A container ship traffic model for simulation studies

Wawrzyniak, Jakub ; Drozdowski, Maciej  ; Sanlaville, Éric

International Journal of Applied Mathematics and Computer Science, Tome 32 (2022) no. 4, pp. 537-552.

Résumé

The aim of this paper is to develop a container ship traffic model for port simulation studies. Such a model is essential for terminal design analyses and testing the performance of optimization algorithms. This kind of studies requires accurate information about the ship stream to build test scenarios and benchmark instances. A statistical model of ship traffic is developed on the basis of container ship arrivals in eight world ports. The model provides three parameters of the arriving ships: ship size, arrival time and service time. The stream of ships is divided into classes according to vessel sizes. For each class, service time distributions and mixes of return time distributions are provided. A model of aperiodic arrivals is also proposed. Moreover, the results achieved are used to compare port specific features.

Détail

Keywords: benchmark instances, algorithm evaluation, data analysis, container ship, traffic modeling, marine logistics
Mots-clés : instancje wzorcowe, ocena algorytmu, analiza danych, kontenerowiec, modelowanie ruchu, logistyka morska

Bibliographie

[1] [1] Beasley, J. (2018). OR-Library, http://people.brunel.ac.uk/~mastjjb/jeb/info.html.

[2] [2] Bellsola Olba, X., Daamen, W., Vellinga, T. and Hoogendoorn, S.P. (2017). Network capacity estimation of vessel traffic: An approach for port planning, Journal of Waterway, Port, Coastal, and Ocean Engineering 143(5): 04017019.

[3] [3] Bellsola Olba, X., Daamen, W., Vellinga, T. and Hoogendoorn, S.P. (2018). State-of-the-art of port simulation models for risk and capacity assessment based on the vessel navigational behaviour through the nautical infrastructure, Journal of Traffic and Transportation Engineering (English Edition) 5(5): 335–347.

[4] [4] Benaglia, T., Chauveau, D., Hunter, D.R. and Young, D.S. (2009). Mixtools: An R package for analyzing mixture models, Journal of Statistical Software 32(6): 1–29.

[5] [5] Bierwirth, C. and Meisel, F. (2010). A survey of berth allocation and quay crane scheduling problems in container terminals, European Journal of Operational Research 202(3): 615–627.

[6] [6] Bierwirth, C. and Meisel, F. (2015). A follow-up survey of berth allocation and quay crane scheduling problems in container terminals, European Journal of Operational Research 244(3): 675–689.

[7] [7] Buhrkal, K., Zuglian, S., Ropke, S., Larsen, J. and Lusby, R. (2011). Models for the discrete berth allocation problem: A computational comparison, Transportation Research E: Logistics and Transportation Review 47(4): 461–473.

[8] [8] Çagatay, I. and Siu Lee Lam, J. (2021). Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty, Omega 103: 102445, DOI: 10.1016/j.omega.2021.102445.

[9] [9] Chen, G. and Yang, Z.-Z. (2014). Methods for estimating vehicle queues at a marine terminal: A computational comparison, International Journal of Applied Mathematics and Computer Science 24(3): 611–619, DOI: 10.2478/amcs-2014-0044.

[10] [10] Delignette-Muller, M.L. and Dutang, C. (2015). fitdistrplus: An R package for fitting distributions, Journal of Statistical Software 64(4): 1–34.

[11] [11] Dragovic, B., Park, N.K. and Radmilovic, Z. (2006). Ship-berth link performance evaluation: Simulation and analytical approaches, Maritime Policy Management 33(3): 281–299.

[12] [12] Feitelson, D.G., Tsafrir, D. and Krakov, D. (2014). Experience with using the parallel workloads archive, Journal of Parallel and Distributed Computing 74(10): 2967–2982.

[13] [13] Giallombardo, G., Moccia, L., Salani, M. and Vacca, I. (2010). Modeling and solving the tactical berth allocation problem, Transportation Research B: Methodological 44(2): 232–245.

