Advertisement

Speed Optimization for Sustainable Shipping

  • Harilaos N. PsaraftisEmail author
Chapter

Abstract

Among the spectrum of logistics – based measures for sustainable shipping – this chapter focuses on speed optimization. This involves the selection of an appropriate speed by the vessel, so as to optimize a certain objective. As ship speed is not fixed, depressed shipping markets and/or high fuel prices induce slow steaming which is being practiced in many sectors of the shipping industry. In recent years the environmental dimension of slow steaming has also become important, as ship emissions are directly proportional to fuel burned. Win-win solutions are sought, but they will not necessarily be possible. The chapter presents some basics, discusses the main trade-offs and also examines combined speed and route optimization problems. Some examples are presented so as to highlight the main issues that are at play, and the regulatory dimension of speed reduction via speed limits is also discussed.

Abbreviations

AIS

Automatic identification system

BRI

Belt and Road Initiative

CBO

(US) Congressional Budget Office

CIF

Cost insurance freight

CO2

Carbon dioxide

CSC

Clean Shipping Coalition

DWT

Deadweight ton

EEDI

Energy Efficiency Design Index

ECA

Emissions Control Area

FMC

(US) Federal Maritime Commission

GHG

Greenhouse gas

HFO

Heavy fuel oil

IMO

International Maritime Organization

MBM

Market-based measure

MEPC

Marine Environment Protection Committee

MSC

Mediterranean Shipping Company

NGO

Nongovernmental organization

Ro/Ro

Roll on/Roll off

Ro/Pax

Ro/Ro passenger

SECA

Sulfur emissions control area

SOx

Sulfur oxides

TEU

Twenty-foot equivalent unit

USD

United States dollar

VLCC

Very large crude carrier

VSRP

Vessel speed reduction programme

WS

World scale (index)

Notes

Acknowledgments

Work reported in this chapter was funded in part by various sources. Early work was supported in part by the Lloyd’s Register Foundation (LRF) in the context of the Centre of Excellence in Ship Total Energy-Emissions-Economy at the National Technical University of Athens (NTUA), the author’s former affiliation. Later sources include an internal grant by the President of the Technical University of Denmark (DTU) and an internal grant at the DTU Department of Management Engineering, Management Science Division; the BlueSIROS project at DTU, funded by the European Space Agency (DTU Space leader); and the ShipCLEAN project at DTU, funded by the Swedish Energy Agency (Chalmers University project leader). Three recent DTU MSc theses, by Juan Morales, Massimo Giovannini and Fabio Vilas, have also contributed to the chapter (in Sects. 4.2, 5, and 6, respectively).

