Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Borumand, A., Beheshtinia, M.A.: A developed genetic algorithm for solving the multi-objective supply chain-scheduling problem. Kybernetes (2018)
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)
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)
Hollan, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1975)
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)
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)
Qing, C.: Vehicle scheduling model of emergency logistics distribution based on internet of things. Int. J. Appl. Decis. Sci. 11(1), 36–54 (2018)
Shibaev, A.G.: Improvement of methods of chart optimization the sea cargo ships’ work. Moscow (1984)
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)
Stone, P., Veloso, M.: Multiagent systems: a survey from a machine learning perspective. Auton. Robot. 8, 345–383 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Vasileva, O., Kiyaev, V. (2019). Generation of Efficient Cargo Operation Schedule at Seaport with the Use of Multiagent Technologies and Genetic Algorithms. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing, vol 874. Springer, Cham. https://doi.org/10.1007/978-3-030-01818-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-01818-4_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01817-7
Online ISBN: 978-3-030-01818-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)