Analysis of Container Terminal Handling System Based on Petri Net and ExtendSim


  • Danfeng Du School of Traffic and Transportation, Northeast Forestry University
  • Tiantian Liu School of Traffic and Transportation, Northeast Forestry University
  • Chun Guo Research and Development Center of Automotive Product, Midea Cloud Technology Co.



container terminal, handling system, Petri net, correlation matrix, eigenvalue calculation


The container terminal handling system plays an important role in marine transportation, and improving its efficiency has become a big challenge. Therefore, this paper proposes an analytical method that combines a Petri net with simulation tools. Firstly, the container terminal handling system is abstracted into a Petri net system according to the internal logic of the handling process. Next, eigenvalues of the correlation matrix are calculated to analyse the effectiveness of the Petri net system. Then, the Petri net system is simulated using the Extend-Sim software. The result suggests that, after optimising, the handling capacity of the berth is clearly improved. Using the Petri net and simulation tools together to analyse the container terminal system is the innovation and the most important aspect of this paper. Because the combination of a Petri net and simulation can not only ensure the reliability of the model but also optimise the container terminal handling system more intuitively.


Haralambides H. Globalization, public sector reform, and the role of ports in international supply chains. Maritime Economics and Logistics. 2017;19(1):1-51. DOI: 10.1057/s41278-017-0068-6.

Agra A, Oliveira M. MIP approaches for the integrated berth allocation and quay crane assignment and scheduling problem. European Journal of Operational Research. 2018;264(1):138-148. DOI: 10.1016/j.ejor.2017.05.040.

Barbosa F, Rampazzo P, Yamakami A, Camanho A. The use of frontier techniques to identify efficient solutions for the Berth Allocation Problem solved with a hybrid evolutionary algorithm. Computers & Operations Research. 2019;107(2019):43-60. DOI: 10.1016/j.cor.2019.01.017.

Yang D, Zhao Y, Yanagita T. A frame study of correlation analysis between open macroeconomics system and container throughput. Transportation Research Procedia. 2017;25:2784/2796. DOI: 10.1016/j.trpro.2017.05.233.

Xie G, Zhang N, Wang S. Data characteristic analysis and model selection for container throughput forecasting within a decomposition-ensemble methodology. Transportation Research Part E: Logistics and Transportation Review. 2017;108:160-78. DOI: 10.1016/j.tre.2017.08.015.

Notteboom T. The adaptive capacity of container ports in an era of mega vessels: The case of upstream seaports Antwerp and Hamburg. Journal of Transport Geography. 2016;54:295-309. DOI: 10.1016/j.jtrangeo.2016.06.002.

Haralambides HE. Gigantism in container shipping, ports and global logistics: A time-lapse into the future. Maritime Economics and Logistics. 2019;21:1-60. DOI: 10.1057/s41278-018-00116-0.

Song WP. Shanghai International Shipping Research Center reported: The growth rate of container throughput at global ports rebounded to 6.5%, surpassing the pre-pandemic level.,bwkx-202204-4862514.htm [Accessed 9th Nov. 2022].

Vrakas G. The effects of evolving port technology and process optimisation on operational performance: The case study of an Australian container terminal operator. The Asian Journal of Shipping and Logistics. 2021;37(2021):281-290. DOI: 10.1016/j.ajsl.2020.04.001.

Said GAEA, El-Horbaty EM. An optimization methodology for container handling using genetic algorithm. Procedia Computer Science. 2015;65(2015):662-671. DOI: 10.1016/j.procs.2015.09.010.

Msakni MK, et al. Exact methods for the quay crane scheduling problem when tasks are

modeled at the single container level. Computers and Operations Research. 2018;99(2018):218-233. DOI: 10.1016/j.cor.2018.07.005.

Dkhil H, Yassine A, Chabchoub H. Multi-objective optimization of the integrated problem of location assignment and straddle carrier scheduling in maritime container terminal at import. Journal of the Operational Research Society. 2017;1-23. DOI: 10.1057/s41274-017-0184-9.

Dik G, Kozan E. A flexible crane scheduling methodology for container terminals. Flexible Services and Manufacturing Journal. 2017;29:64-96. DOI: 10.1007/s10696-016-9264-4.

Kizilay D, Eliiyi DT, Van Hentenryck P. Constraint and mathematical programming models for integrated port container terminal operations. In: Van Hoeve WJ. (ed.) Integration of constraint programming, artificial intelligence, and operations research. CPAIOR 2018. Lecture Notes in Computer Science. Springer, Cham; 2018. p. 10848.

Jonker T, et al. Coordinated optimization of equipment operations in a container terminal. Flexible Services and Manufacturing Journal. 2021;33:281-311. DOI: 10.1007/s10696-019-09366-3.

Zhuang ZL, et al. Optimization for integrated scheduling of intelligent handling equipment with bidirectional flows and limited buffers at automated container terminals. Computers & Operations Research. 2022;145(2022):105863. DOI: 10.1016/j.cor.2022.105863.

