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Disruption Management for Liner Shipping

  • Xiangtong QiEmail author
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 220)

Abstract

In many operations management problems, including vessel scheduling in liner shipping, people need to make and announce an operations plan in advance, with tremendous efforts being paid to optimize the plan. When the plan is executed in real time, however, it is constantly subject to different unexpected disruptions, making the original plan sub-optimal or even infeasible. Therefore we have the need of dynamically revising the operations plan at the execution stage, a problem often referred to as disruption management. In the context of liner shipping, disruption events may include bad weather, unusual port congestion and even port closure, etc., with the direct consequence of delaying the vessels from their schedules. In this chapter, we will study how disruptions can be effectively managed in liner shipping. We will show how to model and formulate such problems, and present a few key results of the solution schemes and managerial insights observed.

Keywords

Liner Shipping Managerial Insight Disruption Management Localize Port Multiple Vessel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Agarwal, R., & Ergun, O. (2008). Ship scheduling and network design for cargo routing in liner shipping. Transportation Science, 42(2), 175–196.CrossRefGoogle Scholar
  2. Brouer, B. D., Dirksen, J., Pisinger, D., Plum, C. E. M., & Vaaben, B. (2013). The vessel schedule recovery problem (VSRP)—A MIP model for handling disruptions in liner shipping. European Journal of Operational Research, 224(2), 362–374.CrossRefGoogle Scholar
  3. Cariou, P. (2011). Is slow steaming a sustainable means of reducing CO2 emissions from container shipping? Transportation Research Part D, 16(3), 260–264.CrossRefGoogle Scholar
  4. Chang, C. -H., Xu, J., & Song, D. -P. (2014). An analysis of safety and security risks in container shipping operations: A case study of Taiwan. Safety Science, 63, 168–178.CrossRefGoogle Scholar
  5. Christiansen, M., Fagerholt, K., Nygreen, B., & Ronen, D. (2013). Ship routing and scheduling in the new millennium. European Journal of Operational Research, 228(3), 467–478.CrossRefGoogle Scholar
  6. Clausen, J., Larsen, A., Larsen, J., & Rezanova, N.J. (2010). Disruption management in the airline industry-concepts, models and methods. Computers & Operations Research, 37, 809–821.CrossRefGoogle Scholar
  7. Corbett, J., Wang, H., & Winebrake, J. (2009). The effectiveness and costs of speed reductions on emissions from international shipping. Transportation Research Part D, 14, 539–598.CrossRefGoogle Scholar
  8. Dong, J.X., & Song, D. -P. (2012). Quantifying the impact of inland transport time on container fleet sizing in a liner shipping service with uncertainties. OR Spectrum, 34(1), 155–180.CrossRefGoogle Scholar
  9. Francesco, M.D., Lai, M., & Zuddas, P. (2013). Maritime repositioning of empty containers under uncertain port disruptions Computers and Industrial Engineering, 64, 827–837.CrossRefGoogle Scholar
  10. Fransoo, J.C., & Lee, C. -Y. (2013). The critical role of ocean container transport in global supply chain performance. Production and Operations Management, 22(2), 253–268.CrossRefGoogle Scholar
  11. Kontovas, C., & Psaraftis, H.N. (2011). Reduction of emissions along the maritime intermodal container chain: Operational models and policies. Maritime Policy & Management, 38(4), 451–469.CrossRefGoogle Scholar
  12. Lam, J. S. L. (2012). Risk management in maritime logistics and supply chains. In D. W Song & P. M. Panayides (Eds.), Maritime logistics: Contemporary issues (pp. 117–132). Bingley: Emerald.Google Scholar
  13. Lam, J. S. L., & Yip, T. L. (2012). Impact of port disruption on supply chains: A petri net approach. Lecture Notes in Computer Science, 7555, 72–85.CrossRefGoogle Scholar
