한국해양대학교

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컨테이너 선박 자동 선적계획을 위한 심층 학습 모형

Title
컨테이너 선박 자동 선적계획을 위한 심층 학습 모형
Alternative Title
Deep Learning Models for Stowage Planning Automation in Containership
Author(s)
이채민
Keyword
stowage planning automationblock stowage; lashing forcedeep learningreinforcement learningsupervised learning
Issued Date
2021
Publisher
한국해양대학교 대학원
URI
http://repository.kmou.ac.kr/handle/2014.oak/12823
http://kmou.dcollection.net/common/orgView/200000506446
Abstract
The stowage planning in containership becomes crucial to optimize the shipping logistics supply chain but complexity of it becomes very high especially for large containership like over 15,000 TEU. The quality of stowage varies from the level of skill of stowage planners, and it impacts a lot to the profit and loss. To overcome the problem, there were many studies in this field for the stowage automation but none of them becomes commercialized due to the NP-Complete problem. This study defines the process of stowage planning automation and its evaluation criteria and builds two different deep learning models - one is the Block Stowage and another is Lashing Force prediction. The Reinforcement Learning is used for Block Stowage to allocate stowage spaces for multiple ports and the Supervised Learning is studied for Lashing Force prediction which is one of the most important aspects in terms of cargo safety as well as utilization, especially for large containership such as more than 10,000 TEU. It is a challenge for stowage planners when large containerships are in the last port of region because mostly the ship is full and the stacks on deck are very high. This study also conducts training and testing the models and provides the results in comparison with stowage planning tool.
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물류시스템학과 > Thesis
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