산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제38권2호
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  • Pages.120-128
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  • 2015
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  • 2005-0461(pISSN)
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  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
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다수의 개별시장 하에서 QFD의 기술속성의 최적 값을 결정하기 위한 동적 계획법

Dynamic Programming Approach for Determining Optimal Levels of Technical Attributes in QFD under Multi-Segment Market

  • 유재욱 (동아대학교 경영대학 경영학과)
  • Yoo, Jaewook (Department of Business Administration, Dong-A University)
  • 투고 : 2015.04.30
  • 심사 : 2015.06.08
  • 발행 : 2015.06.30
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초록

Quality function deployment (QFD) is a useful method in product design and development to maximize customer satisfaction. In the QFD, the technical attributes (TAs) affecting the product performance are identified, and product performance is improved to optimize customer requirements (CRs). For product development, determining the optimal levels of TAs is crucial during QFD optimization. Many optimization methods have been proposed to obtain the optimal levels of TAs in QFD. In these studies, the levels of TAs are assumed to be continuous while they are often taken as discrete in real world application. Another assumption in QFD optimization is that the requirements of the heterogeneous customers can be generalized and hence only one house of quality (HoQ) is used to connect with CRs. However, customers often have various requirements and preferences on a product. Therefore, a product market can be partitioned into several market segments, each of which contains a number of customers with homogeneous preferences. To overcome these problems, this paper proposes an optimization approach to find the optimal set of TAs under multi-segment market. Dynamic Programming (DP) methodology is developed to maximize the overall customer satisfaction for the market considering the weights of importance of different segments. Finally, a case study is provided for illustrating the proposed optimization approach.

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참고문헌

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피인용 문헌

  1. Using Fuzzy Numbers in Quality Function Deployment Optimization vol.39, pp.2, 2015, https://doi.org/10.11627/jkise.2016.39.2.138
  2. A Genetic Algorithm for Solving a QFD(Quality Function Deployment) Optimization Problem vol.16, pp.4, 2015, https://doi.org/10.5392/ijoc.2020.16.4.026