재 제조 프로세스를 가진 순환 형 SCM에서의 비선형 퍼지 함수 기반 가격 정책 프레임웍

서지정보

ㆍ발행기관 : 한국산업경영시스템학회 ㆍ수록지정보 : 산업경영시스템학회지 / 40권 / 4호
ㆍ저자명 : 김진배, 김태성, 이현수

목차

1. 서 론
2. 이론적 배경
2.1 재 제조 프로세스를 가지는 순환 형 공급사슬
2.2 불확실성이 내제된 공급사슬 모형
2.3 퍼지 이론을 응용한 공급사슬의 관리
2.4 퍼지 비선형 계획법
3. 순환 형 공급사슬 프레임웍의 설계
3.1 순환 형 공급사슬의 시나리오
3.2 제품 구성과 고장 유형의 분류
3.3 수리기호의 표기 및 수리모형
4. 비선형 퍼지 계획법 기반의 가격 정책결정및 프레임웍
4.1 비선형 퍼지 계획법
4.2 비선형 퍼지 계획법을 사용한 프레임웍의 구현
5. 수치실험 및 분석
6. 결론 및 향후 연구과제
Acknowledgement
References

영어 초록

This papers focuses on remanufacturing processes in a closed loop supply chain. The remanufacturing processes is considered as one of the effective strategies for enterprises’ sustainability. For this reason, a lot of companies have attempted to apply remanufacturing related methods to their manufacturing processes. While many research studies focused on the return rate for remanufacturing parts as a control parameter, the relationship with demand certainties has been studied less comparatively. This paper considers a closed loop supply chain environment with remanufacturing processes, where highly fluctuating demands are embedded. While other research studies capture uncertainties using probability theories, highly fluctuating demands are modeled using a fuzzy logic based ambiguity based modeling framework. The previous studies on the remanufacturing have been limited in solving the actual supply chain management situation and issues by analyzing the various situations and variables constituting the supply chain model in a linear relationship. In order to overcome these limitations, this papers considers that the relationship between price and demand is nonlinear. In order to interpret the relationship between demand and price, a new price elasticity of demand is modeled using a fuzzy based nonlinear function and analyzed. This papers contributes to setup and to provide an effective price strategy reflecting highly demand uncertainties in the closed loop supply chain management with remanufacturing processes. Also, this papers present various procedures and analytical methods for constructing accurate parameter and membership functions that deal with extended uncertainty through fuzzy logic system based modeling rather than existing probability distribution based uncertainty modeling.

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