Advertisement

Autonomous Vessel Scheduling

  • Wei Zhang
  • Shuai-An Wang
Article
  • 7 Downloads

Abstract

This study deals with an autonomous vessel scheduling problem when collaboration exists between port operators and an autonomous vessel company. A mixed-integer nonlinear programming model is developed, including decisions in assigning autonomous vessels to berths at each port and the optimal arrival time of each vessel at each port in an entire autonomous shipping network. This study aims to minimize the total cost of fuel consumption and the delay penalty of an autonomous vessel company. The nonlinear programming model is linearized and further solved using off-the-shelf solvers. Several experiments are conducted to test the effectiveness of the model and to draw insights for commercializing autonomous vessels. Results show that a company may speed up an autonomous vessel with short-distance voyage once fuel price decreases to gain additional benefits.

Keywords

Autonomous vessel Autonomous ship Ship scheduling Berth allocation 

Mathematics Subject Classification

15A39 

References

  1. [1]
    Phoenix New Media Limited. http://news.ifeng.com/a/20140802/41415061_0.shtml (2014). Accessed 4 June 2018
  2. [2]
  3. [3]
    Kknews. https://kknews.cc/news/plm5pz.html (2016). Accessed 1 May 2018
  4. [4]
    Apostol-Mates, R., Barbu, A.: Human error-the main factor in marine accidents. Sci. Bull. Mircea Cel Batran Nav. Acad. 19(2), 451–454 (2016)Google Scholar
  5. [5]
    Maritime unmanned navigation through intelligence in networks. http://www.unmanned-ship.org/munin/wp-content/uploads/2016/02/MUNIN-final-brochure.pdf (2016). Accessed 28 June 2018
  6. [6]
    Yan, R.J., Pang, S., Sun, H.B., Pang, Y.J.: Development and missions of unmanned surface vehicle. J. Mar. Sci. Appl. 9(4), 451–457 (2010)CrossRefGoogle Scholar
  7. [7]
    Koski, W.R., Allen, T., Ireland, D., Buck, G., Smith, P.R., Macrander, A.M., Halick, M.A., Rushing, C., Sliwa, D.J., McDonald, T.L.: Evaluation of an unmanned airborne system for monitoring marine mammals. Aquat. Mamm. 35(3), 347 (2009)CrossRefGoogle Scholar
  8. [8]
    Breivik, M., Hovstein, V.E., Fossen, T.I.: Straight-line target tracking for unmanned surface vehicles. Model. Identif. Control 29(4), 131 (2008)CrossRefGoogle Scholar
  9. [9]
    Caccia, M., Bibuli, M., Bono, R., Bruzzone, G.: Basic navigation, guidance and control of an unmanned surface vehicle. Auton. Robot. 25(4), 349–365 (2008)CrossRefGoogle Scholar
  10. [10]
    Dai, S.L., Wang, M., Wang, C.: Neural learning control of marine surface vessels with guaranteed transient tracking performance. IEEE Trans. Ind. Electron. 63(3), 1717–1727 (2016)CrossRefGoogle Scholar
  11. [11]
    Liao, Y.L., Su, Y.M., Cao, J.: Trajectory planning and tracking control for underactuated unmanned surface vessels. J. Cent. South Univ. 21(2), 540–549 (2014)CrossRefGoogle Scholar
  12. [12]
    Soltan, R.A., Ashrafiuon, H., Muske, K.R.: State-dependent trajectory planning and tracking control of unmanned surface vessels. In: American Control Conference, pp. 3597–3602. IEEE (2009)Google Scholar
  13. [13]
    Toussaint, G.J., Basar, T., Bullo, F.: Tracking for nonlinear underactuated surface vessels with generalized forces. In: IEEE International Conference on Control Applications, pp. 355–360. IEEE (2000)Google Scholar
  14. [14]
    Yu, R., Zhu, Q., Xia, G., Liu, Z.: Sliding mode tracking control of an underactuated surface vessel. IET Control Theory Appl. 6(3), 461–466 (2012)MathSciNetCrossRefGoogle Scholar
  15. [15]
    Porathe, T., Prison, J., Man, Y.: Situation awareness in remote control centres for unmanned ships. In: Proceedings of Human Factors in Ship Design & Operation, London, p. 93 (2014)Google Scholar
  16. [16]
    Man, Y., Lundh, M., Porathe, T., MacKinnon, S.: From desk to field-Human factor issues in remote monitoring and controlling of autonomous unmanned vessels. Procedia Manuf. 3, 2674–2681 (2015)CrossRefGoogle Scholar
  17. [17]
    Zheng, H., Negenborn, R.R., Lodewijks, G.: Closed-loop scheduling and control of waterborne AGVs for energy-efficient inter terminal transport. Transp. Res. Part E 105, 261–278 (2017)CrossRefGoogle Scholar
  18. [18]
    Brown, G.G., Lawphongpanich, S., Thurman, K.P.: Optimizing ship berthing. Naval Postgraduate School, Monterey CA. http://www.dtic.mil/docs/citations/ADA608104 (1994). Accessed 4 June 2018CrossRefGoogle Scholar
  19. [19]
    Imai, A., Nishimura, E., Papadimitriou, S.: The dynamic berth allocation problem for a container port. Transp. Res. Part B 35(4), 401–417 (2001)CrossRefGoogle Scholar