Lee LH, Chew EP, Teng S, Chen Y. Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem. European Journal of Operational Research. 2008;189( 2):476-491. DOI: 10.1016/j.ejor.2007.05.036.

Sun Z, Lee LH, Chew EP, Tan KC. MicroPort: A general simulation platform for seaport container terminals. Advanced Engineering Informatics. 2012;26(1):80-90. DOI: 10.1016/j.aei.2011.08.010.

Clausen U, Kaffka J, Meier F. CONTSIM – Container terminal management with simulation. Procedia - Social and Behavioral Sciences. 2012;54(2012):332-340. DOI: 10.1016/j.sbspro.2012.09.752.

He JL, Zhang WM, Huang YF. A simulation optimization method for internal trucks sharing assignment among multiple container terminals. Advanced Engineering Informatics. 2013;27(2013):598-614. DOI: 10.1016/j.aei.2013.08.001.

Schroër HJ, et al. Evaluation of inter terminal transport configurations at Rotterdam Maasvlakte using discrete event simulation. Proceedings of the Winter Simulation Conference 2014. IEEE; 2014. p. 1771-1782. DOI: 10.1109/WSC.2014.7020026.

Abourraja MN, et al. A multi-agent based simulation model for rail-rail transshipment: An engineering approach for gantry crane scheduling. IEEE Access. 2017;5:13142-13156. DOI: 10.1109/ACCESS.2017.2713246.

Castilla- Rodríguez L, et al. Simulation-optimization for the management of the transshipment operations at maritime container terminals. Expert Systems with Applications. 2020;139:112852. DOI: 10.1016/j.eswa.2019.112852.

Muravev D, Hu H, Rakhmangulov A, Mishkurov P. Multi-agent optimization of the intermodal terminal main parameters by using AnyLogic simulation platform: Case study on the Ningbo-Zhoushan Port. International Journal of Information Management. 2021;57:102133. DOI: 10.1016/j.ijinfomgt.2020.102133.

Rožić T, Ivanković B, Bajor I, Starčević M. A network-based model for optimization of container location assignment at inland terminals. Applied Sciences. 2022;12(12):5833. DOI: 10.3390/app12125833.

Alessandri A, et al. Model-based feedback control of container handling in intermodal terminals. IFAC Proceedings Volumes. 2006;39(12):531-536. DOI: 10.3182/20060829-3-NL-2908.00092.

Fu Q, Zhong CY. Modeling and simulation of container port logistics system based on HTCPN. Logistics Technology. 2014;33(05):419-421+429. DOI: 10.3969/j.issn.1005-152X.2014.03.133.

Cavone G, Dotoli M, Epicoco N, Seatzu C. Intermodal terminal planning by Petri Nets and Data Envelopment Analysis. Control Engineering Practice. 2017;69(2017):9-22. DOI: 10.1016/j.conengprac.2017.08.007.

Si YJ. Study of berth and quay crane assignment and container truck scheduling problem. PhD thesis. Hebei University of Technology; 2017.

Facchini F, Digiesi S, Mossa G. Optimal dry port configuration for container terminals: A non-linear model for sustainable decision making. International Journal of Production Economics. 2020;219(2020):164-78. DOI: 10.1016/j.ijpe.2019.06.004.

Yuan CY. Principle of Petri net. Beijing: Electronics Industry; 1998.

Yu W, et al. Modeling and analysis of medical resource allocation based on Timed Colored Petri net. Future Generation Computer Systems. 2020;111:368-74. DOI: 10.1016/j.future.2020.05.010.

Flores Geronimo M, et al. A multiagent systems with Petri Net approach for simulation of urban traffic networks. Environment and Urban Systems. 2021;89:101662. DOI: 10.1016/j.compenvurbsys.2021.101662.

Jensen K, Kristensen LM. Coloured Petri Nets: Modelling and validation of concurrent systems. Berlin: Springer; 2009.

Yuan JM. Petri Net modeling and functional analysis for reliability of complex systems. Beijing: National Defense Industry; 2011.

Guo ZF. Optimization of outbound containers collection and shipment in container terminal with considering energy consumption analysis. PhD thesis. Dalian Maritime University; 2019.

Teruel E, Silva M. Structure theory of equal conflict systems. Theoretical Computer Science. 1996;153(1-2):271-300. DOI: 10.1016/0304-3975(95)00124-7.

Liao JJ, Wang MZ. Eigenvalues of incidence matrices applied to the analysis of Petri net structure. Journal of Applied Science. 2010;28(4):417-423. DOI: 10.3969/j.ssn.0255-8297.2010.04.015.

Alodhaibi S, Burdett RL, Yarlagadda PKDV. Impact of passenger-arrival patterns in outbound processes of airports. Procedia Manufacturing. 2019;30:323-30. DOI: 10.1016/j.promfg.2019.02.046.

Qin TB, Wang YF. Application oriented simulation modeling and analysis with ExtendSim. Beijing: Tsinghua University; 2011.




How to Cite

Du, D., Liu, T., & Guo, C. (2023). Analysis of Container Terminal Handling System Based on Petri Net and ExtendSim. Promet - Traffic&Transportation, 35(1), 87–105.