  14. Li, C., Qi, X., & Lee, C. -Y. (2014a). Disruption recovery for liner shipping. Working paper, The Hong Kong University of Science and Technology.Google Scholar
  15. Li, C., Qi, X., & Song, D.P. (2014b). Real-time scheduling and rescheduling in liner shipping service with regular uncertainties and disruption events. Working paper, The Hong Kong University of Science and Technology.Google Scholar
  16. Meng, Q., Wang, T., & Wang, S. (2012). Short-term liner ship fleet planning with container transshipment and uncertain container shipment demand. European Journal of Operational Research, 223(1), 96–105.CrossRefGoogle Scholar
  17. Meng, Q., Wang, S., Andersson, H., & Thun, K. (2014). Containership routing and scheduling in liner shipping: Overview and future research directions. Transportation Science. doi:10.1287/trsc.2013.0461.Google Scholar
  18. Notteboom, T. E. (2006). The time factor in liner shipping services. Maritime Economics & Logistics, 8, 19–39.CrossRefGoogle Scholar
  19. Paul, J. A., & Maloni, M. J. (2010). Modeling the effects of port disasters. Maritime Economics & Logistics, 12(2), 127–146.CrossRefGoogle Scholar
  20. Psaraftis, H. N., & Kontovas, C. A. (2013). Speed models for energy-efficient maritime transportation: A taxonomy and survey. Transportation Research Part C, 26, 331–351.CrossRefGoogle Scholar
  21. Qi, X., & Song, D.P. (2012). Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times. Transportation Research Part E, 48(4), 863–880.CrossRefGoogle Scholar
  22. Thengvall, B., Bard, J. F., & Yu, G. (2000). Balancing user preferences for aircraft schedule recovery during irregular operations. IIE Transactions, 32(3), 181–193.Google Scholar
  23. Tran, N.K., & Haasis, H. -D. (2013) Literature survey of network optimization in container liner shipping. Flexible Services and Manufacturing Journal. doi:10.1007/s10696-013-9179-2.Google Scholar
  24. Vernimmen, B., Dullaert, W., & Engelen, S. (2007). Schedule unreliability in liner shipping: Origins and consequences for the hinterland supply chain. Maritime Economics & Logistics, 9, 193–213.CrossRefGoogle Scholar
  25. Wang, S., & Meng, Q. (2012a). Liner ship route schedule design with sea contingency time and port time uncertainty. Transportation Research Part B, 46(5), 615–633.Google Scholar
  26. Wang, S., & Meng, Q. (2012b). Robust schedule design for liner shipping services. Transportation Research Part E, 48(6), 1093–1106.Google Scholar
  27. Wang, Y., & Qi, X. (2014) Vessel Speeding Decisions Under The Chasing Of A Pirate Boat. Working paper, The Hong Kong University of Science and Technology.Google Scholar
  28. Wang, S., Meng, Q. & Liu Z. (2013a). Bunker consumption optimization methods in shipping: A critical review and extensions. Transportation Research Part E, 53, 49–62.Google Scholar
  29. Wang, T., Meng, Q., Wang, S., & Tan, Z. (2013b) Document risk management in liner ship fleet deployment: A joint chance constrained programming model. Transportation Research Part E, 60, 1–12.Google Scholar
  30. Yu, G., & Qi, X. (2004). Disruption management: Framework, models and applications. New Jersey: World Scientific Publisher.CrossRefGoogle Scholar
  31. Yu, G., Arguello, M., Song, G., McCowan, S., & White, A. (2003). A new era for crew recovery at continental airlines. Interfaces, 33, 5–22.CrossRefGoogle Scholar
  32. Zhen, L. (2014). Storage allocation in transshipment hubs under uncertainties. International Journal of Production Research, 52(1), 72–88.CrossRefGoogle Scholar
  33. Zhen, L., Lee, L.H., & Chew, E.P. (2011). A decision model for berth allocation under uncertainty. European Journal of Operational Research, 212(1), 54–68.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Industrial Engineering and Logistics ManagementThe Hong Kong University of Science and TechnologyClearwater BayHong Kong

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