Intelligent dispatching method for cruise ship transportation based on machine learning

By constructing a virtual scene of cruise transportation routes and using a combination method of random forests and genetic algorithms, the problem that scheduling systems in the existing technology is difficult to cope with emergencies and large-scale data processing is solved, and efficient and intelligent scheduling of cruise transportation is achieved, which improves the efficiency and intelligent scheduling of cruise transportation. Prediction accuracy and resource utilization efficiency.

CN119294746APending Publication Date: 2025-01-10CHINA WATERBORNE TRANSPORT RES INST
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Patent Information

Application Number
CN202411398301.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-09
Publication Date
2025-01-10

AI Technical Summary

Technical Problem

The existing cruise transportation scheduling system relies on manual experience and traditional algorithms, and it is difficult to deal with emergencies quickly. Its performance is limited when processing large-scale data, and it cannot achieve global optimal scheduling. It also lacks the ability to predict passenger demand and weather changes, which affects the impact of The accuracy and reliability of the schedule.

Method used

Using machine learning-based method, a virtual scenario for cruise transport routes is constructed, a random forest algorithm is used to dynamic training of the cruise capacity prediction model, and the objective function is solved with improved genetic algorithm to obtain the optimal scheduling scheme for cruise transport.

Benefits of technology

It significantly improves scheduling efficiency and prediction accuracy, realizes intelligence and automation of scheduling processes, reduces manual errors, improves resource utilization efficiency, and reduces operating costs.

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Abstract

The invention discloses a cruise ship transportation intelligent scheduling method based on machine learning. The method comprises the following steps: constructing a cruise ship transportation route virtual scene to obtain an initial cruise ship transportation capacity prediction model; dynamically training the initial cruise ship transport capacity prediction model, training the processed data by using a random forest algorithm, and updating the cruise ship transport capacity prediction model; the random forest algorithm divides historical traffic and transportation data into a training set and a test set, data enhancement is performed on the training set data, and influence factors are extracted; and solving the target function by using an improved genetic algorithm for the updated cruise ship transport capacity prediction model and constraint conditions to obtain an optimal scheduling scheme of cruise ship transport. According to the invention, the intelligent and automatic scheduling process is realized, the dependence on human experience is reduced, the risk of human errors is reduced, and the overall operation efficiency is improved.
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