Neural Computing and Applications

, Volume 31, Supplement 1, pp 195–207 | Cite as

Evaluation function, game-theoretic machine learning algorithm, and the optimal solution for regional ports resources sharing

  • Cai-hong Ye
  • Xin-ping DongEmail author
  • Pei-jun Zhuang
S.I. : Machine Learning Applications for Self-Organized Wireless Networks


Seaport resource sharing is critical for regional integration of port clusters. Port resources include the resources of a single port and the strategic resources of port cluster. The decision of a port will have a long-term impact on its competitive port decisions, and understanding the long-term value of an action relative to another is the essence of the opportunity cost trade-off of many reinforcement learning algorithms. The authors construct the evaluation function and the game-theoretic machine learning algorithm of a single port for resources sharing in seaports regional integration. The optimal solution with different parameters and the mechanism of sharing decision have been studied. On the basis of mechanism analysis, the variation law of optimal strategy is further expounded. The following conclusions are given: (1) the resource-sharing decision is beneficial for the overall competitiveness of the port cluster; (2) a “boxed pigs” game exists between ports with different resource output levels; (3) besides the decision of sharing resources or not, the optimal sharing level is also important; (4) opponent’s sharing decision will influence the decision critical point; (5) in the process of game, there is a prisoner’s dilemma.


Resources sharing Port clusters Evaluation function Game-theoretic machine learning Optimal solution Simulation 



We wish to thank the anonymous reviewers who helped to improve the quality of the paper. The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation. This paper was partly supported by Humanities and Social Science Project of Ministry of Education of China (Grant Nos. 14jyc630171 and 17YJA870005), the Natural Science Foundation of Zhejiang Province, China (Grant No. LY14D010002).

Compliance with ethical standards

Conflict of interest

All the authors of the manuscript declared that there is no conflict of interest.


  1. 1.
    Langen PWD (2004) The performance of seaport clusters, a framework to analyze cluster performance and an application to the seaport clusters of Durban, Rotterdam, and the Lower Mississippi, Rotterdam, ERIM PhD seriesGoogle Scholar
  2. 2.
    Langen PWD (2006) Chapter 20 Stakeholders, conflicting interests and governance in port clusters. Res Transp Econ 17:457–477CrossRefGoogle Scholar
  3. 3.
    Flämig H, Hesse M (2011) Placing dryports. Port regionalization as a planning challenge—the case of Hamburg, Germany, and the Süderelbe. Res Transp Econ 33(1):42–50CrossRefGoogle Scholar
  4. 4.
    Hasanbeig M, Pavel L (2018) From game-theoretic multi-agent log linear learning to reinforcement learning. paperuri: (80514eb725d38b07fff0fb0c1705aedc)Google Scholar
  5. 5.
    He D et al (2014) A game-theoretic machine learning approach for revenue maximization in sponsored search, arXiv:1406.0728
  6. 6.
    Heinrich J, Silver D (2016) Deep reinforcement learning from self-play in imperfect-information games, arXiv:1603.01121
  7. 7.
    Ishii M, Lee PT, Tezuka K, Chang Y (2013) A game theoretical analysis of port competition. Transp Res E 49:92–106CrossRefGoogle Scholar
  8. 8.
    Kavakeb S, Nguyen TT, Mcginley K, Yang Z, Jenkinson I, Murray R (2015) Green vehicle technology to enhance the performance of a European port: a simulation model with a cost-benefit approach. Transp Res C Emerg Technol 60:169–188CrossRefGoogle Scholar
  9. 9.
    Knatz G (2016) How competition is driving change in port governance, strategic decision-making and government policy in the United States. Res Transp Bus Manag 22:67–77CrossRefGoogle Scholar
  10. 10.
    Luo MF, Liu LM, Gao F (2012) Post-entry container port capacity expansion. Transp Res B 46(1):120–138CrossRefGoogle Scholar
  11. 11.
    Mitchell T (1997) Machine learning. McGraw Hill, New York, p 2. ISBN 0-07-042807-7Google Scholar
  12. 12.
    Notteboom TE, De Langen PW (2015) Container port competition in Europe. Handb Ocean Contain Transp Logist 220:75–95Google Scholar
  13. 13.
    Notteboom TE, Rodrigue J (2005) Port regionalization: towards a new phase in port development. Marit Policy Manag 32:297–313CrossRefGoogle Scholar
  14. 14.
    Notteboom TE, César D, Peter W (2009) Ports in proximity: competition and coordination among adjacent seaports. Ashgate Publishing, Farnham, Surrey, United KingdomGoogle Scholar
  15. 15.
    Pallis AA, Syriopoulos T (2007) Port governance models: financial evaluation of Greek port restructuring. Transp Policy 14(3):232–246CrossRefGoogle Scholar
  16. 16.
    Saeed N, Larsen OI (2010) An application of cooperative game among container terminals of one port. Eur J Oper Res 203:393–403CrossRefzbMATHGoogle Scholar
  17. 17.
    Seo J, Ha Y (2010) The role of port size and incentives in the choice of location by port users: a game-theoretic approach. Asian J Shipp Logist 26(1):049–066CrossRefGoogle Scholar
  18. 18.
    Yu M, Lee CY, Wang JJ (2017) The regional port competition with different terminal competition intensity. Flex Serv Manuf J 29:659–688CrossRefGoogle Scholar
  19. 19.
    Zhou FB (2010) Research on the mode and content of the dislocation development of the Yangtze River Delta ports. Zhejiang daily (theoretical edition)Google Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Faculty of Maritime and TransportationNingbo UniversityNingboChina
  2. 2.Ningbo Institute of TechnologyZhejiang UniversityNingboChina
  3. 3.National Traffic Management Engineering and Technology Research Centre Ningbo University Sub-centreNingboChina
  4. 4.Jiangsu Province Collaborative Innovation Center for ModernUrban Traffic TechnologiesNingboChina

Personalised recommendations