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Decision-making methodology in environmentally-conditioned ship operations based on ETD–ETA windows of opportunity

Published online by Cambridge University Press:  02 July 2021

Tommaso Fabbri Open the ORCID record for Tommaso Fabbri [Opens in a new window]  and
Raul Vicen-Bueno
Tommaso Fabbri*
Affiliation:
NATO STO Centre for Maritime Research & Experimentation (CMRE), La Spezia, Italy
Raul Vicen-Bueno
Affiliation:
NATO STO Centre for Maritime Research & Experimentation (CMRE), La Spezia, Italy
*
*Corresponding author. E-mail: [email protected]
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Abstract

This paper presents a methodology to support the decision-making process during the planning of ship operations. The methodology is designed with the aim of identifying and providing the operator with the best Estimated Time of Departure (ETD)–Estimated Time of Arrival (ETA) window of opportunity to execute the journey/operation between two predefined locations. To achieve this purpose, the International Maritime Organization (IMO) stability criteria are exploited in the process to formulate an operational safety criterion based on fuzzy reasoning as a function of the METeorological and OCeanographic (METOC) and sailing conditions. This allows for the analysis of the set of Pareto routes computed by a weather routing systems relying on a multi-objective set-up. The proposed methodology is tested in an operational scenario in the Mediterranean Sea.

Keywords

weather routingdecision supportdecision makingmaritime safetyPareto frontETDETAMETOC
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 6 , November 2021 , pp. 1219 - 1237
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Copyright
Copyright © NATO STO CMRE, 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

