Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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군집분석을 이용한 전시장비의 취약성 그룹 재분류

Reclassification of the vulnerability group of wartime equipment

  • 이한우 (국방대학교 운영분석학과) ;
  • 김수환 (국방대학교 운영분석학과) ;
  • 주경식 (국방대학교 직무교육원)
  • Lee, Hanwoo (Department of Operations Research, Korea National Defense University) ;
  • Kim, Suhwan (Department of Operations Research, Korea National Defense University) ;
  • Joo, Kyungsik (Occupational Work Training Center, Korea National Defense University)
  • 투고 : 2015.01.30
  • 심사 : 2015.05.18
  • 발행 : 2015.05.31
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초록

현재 한국군의 전시장비소요산정은 미군의 모델인 ELCON을 기반으로 하고 있다. 하지만 전시장비소요산정을 위해 ELCON에서 적용하는 22개의 취약성그룹에 대한 구체적인 분류기준은 알려져 있지 않다. 따라서 이번 연구에서는 2014년 국방전시기본품목 505종에 대해 데이터를 수집하고, 군집분석 방법을 통해 ELCON에서 사용되는 취약성그룹과 다른 새로운 취약성그룹을 제시하였다. 이번 연구를 통해 한국의 상황에 맞는 전시장비들의 취약성그룹을 분류하는 방법을 제시하였을 뿐만 아니라 새로 추가될 장비 역시 해당 장비의 특성을 통해 분류 할 수 있는 기준을 제시함으로 전시 정확한 장비소요를 예측할 수 있도록 하였다.

In the GORRAM, the estimation of resource requirements for wartime equipment is based on the ELCON of the USA. The number of vulnerability groups of ELCON are 22, but unfortunately it is hard to determine how the 22 groups are classified. Thus, in this research we collected 505 types of basic items used in wartime and classified those items into new vulnerability groups using AHP and cluster analysis methods. We selected 11 variables through AHP to classify those items with cluster analysis. Next, we decided the number of vulnerability groups through hierarchical clustering and then we classified 505 types of basic items into the new vulnerability groups through K-means clustering.This paper presents new vulnerability groups of 505 types of basic items fitted to Korean weapon systems. Furthermore, our approach can be applied to a new weapon system which needs to be classified into a vulnerability group. We believe that our approach will provide practitioners in the military with a reliable and rational method for classifying wartime equipment and thus consequentially predict the exact estimation of resource requirements in wartime.

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