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논문 기본 정보

자료유형
학술대회자료
저자정보
Suhyeon Kim (Ulsan National Institute of Science and Technology) Wonho Sohn (Ulsan National Institute of Science and Technology) Dongcheol Lim (Ulsan National Institute of Science and Technology) Junghye Lee (Ulsan National Institute of Science and Technology)
저널정보
대한산업공학회 대한산업공학회 추계학술대회 논문집 2019년 대한산업공학회 추계학술대회
발행연도
2019.11
수록면
393 - 400 (8page)

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초록· 키워드

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Port cargo volume analysis is a challenging task for researchers because of non-stationary and highly volatile data affected by external factors. Nevertheless, it is important to establish an analysis system for the port cargo volume as the analysis of the port cargo volume can provide information on the establishment of strategies for port planning and management. In this paper, we propose a new framework to analyze port cargo volume, which consists of three parts: item segmentation, exploratory data analysis, and time series forecasting specifically for liquid bulk cargo volume. We firstly create an item dictionary containing main keywords to characterize each item and then categorize items based on the dictionary. Next, we perform an exploratory data analysis to understand the volume characteristics of each subcategorized item. Lastly, we use representation learning- and deep learning-based time series techniques to forecast their port volume and compare the results with existing statistical models. Experimental results for the three steps show the usefulness of our novel framework in several aspects including forecasting accuracy. It is believed that our proposed method will be a helpful system for stakeholders in port logistics to have insights and to make better decisions.

목차

Abstract
1. Introduction
2. Related work
3. Materials
4. Proposed Framework
5. Results
6. Discussion
7. Conclusion
References

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UCI(KEPA) : I410-ECN-0101-2019-530-001293612