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Multi-object-based Vessel Traffic Scheduling Optimisation in a Compound Waterway of a Large Harbour

Published online by Cambridge University Press:  05 November 2018

Xinyu Zhang ,
Ruijie Li ,
Xiang Chen ,
Junjie Li  and
Chengbo Wang
Xinyu Zhang*
Affiliation:
(Key Laboratory of Maritime Dynamic Simulation and Control of Ministry of Transportation, Dalian Maritime University, Dalian, China)
Ruijie Li
Affiliation:
(Key Laboratory of Maritime Dynamic Simulation and Control of Ministry of Transportation, Dalian Maritime University, Dalian, China)
Xiang Chen
Affiliation:
(Department of Civil, Environment and Geomatic Engineering, University College London, London, UK)
Junjie Li
Affiliation:
(Key Laboratory of Maritime Dynamic Simulation and Control of Ministry of Transportation, Dalian Maritime University, Dalian, China)
Chengbo Wang
Affiliation:
(Key Laboratory of Maritime Dynamic Simulation and Control of Ministry of Transportation, Dalian Maritime University, Dalian, China)
*
(E-mail: [email protected])
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Abstract

In order to investigate the benefits of compound waterways more fully, this study reveals vessel navigational mode and traffic conflicts in a compound waterway through a case analysis, following which a type of simplified prototype of a compound waterway is proposed and three key conflict areas are specified. Based on the three key sub-models of slot allocation for vessels in a waterway entrance, traffic flow conversion of a main and auxiliary waterway in a precautionary area, and traffic flow coordination of division and confluence in a Y crossing area, a vessel traffic scheduling optimisation model is presented, with the minimum waterway occupancy time and minimum total waiting time of vessels as the objective. Furthermore, a multi-objective genetic algorithm is proposed to solve the model and a simulation experiment is carried out. By analysing the optimised solution and comparing it with other scheduling schemes in common use, the results indicate that this method can effectively improve navigation safety and efficiency in a compound waterway.

