Sailing Route Planning Method Considering Various User Categories

AbstractThe article describes the methodology related to determining the multi-criteria routes for sailing ships. Details of sea area discretisation and discretisation of the description of the sailing vessel properties and manoeuvring principles are shown. User requirements were specified (for five different categories of users) and on this basis the criteria for selecting the most suitable shipping route were formulated. The presented algorithm recommends a route for a given user category by means of defined restrictions and configuration parameters. The applied multi-criteria approach proves the universality and usability of the sailing ship route planning method.

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Research on Ship Route Planning Method Based on Neural Network Wave Data Forecast

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/638/1/012033 ◽

2021 ◽

Vol 638 (1) ◽

pp. 012033

Author(s):

Xin Jin ◽

Junting Xiong ◽

Dongliang Gu ◽

Chengtao Yi ◽

Yongxin Jiang

Keyword(s):

Neural Network ◽

Route Planning ◽

Planning Method ◽

Wave Data ◽

Ship Route

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Multicriteria Ship Route Planning Method Based on Improved Particle Swarm Optimization–Genetic Algorithm

Journal of Marine Science and Engineering ◽

10.3390/jmse9040357 ◽

2021 ◽

Vol 9 (4) ◽

pp. 357

Author(s):

Wei Zhao ◽

Yan Wang ◽

Zhanshuo Zhang ◽

Hongbo Wang

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Fuel Consumption ◽

Particle Swarm ◽

Route Planning ◽

Planning Method ◽

Pareto Optimal ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Ship Route

With the continuous prosperity and development of the shipping industry, it is necessary and meaningful to plan a safe, green, and efficient route for ships sailing far away. In this study, a hybrid multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm is presented, which aims to optimize the meteorological risk, fuel consumption, and navigation time associated with a ship. The proposed algorithm not only has the fast convergence of the particle swarm algorithm but also improves the diversity of solutions by applying the crossover operation, selection operation, and multigroup elite selection operation of the genetic algorithm and improving the Pareto optimal frontier distribution. Based on the Pareto optimal solution set obtained by the algorithm, the minimum-navigation-time route, the minimum-fuel-consumption route, the minimum-navigation-risk route, and the recommended route can be obtained. Herein, a simulation experiment is conducted with respect to a container ship, and the optimization route is compared and analyzed. Experimental results show that the proposed algorithm can plan a series of feasible ship routes to ensure safety, greenness, and economy and that it provides route selection references for captains and shipping companies.

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