Boukhanovsky, A., Lopatoukhin, L. and Soares Guedes, C. (2007). Spectral wave climate of the north sea. Applied Ocean Research, 29, 146–154. doi:10.1016/j.apor.2007.08.004CrossRefGoogle Scholar
Chiu, P.-W. and Bloebaum, C. L. (2009). Hyper-radial visualization (HRV) method with range-based preferences for multi-objective decision making. Structural and Multidisciplinary Optimization, 40(1), 97. doi:10.1007/s00158-009-0361-9CrossRefGoogle Scholar
Degtyarev, A., Boukhanovsky, A., Lopatoukhin, L. and Rozhkov, V. (2000). Stable states of wave climate: applications for risk estimation. 7th International Conference on Stability of Ships and Ocean Vehicles, STAB 2000, Launceston, Tasmania, Australia, Volume: B.Google Scholar
Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271. doi:10.1007/BF01386390CrossRefGoogle Scholar
Dipartimento di Ingegneria Civile, Chimica e Ambientale (DICCA). (2018). Meteocean - dicca. http://www.dicca.unige.it/meteocean/. Accessed 1 June 2018.Google Scholar
EMODnet Bathymetry - The European Marine Observation And Data Network. (2018). EMODnet - understading the topography of the European seas. http://www.emodnet-bathymetry.eu/. Accessed 1 June 2018.Google Scholar
Fabbri, T. and Vicen-Bueno, R. (2019). Weather-routing system based on METOC navigation risk assessment. Journal of Marine Science and Engineering, 7(5), 127. doi: 10.3390/jmse7050127CrossRefGoogle Scholar
Fabbri, T., Vicen-Bueno, R., Grasso, R., Pallotta, G., Millefiori, L. M. and Cazzanti, L. (2015). Optimization of Surveillance Vessel Network Planning in Maritime Command and Control Systems by Fusing METOC and AIS Vessel Traffic Information. In: OCEANS 2015 - Genova, Genova, Italy, 1–7, May 2015. IEEE. doi:10.1109/OCEANS-Genova.2015.7271532CrossRefGoogle Scholar
Fabbri, T., Vicen-Bueno, R. and Hunter, A. (2018). Multi-criteria Weather Routing Optimization based on Ship Navigation Resistance, Risk and Travel Time. In: 2018 International Conference on Computational Science and Computational Intelligence (CSCI), 1–4, May 2018, Las Vegas, NV, USA. doi:10.1109/CNSA.2017.7973982.CrossRefGoogle Scholar
Green, E. H., Winebrake, J. J. and Corbett, J. (2008). Prevention of air pollution from ships - Opportunities for reducing greenhouse gas emissions from ships. Technical report, International Maritime Organization (IMO).Google Scholar
Hagiwara, H. and Spaans, J. A. (1987). Practical weather routing of sail-assisted motor vessels. Journal of Navigation, 40(1), 96–119. doi:10.1017/S0373463300000333CrossRefGoogle Scholar
Holtrop, J. and Mennen, G. G. J. (1982). An approximate power prediction method. International Shipbuilding Progress, 29, 16.CrossRefGoogle Scholar
IMO. (2007). Revised guidance to the master for avoiding dangerous situations in adverse weather and sea conditions. Technical report, International Maritime Organization (IMO).Google Scholar
James, R. W. (1957). Application of wave forecasts to marine navigation. In: Washington - US Navy Hydrographic Office. Reprinted 1959, 1975 by U.S. Naval Oceanographic Office, Washington, D.C.Google Scholar
Kjetil, F. (2004). A computer-based decision support system for vessel fleet scheduling–experience and future research. Decision Support Systems, 37(1), 35–47. doi:10.1016/S0167-9236(02)00193-8Google Scholar
Krata, P. and Szlapczynska, J. (2012). Weather hazard avoidance in modeling safety of motor-driven ship for multicriteria weather routing. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 6, 71–78. doi:10.1201/b11344-27Google Scholar
Li, X., Wang, H. and Wu, Q. (2017). Multi-objective Optimization in Ship Weather Routing. In: 2017 Constructive Nonsmooth Analysis and Related Topics (dedicated to the memory of V.F. Demyanov) (CNSA), 1–4, May 2017, St. Petersburg, Russia. doi:10.1109/CNSA.2017.7973982CrossRefGoogle Scholar
Lin, Y.-H., Fang, M.-C. and Yeung, R. W. (2013). The optimization of ship weather-routing algorithm based on the composite influence of multi-dynamic elements. Applied Ocean Research, 43, 184–194. doi:10.1016/j.apor.2013.07.010CrossRefGoogle Scholar
Loeches, J., Vicen-Bueno, R. and Mentaschi, L. (2015). METOC-driven Vessel Interdiction System (MVIS): Supporting Decision Making in Command and Control (C2) Systems. In: OCEANS 2015-Genova, Genova, Italy, 1–6. IEEE.CrossRefGoogle Scholar
Lu, R., Turan, O., Boulougouris, E., Banks, C. and Incecik, A. (2015). A semi-empirical ship operational performance prediction model for voyage optimization towards energy efficient shipping. Ocean Engineering, 110, 18–28. doi:10.1016/j.oceaneng.2015.07.042CrossRefGoogle Scholar
