Generation of Efficient Cargo Operation Schedule at Seaport with the Use of Multiagent Technologies and Genetic Algorithms

  • Olga VasilevaEmail author
  • Vladimir Kiyaev
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 874)


Seaport is a complex economic techno - technological facility. In the modern meaning of this concept it is a specially built and equipped enterprise on the coast, designed for sheltering, loading/unloading and servicing of ships. Information support of such object’s operations is a rather difficult information-computational task for many reasons - the main one being the generation of an efficient schedule for loading and unloading operations. For these reasons, there is a need to develop an automated system capable of generating an optimal schedule of loading/unloading in a seaport, taking into account the dynamic effect of external factors on its efficient operation. The approach to scheduling is based on multi-agent technologies and a genetic algorithm for generating a schedule for port operation improving the quality of the obtained schedule. It is supposed to use the data received from different agents and corrected during the interaction of such agents. The scheduling is carried out with the genetic algorithms.


Seaport work schedule Multiagent technologies Genetic algorithms 


  1. 1.
    Baniamerian, A., Bashiri, M., Zabihi, F.: Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction. J. Ind. Eng. Int. 14(1), 15–30 (2018)CrossRefGoogle Scholar
  2. 2.
    Borumand, A., Beheshtinia, M.A.: A developed genetic algorithm for solving the multi-objective supply chain-scheduling problem. Kybernetes (2018)Google Scholar
  3. 3.
    Changan, R., Zhao, J., Chen, L.: A fast information scheduling algorithm for large scale logistics supply chain. J. Discret. Math. Sci. Cryptogr. 20(6–7), 1459–1463 (2017)CrossRefGoogle Scholar
  4. 4.
    He, Z., Guo, Z., Wang, J.: Integrated scheduling of production and distribution operations in a global MTO supply chain. Enterp. Inf. Syst., 1–25 (2018)Google Scholar
  5. 5.
    Hollan, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1975)Google Scholar
  6. 6.
    Ivaschenko, A., Minaev A.: Multi-agent solution for adaptive data analysis in sensor networks at the intelligent hospital ward. In: International Conference on Active Media Technology, pp. 453–463. Springer (2017)Google Scholar
  7. 7.
    Liu, J., Luo, Z., Duan, D., Lai, Z., Huang, J.: A GA approach to vehicle routing problem with time windows considering loading constraints. High Technol. Lett. 23(1), 54–62 (2017)Google Scholar
  8. 8.
    Qing, C.: Vehicle scheduling model of emergency logistics distribution based on internet of things. Int. J. Appl. Decis. Sci. 11(1), 36–54 (2018)Google Scholar
  9. 9.
    Shibaev, A.G.: Improvement of methods of chart optimization the sea cargo ships’ work. Moscow (1984)Google Scholar
  10. 10.
    Sologub, N.K., Sharov, V.A., Abramov, A.A.: Plan development for the interaction of different transport’s types in a node. A manual on the course “ETS and the basis for the interaction of various modes of transport” for training specialists in the field of transport communications, Moscow (1982)Google Scholar
  11. 11.
    Stone, P., Veloso, M.: Multiagent systems: a survey from a machine learning perspective. Auton. Robot. 8, 345–383 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Saint-Petersburg State UniversitySaint-PetersburgRussia
  2. 2.Saint-Petersburg State University of EconomicsSaint-PetersburgRussia

Personalised recommendations