The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
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Using a Hybrid Model of DEA and Decision Tree Algorithm C5.0 to Evaluate the Efficiency of Ports

DEA와 의사결정 나무(C5.0)의 하이브리드 모델을 사용한 항만의 효율성 평가

  • 홍한국 (동의대학교 경영정보학부) ;
  • 임병학 (부산외국어대학교 경영학부) ;
  • 김삼문 (동의대학교 응용소프트웨어공학과)
  • Received : 2019.06.17
  • Accepted : 2019.07.11
  • Published : 2019.07.28
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Abstract

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. For example DEA is good at estimating "relative" efficiency of a DMU(Decision Making Unit), it only tells us how well we are doing compared with our peers but not compared with a "theoretical maximum." Thus, in order to measure efficiency of a new DMU, we have to develop entirely new DEA with the data of previously used DMUs. Also we cannot predict the efficiency level of the new DMU without another DEA analysis. We aim to show that DEA can be used to evaluate the efficiency of ports and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with C5.0. We can generate classification rules C5.0 in order to classify any new Port without perturbing previously existing evaluation structures by proposed methodology.

비모수 생산성 분석기법인 Data Envelopment Analysis (DEA)는 여러 분야의 효율성 평가에 적용되고 있다. DEA 방법론이 다양한 분야의 문제에 대한 현실적 적용에 있어 단점이 있다. 예를 들어 DEA는 각 의사 결정단위의 상대적인 효율성 평가에 적합하다. 그러나 이론적인 최대치와의 비교가 아닌 벤치마킹해야 할 참조그룹과 얼마만큼 개선해야 할지를 단지 알려 줄 뿐이다. 즉, 새로운 의사결정단위의 효율성을 측정하기 위해 우리는 과거에 사용된 의사결정단위 데이터와 함께 완전히 새로운 DEA를 적용해야만 한다. 또한 우리는 다시 DEA를 적용하지 않고서 새로운 의사결정단위의 효율성 수준을 예상할 수 없다. 우리는 이러한 DEA의 단점을 보완하기 위해 C5.0과 결합한 하이브리드 분석방법론을 제안한다. 35개의 항만의 효율성 평가를 통해 새로운 의사결정단위는 기존의 의사결정단위와 함께 다시 DEA를 실행할 필요 없이 제안한 방법론을 적용하여 어느 등급에 속하는지 예상할 수 있다.

Keywords

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Fig. 1. Research Framework

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Fig. 2. A Decision Tree generated by C5.0

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Fig. 3. Rule induced from the decision tree

Table 1. Summarization of Decision Tree Algorithms [13]

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Table 2. Variable Description

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Table 3. DEA and Tier Analysis Results

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Table 4. Training cases for C5.0.

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