한국해양대학교

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조선 생산 리드타임 기준정보 관리를 위한 앙상블 학습 기법 적용 연구

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dc.contributor.advisor 남종호 -
dc.contributor.author 정주현 -
dc.date.accessioned 2020-07-22T04:18:27Z -
dc.date.available 2020-07-22T04:18:27Z -
dc.date.issued 2020 -
dc.identifier.uri http://repository.kmou.ac.kr/handle/2014.oak/12413 -
dc.identifier.uri http://kmou.dcollection.net/common/orgView/200000283918 -
dc.description.abstract Building large structures such as ships requires efficient management of production information. In shipyards, enterprise management information that includes production information is called master data, and which includes BOM (bill of material), WBS (work breakdown structure), basic unit and lead time. Master data related to production is closely related to time information. In the shipbuilding industry, however, the high variability of the shipbuilding process has made it difficult to gain the reliability of master data management. Low accuracy of master data can lead to financial losses as well as confusion of work. To solve these problems, shipyards and related academia have been making various efforts to improve the master data system. However, most of the existing research is focused on traditional engineering perspectives and does not reflect the rapidly changing shipbuilding production environment. Recently, machine learning methodologies have been widely applied to the manufacturing industry, along with the rapid development of big data related technologies. Machine learning is a correlation analysis technique of vast amounts of data, which is known to be able to overcome the limitations of causation analysis with traditional engineering methodologies. Research is also being conducted in the shipbuilding industry by applying various machine learning algorithms to systematically manage the production master data. In this paper, I would like to introduce the study of applying ensemble learning, which is known to maximize the performance of machine learning, to the analysis of shipbuilding production master data. In addition, we would like to examine the applicability of production management tasks in actual shipyards by comparing the prediction results of the ensemble learning with the results of applying various learning algorithms and attaching a better performance learning model to the simulation. -
dc.description.tableofcontents List of Tables ······· ⅲ List of Figures ······· ⅳ Abstract ······· ⅵ 1. 서 론 ······· 1 1.1 연구배경 ······· 1 1.2 관련 연구 동향 ······· 2 1.3 연구 목적 ······· 5 2. 적용 개념 ······· 6 2.1 분석 알고리즘 소개 ······· 6 2.1.1 기계학습 알고리즘 ······· 6 2.1.2 심층학습 알고리즘 ······· 8 2.1.3 앙상블학습 알고리즘 ······· 10 2.2 데이터 분석 과정 ······· 17 3. 조선 생산 리드타임 예측을 위한 데이터 분석 ······· 29 3.1 해양플랜트 배관재 공급망 데이터 분석 ······· 30 3.2 조선소 블록 조립 공정 데이터 분석 ······· 37 3.3 조선소 블록 절단 공정 데이터 분석 ······· 43 4. 분석 결과 ······· 49 4.1 해양플랜트 배관재 공급망 데이터 분석 결과 ······· 49 4.2 조선소 블록 조립 공정 데이터 분석 결과 ······· 54 4.3 조선소 블록 절단 공정 데이터 분석 결과 ······· 57 5. 리드타임 예측모델 활용 방안 ······· 61 5.1 Python/Simpy를 이용한 이산 사건 시뮬레이션 ······· 61 5.2 시뮬레이션 대상 선정 ······· 63 5.3 시뮬레이션 적용 및 분석 ······· 64 6. 결론 ······· 67 6.1 연구 결론 ······· 67 References ······· 69 Bibliography ······· 70 Appendix ······· 71 -
dc.format.extent 72 -
dc.language kor -
dc.publisher 한국해양대학교 대학원 -
dc.rights 한국해양대학교 논문은 저작권에 의해 보호받습니다. -
dc.title 조선 생산 리드타임 기준정보 관리를 위한 앙상블 학습 기법 적용 연구 -
dc.type Dissertation -
dc.date.awarded 2020. 2 -
dc.contributor.alternativeName Jeong Ju Hyeon -
dc.contributor.department 대학원 조선해양시스템공학과 -
dc.contributor.affiliation 한국해양대학교 대학원 조선해양시스템공학과 -
dc.description.degree Master -
dc.identifier.bibliographicCitation 정주현. (2020). 조선 생산 리드타임 기준정보 관리를 위한 앙상블 학습 기법 적용 연구. -
dc.subject.keyword 조선, 생산 리드타임, 앙상블학습 -
dc.title.translated A study on the application of ensemble learning for the management of the shipbuilding production lead time master data -
dc.contributor.specialty 조선해양시스템공학전공 -
dc.identifier.holdings 000000001979▲200000001565▲200000283918▲ -
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