References

  1. Agarwal, R., & Ergun, Ö. (2008). Ship scheduling and network design for cargo routing in liner shipping. Transportation Science, 42(2), 175–196.Google Scholar
  2. Alphaliner. (2013). Extra and super slow steaming help absorb 7.4% of fleet. Alphaliner Weekly Newsletter, 2013(44), October 2013.Google Scholar
  3. Alvarez, J. F., Tsilingiris, P., Engebrethsen, E. S., & Kakalis, N. M. (2011). Robust fleet sizing and deployment for industrial and independent bulk ocean shipping companies. INFOR: Information Systems and Operational Research, 49(2), 93–107.Google Scholar
  4. Andersson, H., Duesund, J. M., & Fagerholt, K. (2011). Ship routing and scheduling with cargo coupling and synchronization constraints. Computers & Industrial Engineering, 61(4), 1107–1116.Google Scholar
  5. Andersson, H., Fagerholt, K., & Hobbesland, K. (2014). Integrated maritime fleet deployment and speed optimization: Case study from RoRo shipping. Computers & Operations Research 2015, 55, 233–240.Google Scholar
  6. Azaron, A., & Kianfar, F. (2003). Dynamic shortest path in stochastic dynamic networks: Ship routing problem[J]. European Journal of Operational Research, 144(1), 138–156.Google Scholar
  7. Barrass, C. B. (2005). Ship design and performance for masters and mates. UK: Butterworth-Heinemann.Google Scholar
  8. Bauk, S., & Kovac, N. (2004). Modeling ship's route by the adaptation of Hopfield-Tank TSP neural algorithm[J]. Journal of Maritime Research, 1(3), 45–64.Google Scholar
  9. Bekker, J. F., & Schmid, J. P. (2006). Planning the safe transit of a ship through a mapped minefield[J]. ORiON, 22(1), 1–18.Google Scholar
  10. Brouer, B. D., Alvarez, J. F., Plum, C. E., Pisinger, D., & Sigurd, M. M. (2013). A base integer programming model and benchmark suite for liner-shipping network design. Transportation Science, 48(2), 281–312.Google Scholar
  11. Cariou, P. (2011). Is slow steaming a sustainable means of reducing CO2 emissions from container shipping? Transportation Research Part D, 16(3), 260–264.Google Scholar
  12. Cariou, P., & Cheaitou, A. (2012). The effectiveness of a European speed limit versus an international bunker-levy to reduce CO2 emissions from container shipping. Transportation Research Part D, 17, 116–123.Google Scholar
  13. CBO. (2006). The economic costs of disruptions in container shipments. Washington, DC: U.S. Congress, Congressional Budget Office.Google Scholar
  14. Chatzinikolaou, S.D., Ventikos, N. P., (2016), Critical analysis of air emissions from ships: Lifecycle thinking and results, in Psaraftis, H,N. (ed.), Green Transportation Logistics: in Search for Win-Win Solutions, Springer.Google Scholar
  15. Cheaitou, A., & Cariou, P. (2012). Liner shipping service optimisation with reefer containers capacity: An application to northern Europe–South America trade. Maritime Policy & Management, 39(6), 589–602.Google Scholar
  16. Christiansen, M., Fagerholt, K., Nygreen, B., & Ronen, D. (2013). Ship routing and scheduling in the new millennium. European Journal of Operational Research, 228(2013), 467–483.Google Scholar
  17. Cordeau, J.-F., Laporte, G., Legato, P., & Moccia, L. (2005). Models and Tabu search heuristics for the berth-allocation problem. Transportation Science, 39(4), 526–538.Google Scholar
  18. Devanney, J. W. (2007). Solving elastic transportation networks. Center for tankship excellence [online]. Available at: www.c4tx.org.
  19. Devanney, J. W. (2010). The impact of bunker price on VLCC spot rates. Proceedings of the 3rd International Symposium on Ship Operations, Management and Economics. SNAME Greek Section, Athens, October.Google Scholar
  20. Devanney, J. W. (2011a). The impact of charter party speeds on CO2 emissions. Center for tankship excellence [online]. Available at: www.c4tx.org.
  21. Devanney, J. W. (2011b). Speed limits versus slow steaming, center for tankship excellence [online]. Available at: www.c4tx.org.
  22. Du, Y., Chen, Q., Quan, X., Long, L., & Fung, R. Y. K. (2011). Berth allocation considering fuel consumption and vessel emissions. Transportation Research Part E, 47, 1021–1037.Google Scholar
  23. Elbeltagi, E., Hegazy, T., & Grierson, D. (2005). Comparison among five evolutionary-based optimization algorithms[J]. Advanced Engineering Informatics, 19(1), 43–53.Google Scholar
