Disruption management for liner shipping

International Series in Operations Research and Management Science

Volumn 220, Issue , 2015, Pages 231-249

  Qi, Xiangtong ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84955141677     PISSN: 08848289     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-11891-8_8     Document Type: Chapter

References (33)

1. Agarwal, R., & Ergun, O. (2008). Ship scheduling and network design for cargo routing in liner shipping. Transportation Science, 42(2), 175–196.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
14. Li, C., Qi, X., & Lee, C. -Y. (2014a). Disruption recovery for liner shipping. Working paper, The Hong Kong University of Science and Technology.
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.
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.
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.
18. Notteboom, T. E. (2006). The time factor in liner shipping services. Maritime Economics & Logistics, 8, 19–39.
19. Paul, J. A., & Maloni, M. J. (2010). Modeling the effects of port disasters. Maritime Economics & Logistics, 12(2), 127–146.
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.
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.
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.
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.
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.
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.
26. Wang, S., & Meng, Q. (2012b). Robust schedule design for liner shipping services. Transportation Research Part E, 48(6), 1093–1106.
27. Wang, Y., & Qi, X. (2014) Vessel Speeding Decisions Under The Chasing Of APirate Boat. Working paper, The Hong Kong University of Science and Technology.
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.
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.
30. Yu, G., & Qi, X. (2004). Disruption management: Framework, models and applications. New Jersey:World Scientific Publisher.
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.
32. Zhen, L. (2014). Storage allocation in transshipment hubs under uncertainties. International Journal of Production Research, 52(1), 72–88.
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.