[14] [14] Gosasang, V., Chandraprakaikul, W. and Kiattisin, S. (2011). A comparison of traditional and neural networks forecasting techniques for container throughput at Bangkok port, Asian Journal of Shipping and Logistics 27(3): 463–482.

[15] [15] Hedjar, R. and Bounkhe, M. (2019). An automatic collision avoidance algorithm for multiple marine surface vehicles, International Journal of Applied Mathematics and Computer Science 29(4): 759–768, DOI: 10.2478/amcs-2019-0056.

[16] [16] Imai, A., Yamakawa, Y. and Huang, K. (2014). The strategic berth template problem, Transportation Research E: Logistics and Transportation Review 72: 77–100, DOI: 10.1016/j.tre.2014.09.013.

[17] [17] Kang, L., Meng, Q. and Tan, K.C. (2020). Tugboat scheduling under ship arrival and tugging process time uncertainty, Transportation Research E: Logistics and Transportation Review 144: 102125, DOI: 10.1016/j.tre.2020.102125.

[18] [18] Lasdon, L., Fox, R. and Ratner, M. (1974). Nonlinear optimization using the generalized reduced gradient method, RAIRO—Operations Research—Recherche Opérationnelle 8(3): 73–103.

[19] [19] Li, C., Qi, X. and Song, D. (2016). Real-time schedule recovery in liner shipping service with regular uncertainties and disruption events, Transportation Research B: Methodological 93: 762–788, DOI: 10.1016/j.trb.2015.10.004.

[20] [20] Liu, C. (2020). Iterative heuristic for simultaneous allocations of berths, quay cranes, and yards under practical situations, Transportation Research E: Logistics and Transportation Review 133: 101814, DOI: 10.1016/j.tre.2019.11.008.

[21] [21] NEO Research Group (2013). Vehicle routing problem, https://neo.lcc.uma.es/vrp/.

[22] [22] Pachakis, D. and Kiremidjian, A.S. (2003). Ship traffic modeling methodology for ports, Journal of Waterway, Port, Coastal, and Ocean Engineering 129(5): 193–202.

[23] [23] Reinelt, G. (1995). TSPLIB, http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/index.html.

[24] [24] Schepler, X., Balev, S., Michel, S. and Sanlaville, É. (2017). Global planning in a multi-terminal and multi-modal maritime container port, Transportation Research E: Logistics and Transportation Review 100: 38–62, DOI: 10.1016/j.tre.2016.12.002.

[25] [25] Shabayek, A. and Yeung,W. (2002). A simulation model for the Kwai Chung container terminals in Hong Kong, European Journal of Operational Research 140(1): 1–11.

[26] [26] Stahlbock, R. and Voß, S. (2008). Operations research at container terminals: A literature update, OR Spectrum 30(1): 1–52.

[27] [27] Taillard, E. (1993). Benchmarks for basic scheduling problems, European Journal of Operational Research 64(2): 278–285.

[28] [28] van Asperen, E., Dekker, R., Polman, M. and de Swaan Arons, H. (2003). Modeling ship arrivals in ports, in S. Chick et al. (Eds), Proceedings of the 2003 Winter Simulation Conference, IEEE, New York, pp. 1737–1744.

[29] [29] Wang, W., Chen, X., Musial, J. and Blazewicz, J. (2020). Two meta-heuristic algorithms for scheduling on unrelated machines with the late work criterion, International Journal of Applied Mathematics and Computer Science 30(3): 573–584, DOI: 10.34768/amcs-2020-0042.

[30] [30] Wawrzyniak, J., Drozdowski, M. and Sanlaville, É. (2020). Selecting algorithms for large berth allocation problems, European Journal of Operational Research 283(3): 844–862.

[31] [31] Wawrzyniak, J., Drozdowski, M. and Sanlaville, É. (2021). Container ship traffic model for simulation studies—Additional resources, http://www.cs.put.poznan.pl/mdrozdowski/stm/.