  24. Fagerholt, K., & Psaraftis, H. N. (2015). On two speed optimization problems for ships that sail in and out of emission control areas. Transportation Research Part D, 39, 56–64 2015.Google Scholar
  25. Fagerholt, K., & Ronen, D. (2013). Bulk ship routing and scheduling: Solving practical problems may provide better results. Maritime Policy and Management, 40(1), 48–64.Google Scholar
  26. Fagerholt, K., Laporte, G., & Norstad, I. (2010). Reducing fuel emissions by optimizing speed on shipping routes. Journal of the Operational Research Society, 61, 523–529.Google Scholar
  27. Fagerholt, K., Gausel, N., Rakke, J., & Psaraftis, H. (2015). Maritime routing and speed optimization with emission control areas. Transportation Research Part C, 52, 57–63.Google Scholar
  28. FMC. (2012). Study of the 2008 repeal of the liner conference exemption from European Union Competition Law, Bureau of trade analysis. Washington, DC: Federal Maritime Commission.Google Scholar
  29. Giovannini, M., & Psaraftis, H. N. (2018). The profit maximizing liner shipping problem with flexible frequencies: Logistical and environmental considerations. Flexible Services and Manufacturing Journal. https://doi.org/10.1007/s10696-018-9308-z.
  30. Gkonis, K. G., & Psaraftis, H. N. (2012). “Modelling tankers’ optimal speed and emissions,” Archival Paper, 2012 SNAME Transactions, Vol. 120, 90–115, (Annual Meeting of the Society of Naval Architects and Marine Engineers, Providence, RI, USA, Oct 2012).Google Scholar
  31. Golias, M., Boile, M., Theofanis, S., & Efstathiou, C. (2010). The berth-scheduling problem: Maximizing berth productivity and minimizing fuel consumption and emissions production. Transportation Research Record: Journal of the Transportation Research Board, 2166, 20–27.Google Scholar
  32. Goodchild, A. V., & Daganzo, C. F. (2007). Crane double cycling in container ports: planning methods and evaluation. Transportation Research Part B: Methodological, 41(8), 875–891.Google Scholar
  33. Grønhaug, R., Christiansen, M., Desaulniers, G., & Desrosiers, J. (2010). A branch-and-price method for a liquefied natural gas inventory routing problem. Transportation Science, 44(3), 400–415.Google Scholar
  34. Hagiwara, H. (1989). Weather routing of(sail-assisted) motor vessels[D]. Technische Universiteit Delft.Google Scholar
  35. Haltiner G J, Hamilton H D, ‘Arnason G. Minimal-time ship routing[J]. Journal of Applied Meteorology, 1962, 1(1): 1–7.Google Scholar
  36. Halvorsen-Weare, E. E., & Fagerholt, K. (2011). Robust supply vessel planning. In Network optimization (pp. 559–573). Berlin/Heidelberg: Springer.Google Scholar
  37. Halvorsen-Weare, E. E., & Fagerholt, K. (2013). Routing and scheduling in a liquefied natural gas shipping problem with inventory and berth constraints. Annals of Operations Research, 203(1), 167–186.Google Scholar
  38. Hsu, C. I., & Hsieh, Y. P. (2005). Direct versus terminal routing on a maritime hub-and-spoke container network. Journal of Marine Science and Technology, 13(3), 209–217.Google Scholar
  39. Hvattum, L. M., Norstad, I., Fagerholt, K., & Laporte, G. (2013). Analysis of an exact algorithm for the vessel speed optimization problem. Networks, 62(2), 132–135.Google Scholar
  40. Hwang, H.-S., Visoldilokpun, S., & Rosenberger, J. M. (2008). A branch-and-price-and-cut method for ship scheduling with limited risk. Transportation Science, 42(3), 336–351.Google Scholar
  41. IMO. (2009). Second IMO GHG study. Co authored by Buhaug, Ø., Corbett, J. J., Endresen, Ø., Eyring, V., Faber, J., Hanayama, S., et al. IMO document MEPC59/INF. 10.Google Scholar
  42. IMO. (2014). Third IMO GHG study 2014, Co-authored by Smith, T. W. P., Jalkanen, J. P., Anderson, B. A., Corbett, J. J., Faber, J., Hanayama, S., O'Keeffe, E., Parker, S., Johansson,L., Aldous, L., Raucci, C., Traut, M., Ettinger, S., Nelissen, D., Lee, D. S., Ng, S., Agrawal,A., Winebrake, J., Hoen, M., Chesworth, S., Pandey, A., International Maritime Organization (IMO) London, UK, June.Google Scholar
  43. IMO. (2018). Resolution MEPC.304(72) (adopted on 13 April 2018), Initial IMO strategy on reduction of GHG emissions from ships, IMO doc MEPC 72/17/Add.1, Annex 11.Google Scholar
  44. James, R. W. (1957). Application of wave forecasts to marine navigation. U.S. Washington, DC: Naval Oceanographic Office.Google Scholar
