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Planning Navigation in Inland Waterways with Tidal Depth Restrictions

Published online by Cambridge University Press:  16 November 2017

Jesús Muñuzuri Open the ORCID record for Jesús Muñuzuri [Opens in a new window] ,
Elena Barbadilla ,
Alejandro Escudero-Santana  and
Luis Onieva
Jesús Muñuzuri*
Affiliation:
(Universidad de Sevilla Escuela Tecnica Superior de Ingenieria de Sevilla - Industrial Engineering, Sevilla 41092, Spain)
Elena Barbadilla
Affiliation:
(Universidad de Sevilla Escuela Tecnica Superior de Ingenieria de Sevilla - Industrial Engineering, Sevilla 41092, Spain)
Alejandro Escudero-Santana
Affiliation:
(Universidad de Sevilla Escuela Tecnica Superior de Ingenieria de Sevilla - Industrial Engineering, Sevilla 41092, Spain)
Luis Onieva
Affiliation:
(Universidad de Sevilla Escuela Tecnica Superior de Ingenieria de Sevilla - Industrial Engineering, Sevilla 41092, Spain)
*
(E-mail: munuzuri@us.es)
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Abstract

The planning of vessel navigation along a tidal river is a complex task that can affect the efficiency of inland ports and intermodal chains. The availability of water may represent a significant restriction to port accessibility, having an impact on waiting times, cost and even safety. This paper presents a heuristic procedure to schedule vessels on a tidal waterway, which seeks to optimise navigation according to a multi-criteria objective function combining the number of vessels serviced, the total waiting time, the waterway occupation time and the number of crossing operations, where the term “crossing” implies inbound and outbound vessels passing each other. The procedure requires a previous step of estimation of real-time water depth, which identifies the critical points or navigation bottlenecks along the waterway, and contemplates the possibility of vessels having to anchor in the middle of their journey and wait for next tide rise. We validate the procedure through its application to a set of real and test problems and show that its results are reliable in terms of performance and solution quality.

Keywords

Navigation planningVessel sequencingInland waterwayTidal effectsMulti-criteriaHeuristics
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 3 , May 2018 , pp. 547 - 564
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Abd Kader, A.S. and Suleiman, O. (2006). Sustainable Periodic Navigation Channel Assessment Initiative for Restricted Inland Waterway. Proceedings of the 1st International Conference on Natural Resources Engineering and Technology INRET2006, 24–25 July 2006, Marriot, Putrajaya, Malaysia.Google Scholar
Almaz, O.A. and Altiok, T. (2012). Simulation modelling of the vessel traffic in Delaware River: Impact of deepening on port performance. Simulation Modelling Practice and Theory, 22, 146–165.Google Scholar
Arango, C., Cortés, P., Muñuzuri, J. and Onieva, L. (2011). Berth allocation planning in Seville inland port by simulation and optimisation. Advanced Engineering Informatics, 25, 452–461.Google Scholar
Backalic, T. and Maslaric, M. (2012). Navigation conditions and the risk management in inland waterway transport on the middle Danube. Transport Problems, 7(4), 13–24.Google Scholar
Clarke, G. and Wright, J. (1964). Scheduling of vehicles from a central depot to a number of delivery points. Operations Research, 12, 568–581.Google Scholar
Cortés, P., Muñuzuri, J., Ibáñez, J.N. and Guadix, J. (2007). Simulation of freight traffic in the Seville inland port. Simulation Modelling Practice and Theory, 15, 256–271.Google Scholar
Díez-Minguito, M., Baquerizo, A., Ortega-Sánchez, M., Navarro, G. and Losada, M.A. (2012). Tide transformation in the Guadalquivir estuary (SW Spain) and process-based zonation. Journal of Geophysical Research: Oceans, 117, C03019.CrossRefGoogle Scholar
Duviella, E., Bako, L., Sayed-Mouchaweh, M., Blesa, J., Bolea, Y., Puig, V. and Chuquet, K. (2013). Inland navigation channel model: Application to the Cuinchy-Fontinettes reach. 10th IEEE International Conference on Networking, Sensing and Control (ICNSC), Evry.Google Scholar
Ivančić, P., Kasum, J. and Pavič, I. (2013). Proposed guidelines on developing the optimization model for passage planning in inland waterways navigation. Scientific Journal of Maritime Research, 27(2), 343–350.Google Scholar
Janssens, G.K. (2000). Increasing lock throughput in an inland port through simulation-optimisation. Belgian Journal of Operations Research, Statistics and Computer Science, 40(3–4), 235–247.Google Scholar
Kelareva, E., Brand, S., Kilby, P., Thiébaux, S. and Wallace, M. (2012). CP and MIP Methods for Ship Scheduling with Time-Varying Draft. Proceedings of the Twenty-Second International Conference on Automated Planning and Scheduling, Atibaia, Brazil, 110–118.Google Scholar
Lomónaco, P. and Medina, R. (2005). Harbour and inlet navigation-sedimentation interference: morphodynamics and optimum design. Proceedings of the Canadian Coastal Conference 2005, Dartmouth, Nova Scotia.Google Scholar
Losada, M.A., Díez-Minguito, M. and Reyes-Merlo, M.A. (2017). Tidal-fluvial interaction in the Guadalquivir River Estuary: Spatial and frequency-dependent response of currents and water levels. Journal of Geophysical Research: Oceans, 122, 847–865.Google Scholar
Matte, P., Jay, D.A. and Zaron, E.D. (2013). Adaptation of Classical Tidal Harmonic Analysis to Nonstationary Tides, with Application to River Tides. Journal of Atmospheric and Oceanic Technology, 30, 569–589.CrossRefGoogle Scholar
Rossi, F., Van Beek, P. and Walsh, T. (2006). Handbook of constraint programming. Elsevier.Google Scholar
Smith, D.R. (1985). Top-Down Synthesis of Divide-and-Conquer Algorithms. Artificial Intelligence, 27, 43–96.Google Scholar
Tamura, H., Bacopoulos, P., Wang, D., Hagen, S.C. and Kubatko, E.J. (2014). State estimation of tidal hydrodynamics using ensemble Kalman filter. Advances in Water Resources, 63, 45–56.Google Scholar
Wei, S., Yang, H., Song, J., Abbaspour, K. and Xu, Z. (2013). A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrological Sciences Journal, 58(2), 374–389.CrossRefGoogle Scholar
Xinyu, Z., Jun, L., Zijian, G. and Tieshu, L. (2016). Vessel transportation scheduling optimization based on channel–berth coordination. Ocean Engineering, 112, 145–152.Google Scholar