Keywords

Vessel Traffic Services (VTS)OptimisationScheduling modelCompound waterway
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 3 , May 2019 , pp. 609 - 627
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Aydogdu, Y.V., Yurtoren, C., Park, J. and Park, Y. (2012). A Study on Local Traffic Management to Improve Marine Traffic Safety in the Istanbul Strait. The Journal of Navigation, 65(1), 99–112.Google Scholar
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.Google Scholar
Golias, M., Boile, M. and Theofanis, S. (2007). The Berth Allocation Problem: A Formulation Reflecting Time Window Service Deadlines. Proceedings of the 48th Annual Forum of the Transportation Research Forum, Boston, USA.Google Scholar
Golkar, J., Shekhar, A. and Buddhavarapu, S. (1998). Panama Canal Simulation Model. Proceedings of the 30th conference on Winter simulation, Washington, USA.Google Scholar
Huang, L., Liu, Y., Wen, Y., Zhou, C. and Zhang, F. (2017). Abnormal behavior recognition of inland river ferryboat. Journal of Dalian Maritime University, 43(1), 8–13. (in Chinese)Google Scholar
Holland, J. H. (1992). Adaptation in natural and artificial systems. MIT Press.Google Scholar
Imai, A., Chen, H.C., Nishimura, E. and Papadimitriou, S. (2008). The Simultaneous Berth and Quay Crane Allocation Problem. Transportation Research Part E Logistics & Transportation Review, 44(5), 900–920.Google Scholar
Imai, A., Nishimura, E. and Papadimitriou, S. (2001). The Dynamic Berth Allocation Problem for a Container Port. Transportation Research Part B, 35(2), 401–417.Google Scholar
Imaiab, A., Hattori, M. and Papadimitriou, S. (2007). Berth Allocation at Indented Berths for Mega-Containerships. European Journal of Operational Research, 179(2), 579–593.Google Scholar
Köse, E., Başar, E., Demirci, E., Güneroglu, A. and Erkebay, Ş. (2003). Simulation of Marine Traffic in Istanbul Strait. Simulation Modelling Practice & Theory, 11(7–8), 597–608.Google Scholar
Lalla-Ruiz, E., Shi, X. and Voß, S. (2016). The Waterway Ship Scheduling Problem. Transportation Research Part D Transport & Environment, 60(3), 191–209.Google Scholar
Liu, J., Feng, Z., Li, Z., Wang, M., and Wen, L. R. (2016). Dynamic ship domain models for capacity analysis of restricted water channels. The Journal of Navigation, 69(3), 481–503.Google Scholar
Liu, Z., Liu, J., Zhou, F. and Guo, W. (2014). Behavior characteristics of vessel traffic flow and its realization in marine traffic organization. Journal of Dalian Maritime University, 40(2), 22–26. (in Chinese)Google Scholar
Mou, J.M., Tak, C.V.D. and Han, L. (2010). Study On Collision Avoidance in Busy Waterways by Using AIS Data. Ocean Engineering, 37(5), 483–490.Google Scholar
Özbaş, B. and Or, I. (2007). Analysis and Control of Maritime Transit Traffic through the İstanbul Channel: A Simulation Approach. Central European Journal of Operations Research, 15(3), 235–252.Google Scholar
Qi, L., Zheng, Z. and Gang, L. (2016). A Cellular Automaton Model for Ship Traffic Flow in Waterways. Physica A Statistical Mechanics & Its Applications, 471, 705–717.Google Scholar
Sang, L.Z., Tong, X.P., Xu, W.X., Mao, Z. and Yan, X.P. (2017). An Agent-Based Simulation On Navigational Capacity of Multi-Bridge Waterways. Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment, 231(1), 200–211.Google Scholar
Sluiman, F.J. (2017). Transit Vessel Scheduling. Naval Research Logistics, 64(3), 225–248.Google Scholar
UlusU, S, Z. U, S, Z. (2009). Transit Vessel Scheduling in the Strait of Istanbul. The Journal of Navigation, 62(1), 59–77.Google Scholar
UNCTAD. (2017). Review of Maritime Transport. Technical Report. United Nations Conference on Trade and Development (UNCTAD) – Secretariat, 2017. <http://unctad.org/en/PublicationsLibrary/rmt2017_en.pdf>..>Google Scholar
Verstichel, J., Causmaecker, P.D., Spieksma, F.C.R. and Berghe, G.V. (2014). Exact and Heuristic Methods for Placing Ships in Locks. European Journal of Operational Research, 235(2), 387–398.Google Scholar
Wang, W., Peng, Y., Song, X. and Zhou, Y. (2015). Impact of Navigational Safety Level on Seaport Fairway Capacity. The Journal of Navigation, 68(6), 1120–1132.Google Scholar
Wang, W., Peng, Y., Tian, Q. and Song, X. (2017). Key influencing factors on improving the waterway through capacity of coastal ports. Ocean Engineering, 137, 382–393.Google Scholar
Wu, X., Mehta, A.L., Zaloom, V.A. and Craig, B.N. (2016). Analysis of Waterway Transportation in Southeast Texas Waterway Based On AIS Data. Ocean Engineering, 121, 196–209.Google Scholar
Xiao, F., Han, L., Gulijk, C.V. and Ale, B. (2015). Comparison Study On AIS Data of Ship Traffic Behavior. Ocean Engineering, 95(3), 84–93.Google Scholar
Zhang, X., Chen, X., Ji, M. and Yao, S. (2017). Vessel Scheduling Model of a One-Way Port Channel. Journal of Waterway Port Coastal & Ocean Engineering, 143(5), 04017009.Google Scholar
Zhang, X., Lin, J., Guo, Z. and Liu, T. (2016). Vessel Transportation Scheduling Optimization Based On Channel-Berth Coordination. Ocean Engineering, 112, 145–152.Google Scholar
Zhen, L., Wang, K., Wang, S. and Qu, X. (2018). Tug Scheduling for Hinterland Barge Transport: A Branch-And-Price Approach. European Journal of Operational Research, 265, 119–132.Google Scholar