Earticle

초록

영어

Parametric life-cycle cost(LCC) models have been integrated with traditional design tools, and used in prior work to demonstrate the rapid solution of holistic, analytical tradeoffs between detailed design variations. During early designs stages there may be competing concepts with dramatic differences. Additionally, detailed information is scarce, and decisions must be models. for a diverse range of concepts, and the lack of detailed information make the integration make the integration of traditional LCC models impractical. This paper explores an approximate method for providing preliminary life-cycle cost. Learning algorithms trained using the known characteristics of existing products be approximated quickly during conceptual design without the overhead of defining new models. Artificial neural networks are trained to generalize on product attributes and life cycle cost date from pre-existing LCC studies. The Product attribute data to quickly obtain and LCC for a new and then an application is provided. In additions, the statistical method, called regression analysis, is suggested to predict the LCC. Tests have shown it is possible to predict the life cycle cost, and the comparison results between a learning LCC model and a regression analysis is also shown

목차

Abstract
 1. 서론
  1.1 연구 범위와 방법
 2. 본론
  2.1 Life Cycle Cost의 이론적 고찰
  2.2 학습 LCC 모델 및 회귀 모델의 개발
  2.3 회귀모델 및 학습 LCC 모델 실험
 3. 결론
 4. 참고문헌

키워드

저자

• 김동관 [ Kim Dong Kwan | 인하대학교 대학원 산업공학과 박사과정 ]
• 정수일 [ Jung Soo Il | 인하대학교 공과대학 산업공학과 교수 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  대한안전경영과학회지 [Journal of Korea Safety Management & Science]

• 간기

  계간

• pISSN

  1229-6783

• eISSN

  2288-1484

• 수록기간

  1999~2025

• 십진분류

  KDC 530 DDC 620