  20. [20]
    Imai, A., Nishimura, E., Papadimitriou, S.: Berth allocation with service priority. Transp. Res. Part B 37(5), 437–457 (2003)CrossRefGoogle Scholar
  21. [21]
    Zhen, L.: Tactical berth allocation under uncertainty. Eur. J. Oper. Res. 247(3), 928–944 (2015)MathSciNetCrossRefGoogle Scholar
  22. [22]
    Golias, M.M., Saharidis, G.K., Boile, M., Theofanis, S., Ierapetritou, M.G.: The berth allocation problem: optimizing vessel arrival time. Marit. Econ. Logist. 11(4), 358–377 (2009)CrossRefGoogle Scholar
  23. [23]
    Du, Y., Chen, Q., Quan, X., Long, L., Fung, R.Y.: Berth allocation considering fuel consumption and vessel emissions. Transp. Res. Part E 47(6), 1021–1037 (2011)CrossRefGoogle Scholar
  24. [24]
    Zhen, L., Shen, T., Wang, S., Yu, S.: Models on ship scheduling in transshipment hubs with considering bunker cost. Int. J. Prod. Econ. 173, 111–121 (2016)CrossRefGoogle Scholar
  25. [25]
    Notteboom, T.E.: The time factor in liner shipping services. Marit. Econ. Logist. 8(1), 19–39 (2006)CrossRefGoogle Scholar
  26. [26]
    Notteboom, T.E., Vernimmen, B.: The effect of high fuel costs on liner service configuration in container shipping. J. Transp. Geogr. 17(5), 325–337 (2009)CrossRefGoogle Scholar
  27. [27]
    Wang, S., Meng, Q., Liu, Z.: Containership scheduling with transit-time sensitive container shipment demand. Transp. Res. Part B 54, 68–83 (2013)CrossRefGoogle Scholar
  28. [28]
    Mourão, M.C., Pato, M.V., Paixão, A.C.: Ship assignment with hub and spoke constraints. Marit. Policy Manag. 29(2), 135–150 (2002)CrossRefGoogle Scholar
  29. [29]
    Qi, X., Song, D.P.: Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times. Transp. Res. Part E 48(4), 863–880 (2012)CrossRefGoogle Scholar
  30. [30]
    Brouer, B.D., Dirksen, J., Pisinger, D., Plum, C.E., Vaaben, B.: The vessel schedule recovery problem (VSRP)—a MIP model for handling disruptions in liner shipping. Eur. J. Oper. Res. 224(2), 362–374 (2013)CrossRefGoogle Scholar
  31. [31]
    Wang, S., Alharbi, A., Davy, P.: Ship route schedule based interactions between shipping lines and port operators. In: Lee, C.-Y., Meng, Q. (eds.) Ocean Container Transport Logistics Making Global Supply Chain Effective. Elsevier, Amsterdam (2014)Google Scholar
  32. [32]
    Corbett, J.J., Wang, H., Winebrake, J.J.: The effectiveness and costs of speed reductions on emissions from international shipping. Transp. Res. Part D 14(8), 593–598 (2010)CrossRefGoogle Scholar
  33. [33]
    Perakis, A.N., Jaramillo, D.I.: Fleet deployment optimization for liner shipping. Marit. Policy Manag. 18(3), 183–200 (1991)CrossRefGoogle Scholar
  34. [34]
    Ronen, D.: The effect of oil price on containership speed and fleet size. J. Oper. Res. Soc. 62(1), 211–216 (2011)CrossRefGoogle Scholar
  35. [35]
    Wang, S., Meng, Q.: Robust bunker management for liner shipping networks. Eur. J. Oper. Res. 243(3), 789–797 (2015)MathSciNetCrossRefGoogle Scholar
  36. [36]
    Golias, M.M., Boile, M., Theofanis, S., Efstathiou, C.: The berth scheduling problem: maximizing berth productivity and minimizing fuel consumption and emissions production. Transp. Res. Rec. 2166, 20–27 (2010)CrossRefGoogle Scholar
  37. [37]
    Hu, Q.M., Hu, Z.H., Du, Y.: Berth and quay-crane allocation problem considering fuel consumption and emissions from vessels. Comput. Ind. Eng. 70, 1–10 (2014)CrossRefGoogle Scholar
  38. [38]
    Lang, N., Veenstra, A.: A quantitative analysis of container vessel arrival planning strategies. OR Spectr. 32(3), 477–499 (2010)CrossRefGoogle Scholar
  39. [39]
    Wang, S., Meng, Q., Liu, Z.: A note on Berth allocation considering fuel consumption and vessel emissions. Transp. Res. Part E 49(1), 48–54 (2013)CrossRefGoogle Scholar
  40. [40]
    Ship & Bunker. https://shipandbunker.com/prices (2018). Accessed 29 June 2018
  41. [41]
    Du, Y., Meng, Q., Wang, Y.: Budgeting fuel consumption of container ship over round-trip voyage through robust optimization. Transp. Res. Rec. 2477, 68–75 (2015)CrossRefGoogle Scholar
  42. [42]
    Wang, S., Meng, Q.: Sailing speed optimization for container ships in a liner shipping network. Transp. Res. Part E 48(3), 701–714 (2012)CrossRefGoogle Scholar

Copyright information

© Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Logistics and Maritime StudiesHong Kong Polytechnic UniversityHong KongChina
  2. 2.The Hong Kong Polytechnic University Shenzhen Research InstituteShenzhenChina

Personalised recommendations