Maki, A., Akimoto, Y., Nagata, Y., Kobayashi, S., Kobayashi, E., Shiotani, S., Ohsawa, T. and Umeda, N. (2011). A new weather-routing system that accounts for ship stability based on a real-coded genetic algorithm. Journal of Marine Science and Technology, 16(3), 311. doi:10.1007/s00773-011-0128-zCrossRefGoogle Scholar
Mannarini, G., Pinardi, N., Coppini, G., Oddo, P. and Iafrati, A. (2015). VISIR-I: Small vessels, least-time nautical routes using wave forecasts. Geoscientific Model Development, 8(9), 7911–7981. doi:10.5194/gmdd-8-7911-2015Google Scholar
Marie, S. and Courteille, E. (2009). Multi-objective optimization of motor vessel route. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 3(2), 133–141.Google Scholar
Maritime Data Systems. (2018). Searoutes.com. https://www.searoutes.com. Accessed 1 June 2018.Google Scholar
Martins, E. Q. V. (1984). On a multicriteria shortest path problem. European Journal of Operational Research, 16(2), 236–245. doi:10.1016/0377-2217(84)90077-8CrossRefGoogle Scholar
Masoudi, E. (2017). Second generation IMO intact stability vulnerability criteria and its application to ships navigating in Persian Gulf and Oman Sea. International Journal of Maritime Technology, 7, 39–48. doi:10.18869/acadpub.ijmt.7.39Google Scholar
Mentaschi, L., Besio, G., Cassola, F. and Mazzino, A. (2013). Developing and validating a forecast/hindcast system for the Mediterranean sea. Journal of Coastal Research, 2(65), 1551–1556. doi:https://doi.org/10.2112/SI65-262.1CrossRefGoogle Scholar
National Weather Service - Environmental Modeling Center. (2018). Wavewatch III Model. http://polar.ncep.noaa.gov/waves/wavewatch/. Accessed 1 June 2018.Google Scholar
Neves, M., Belenky, V., Kat, J., Spyrou, K. and Umeda, N. (2011). Contemporary Ideas on Ship Stability and Capsizing in Waves. ISBN 978-94-007-1481-6CrossRefGoogle Scholar
Olmer, N., Comer, B., Biswajoy, R. and Rutherford, D. (2017). Greenhouse gas emissions from global shipping, 2013–2015. Technical report, The International Council on Clean Trasportation.Google Scholar
Papanikolaou, A., Vassalos, D., Hamamoto, M. and Molyneux, D. (2000). Contemporary Ideas on Ship Stability. Elsevier. ISBN 0-08-043652-8.Google Scholar
Perera, L. P. and Soares, C. G. (2017). Weather routing and safe ship handling in the future of shipping. Ocean Engineering, 130, 684–695. doi:10.1016/j.oceaneng.2016.09.007CrossRefGoogle Scholar
Peters, W., Belenky, V., Bassler, C., Spyrou, K., Umeda, N., Bulian, G. and Altmayer, B. (2011). The second generation intact stability criteria: An overview of development. Transactions - Society of Naval Architects and Marine Engineers, 121.Google Scholar
Przemyslaw, K. and Joanna, S. (2018). Ship weather routing optimization with dynamic constraints based on reliable synchronous roll prediction. Ocean Engineering, 150, 124–137. doi:10.1016/j.oceaneng.2017.12.049Google Scholar
Sidoti, D., Avvari, G. V., Mishra, M., Zhang, L., Nadella, B. K., Peak, J. E., Hansen, J. A. and Pattipati, K. R. (2017). A multiobjective path-planning algorithm with time windows for asset routing in a dynamic weather-impacted environment. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(12), 3256–3271. doi:10.1109/TSMC.2016.2573271CrossRefGoogle Scholar
Szlapczynska, J. (2007). Multiobjective approach to weather routing. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 1(3), 273–278.Google Scholar
Szlapczynska, J. (2015). Multi-objective weather routing with customised criteria and constraints. Journal of Navigation, 68(2), 338–354. doi:10.1017/S0373463314000691CrossRefGoogle Scholar
U.S. Army Engineer Waterways Experiment Station, Coastal Engineering Research Center. (1985). Direct methods for calculating wavelength. Coastal Engineering Technical Note, CETN-1-17.Google Scholar
Vantorre, M., Eloot, K., Delefortrie, G., Lataire, E., Candries, M. and Verwilligen, J. (2017). Maneuvering in Shallow and Confined Water. In: Encyclopedia of Maritime and Offshore Engineering, 1–17. Wiley.Google Scholar
Vettor, R. and Soares, C. G. (2016). Development of a ship weather routing system. Ocean Engineering, 123, 1–14. doi:10.1016/j.oceaneng.2016.06.035CrossRefGoogle Scholar
Walther, L., Rizvanolli, A., Wendebourg, M. and Jahn, C. (2016). Modeling and optimization algorithms in ship weather routing. International Journal of e-Navigation and Maritime Economy, 4, 31–45. doi:10.1016/j.enavi.2016.06.004CrossRefGoogle Scholar
Zaccone, R., Ottaviani, E., Figari, M. and Altosole, M. (2018). Ship voyage optimization for safe and energy-efficient navigation: A dynamic programming approach. Ocean Engineering, 153, 215–224. doi:10.1016/j.oceaneng.2018.01.100CrossRefGoogle Scholar
Žyczkowski, M., Szlapczynska, J. and Szlapczynski, R. (2019). Review of weather forecast services for ship routing purposes. Polish Maritime Research, 26, 80–89. doi:10.2478/pomr-2019-0069CrossRefGoogle Scholar