  45. Kapetanis, G. N., Gkonis, K., & Psaraftis, H. N. (2014). Estimating the operational effects of a bunker levy: The case of handymax bulk carriers,” TRA 2014 conference, Paris, France, April 2014.Google Scholar
  46. Ko, H. J. (2009). A DSS approach with Fuzzy AHP to facilitate international multimodal transportation network. KMI International Journal of Maritime Affairs and Fisheries, 1(1), 51–70.Google Scholar
  47. Kumar, R., & Kumar, M. (2010). Exploring genetic algorithm for shortest path optimization in data networks. Global Journal of Computer Science and Technology, 10(11), 8.Google Scholar
  48. Lin, D.-Y., & Liu, H.-Y. (2011). Combined ship allocation, routing and freight assignment in tramp shipping. Transportation Research Part E: Logistics and Transportation Review, 47(4), 414–431.Google Scholar
  49. Lloyds List. (2018). MSC boxships slow down as chief executive Diego Aponte overhauls network, 13 June.Google Scholar
  50. Lo, H. K., & McCord, M. R. (1998). Adaptive ship routing through stochastic ocean currents: General formulations and empirical results. Transportation Research Part A: Policy and Practice, 32(7), 547–561.Google Scholar
  51. Maersk. (2013). Building the world’s Biggest Ship. Available online at: http://www.maersk.com/innovation/leadingthroughinnovation/pages/buildingtheworldsbiggestship.aspx.
  52. Magirou, E. F., Psaraftis, H. N., & Bouritas, T. (2015). The economic speed of an oceangoing vessel in a dynamic setting. Transportation Research Part B, 76, 48–67.Google Scholar
  53. Meng, Q., & Wang, S. (2011). Optimal operating strategy for a long-haul liner service route. European Journal of Operational Research, 215, 105–114.Google Scholar
  54. Mills, J., Donnison, A., & Brightwell, G. (2014). Factors affecting microbial spoilage and shelf-life of chilled vacuum-packed lamb transported to distant markets: A review. Meat Science, 98(1), 71–80.Google Scholar
  55. Moccia, L., Cordeau, J. F., Gaudioso, M., & Laporte, G. (2006). A branch-and-cut algorithm for the quay crane scheduling problem in a container terminal. Naval Research Logistics (NRL), 53(1), 45–59.Google Scholar
  56. Norstad, I., Fagerholt, K., & Laporte, G. (2011). Tramp ship routing and scheduling with speed optimization. Transportation Research Part C, 19, 853–865.Google Scholar
  57. Norlund, E. K., & Gribkovskaia, I. (2013). Reducing emissions through speed optimization in supply vessel operations. Transportation Research Part D, 23, 105–113.Google Scholar
  58. Padhy, C. P., Sen, D., & Bhaskaran, P. K. (2008). Application of wave model for weather routing of ships in the North Indian Ocean[J]. Natural Hazards, 44(3), 373–385.Google Scholar
  59. Panagakos, G., Stamatopoulou, I. V., & Psaraftis, H. P. (2014). The possible designation of the Mediterranean as a SECA: A case study. Transportation Research Part D, 28, 74–90.Google Scholar
  60. Papadakis, N. A., & Perakis, A. N. (1990). Deterministic minimal time vessel routing[J]. Operations Research, 38(3), 426–438.Google Scholar
  61. Psaraftis, H. N., & Kontovas, C. A. (2016). Green maritime transportation: Speed and route optimization. In H. N. Psaraftis (Ed.), Green transportation logistics: In search for win-win solutions. Cham: Springer.Google Scholar
  62. Perakis, A. N. (1985). A second look at fleet deployment. Maritime Policy & Management, 12, 209–214.Google Scholar
  63. Perakis, A. N., & Papadakis, N. A. (1989). Minimal time vessel routing in a time-dependent environment. Transportation Science, 23(4), 266–276.Google Scholar
  64. Powell, B. J., & Perakis, A. N. (1997). Fleet deployment optimization for liner shipping: An integer programming model. Maritime Policy and Management, 24(2), 183–192.Google Scholar
  65. Psaraftis, H. N. (2017). Ship routing and scheduling: the cart before the horse conjecture. Maritime Economics and Logistics, 17(2), 1–14.Google Scholar
  66. Psaraftis, H. N., & Kontovas, C. A. (2009a). CO2 emissions statistics for the world commercial fleet. WMU Journal of Maritime Affairs, 8(1), 1–25.Google Scholar
  67. Psaraftis, H. N., & Kontovas, C. A. (2009b). Ship emissions: Logistics and other tradeoffs. Proceedings of10th International Marine Design Conference. Trondheim, Norway, 26–29 May.Google Scholar
