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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
  • 61 Downloads

Abstract

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.

Keywords

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

Notes

Acknowledgements

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.

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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

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