engineering, 91, 85–99. https://doi.org/10.1016/j.cie.2015.11.002
   Web of Science ®Google Scholar
• Rennemo, S. J., Rø, K. F., Hvattum, L. M., & Tirado, G. (2014). A three-stage stochastic facility routing model for disaster response planning. Transportation Research Part E: Logistics and Transportation Review, 62, 116–135. https://doi.org/10.1016/j.tre.2013.12.006
   Web of Science ®Google Scholar
• Rosyida, E., Santosa, B., & Pujawan, N. (2018). A literature review on multimodal freight transportation planning under disruptions. IOP Conference Series: Materials Science and Engineering, 337, 012043. https://doi.org/10.1088/1757-899X/337/1/012043
   Google Scholar
• Rubiales, A. J., Lotito, P. A., & Parente, L. A. (2013). Stabilization of the generalized benders decomposition applied to short-term hydrothermal coordination problem. IEEE Latin America Transactions, 11(5), 1212–1224. https://doi.org/10.1109/TLA.9907
   Web of Science ®Google Scholar
• Saleck Pay, B. (2017). Decomposition algorithms in stochastic integer programming: Applications and computations [Doctoral dissertation].
   Google Scholar
• SC Department of Natural Resources (2020). Executive summary of South Carolina hurricanes. Retrieved December 15, 2020, from https://www.dnr.sc.gov/climate/sco/hurricanes/
   Google Scholar
• S.C. Department of Transportation (2017). South Carolina statewide freight plan. Retrieved March 23, 2019, from https://www.scdot.org/Multimodal/pdf/SC_MTP_Freight_Plan_FINAL.pdf
   Google Scholar
• Slack, B. (2017). Intermodal transportation. In Handbook of logistics and supply-chain management. Emerald Group Publishing Limited.
   Google Scholar
• Snyder, L. V. (2006). Facility location under uncertainty: A review. IIE Transactions, 38(7), 547–564. https://doi.org/10.1080/07408170500216480
   Web of Science ®Google Scholar
• Sörensen, K., Vanovermeire, C., & Busschaert, S. (2012). Efficient metaheuristics to solve the intermodal terminal location problem. Computers & Operations Research, 39(9), 2079–2090. https://doi.org/10.1016/j.cor.2011.10.005
   Web of Science ®Google Scholar
• University Corporation for Atmospheric Research (2020). Community hurricane preparedness, unidata. Retrieved September 23, 2020, from https://www.unidata.ucar.edu/data/NGCS/lobjects/chp/structure/
   Google Scholar
• Van Slyke, R. M., & Wets, R. (1969). L-shaped linear programs with applications to optimal control and stochastic programming. SIAM Journal on Applied Mathematics, 17(4), 638–663. https://doi.org/10.1137/0117061
   Web of Science ®Google Scholar
• Wang, R., Yang, K., Yang, L., & Gao, Z. (2018). Modeling and optimization of a road–rail intermodal transport system under uncertain information. Engineering Applications of Artificial Intelligence, 72 (C), 423–436. https://doi.org/10.1016/j.engappai.2018.04.022
   Web of Science ®Google Scholar
• Wolf, C., Fábián, C. I., Koberstein, A., & Suhl, L. (2014). Applying oracles of on-demand accuracy in two-stage stochastic programming–a computational study. European Journal of Operational Research, 239(2), 437–448. https://doi.org/10.1016/j.ejor.2014.05.010
   Web of Science ®Google Scholar
• Yang, K., Yang, L., & Gao, Z. (2016). Planning and optimization of intermodal hub-and-spoke network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 95, 248–266. https://doi.org/10.1016/j.tre.2016.10.001
   Web of Science ®Google Scholar