  68. Psaraftis, H. N., & Kontovas, C. A. (2010). Balancing the economic and environmental performance of maritime transportation. Transportation Research Part D, 15(8), 458–462.Google Scholar
  69. Psaraftis, H. N., & Kontovas, C. A. (2013). Speed models for energy-efficient maritime transportation: A taxonomy and survey. Transportation Research Part C: Emerging Technologies, 26, 331–351.Google Scholar
  70. Psaraftis, H. N., & Kontovas, C. A. (2014). Ship speed optimization: Concepts, models and combined speed-routing scenarios. Transportation Research Part C: Emerging Technologies, 44, 52–69.Google Scholar
  71. Psaraftis, H. N., & Kontovas, C. A. (2015). Slow steaming in maritime transportation: Fundamentals, trade-offs, and decision models. In C.-Y. Lee & Q. Meng (Eds.), Handbook of ocean container transportation logistics: Making global supply chains effective. Cham: Springer.Google Scholar
  72. Psaraftis, H. N., Morales Llamas, J., Ding, L., Nehammer, J. (2017). BlueSIROS project WP3, proof of concept. BlueSIROS project technical report, Technical University of Denmark.Google Scholar
  73. Rana, K., & Vickson, R. G. (1991). Routing container ships using lagrangean relaxation and decomposition. Transportation Science, 25(3), 201–214.Google Scholar
  74. Reinhardt, L. B., & Pisinger, D. (2014). A branch and cut algorithm for the container shipping network design problem. Flexible Services and Manufacturing Journal, 24(3), 349–374.Google Scholar
  75. Ronen, D. (1982). The effect of oil price on the optimal speed of ships. Journal of the Operational Research Society, 33, 1035–1040.Google Scholar
  76. Song, D. P., & Xu, J. J. (2012). CO2 emission comparison between direct and feeder liner services: A case study of Asia-Europe services interfacing with the UK. International Journal of Sustainable Transportation, 6(4), 214–237.Google Scholar
  77. Stahlbock, R., & Voss, S. (2008). Operations research at container terminals: A literature update. OR Spectrum, 30(1), 1–52.Google Scholar
  78. TradeWinds. (2010). Slow spur for Maersk VLCCs. TradeWinds magazine, 13 December.Google Scholar
  79. Tsou, M. C., & Hsueh, C. K. (2010). The study of ship collision avoidance route planning by ant colony algorithm[J]. Journal of Marine Science and Technology, 18(5), 746–756.Google Scholar
  80. UNCTAD. (2016). Review of maritime transport 2016, United Nations Conference on Trade and Development, United Nations, New York.Google Scholar
  81. Vilas, R. F (2018). Container shipping performance: A case study on a transpacific service, M.Sc. thesis, Technical University of Denmark, July 2018.Google Scholar
  82. Wang, S., & Meng, Q. (2012). Liner ship route schedule design with sea contingency time and port time uncertainty. Transportation Research Part B, 46(5), 615–633.Google Scholar
  83. Wen, M., Pacino, D., Kontovas, C., & Psaraftis, H. N. (2017). A multiple ship routing and speed optimization problem under time, cost and environmental objectives. Transportation Research Part-D, 52, 303–321.Google Scholar
  84. Zeng, Q., & Yang, Z. (2007). Model integrating fleet design and ship routing problems for coal shipping. In Computational science–ICCS 2007 (pp. 1000–1003). Berlin/Heidelberg: Springer.Google Scholar
  85. Zis, T., & Psaraftis, H. N. (2017). The implications of the new sulphur limits on the European Ro-Ro sector. Transportation Research Part-D, 52, 185–201.Google Scholar
  86. Zis, T., & Psaraftis, H. N. (2018). Operational measures to mitigate and reverse the potential modal shifts due to environmental legislation. Maritime Policy & Management. https://doi.org/10.1080/03088839.2018.1468938.Google Scholar
  87. Zis, T., North, R. J., Angeloudis, P., Ochieng, W. Y., & Bell, M. G. H. (2014). Evaluation of cold ironing and speed reduction policies to reduce ship emissions near and at ports. Maritime Economics & Logistics, 16(4), 371–398.Google Scholar
  88. Zis, T., North, R. J., Angeloudis, P., Ochieng, W. Y., & Bell, M. G. (2015). Environmental balance of shipping emissions reduction strategies. Transportation Research Record: Journal of the Transportation Research Board, 2479, 25–33. Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DTU Management EngineeringTechnical University of DenmarkKongens LyngbyDenmark

Personalised recommendations