Optimization of ship scheduling in port based on NSGA-II algorithm

Yan Wenwen; Zhou Xin

doi:10.1117/12.3039693

28 August 2024 Optimization of ship scheduling in port based on NSGA-II algorithm

Yan Wenwen, Zhou Xin

Proceedings Volume 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024); 1325147 (2024) https://doi.org/10.1117/12.3039693
Event: 9th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 2024, Guilin, China

Abstract

In order to improve the efficiency of ship operations in port, the problem of ship and berth scheduling in port is solved. The coordination and optimization model of waterway and berth resources contains multiple complex constraints, which are difficult to solve through precise algorithms or professional solvers. The NSGA-II algorithm is commonly used to solve multi-objective optimization problems, with the goal of minimizing the total inbound and outbound ship scheduling time and the total waiting time of ships in port. A multi-objective optimization mathematical model for the port ship scheduling problem is established. This genetic algorithm introduces a mutation operator into the NSGA-II algorithm, which can improve the quality of the initial solution and accelerate the convergence speed. Generate the initial set using random genetic operators and generate corresponding new solutions through genetic iteration. The effectiveness of the algorithm was verified by data, and the results showed that the algorithm can effectively solve the optimization problem of port ship scheduling, greatly reduce the time of ship operation in port, and better improve the efficiency of ship operation in port.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yan Wenwen and Zhou Xin "Optimization of ship scheduling in port based on NSGA-II algorithm", Proc. SPIE 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 1325147 (28 August 2024); https://doi.org/10.1117/12.3039693

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