NDLI: Development of an optimal method for remanufacturing process plan selection

Content Provider	Springer Nature Link
Author	Jiang, Zhigang Fan, Zhou Sutherland, John W. Zhang, Hua Zhang, Xugang
Copyright Year	2014
Abstract	Remanufacturing represents an activity that often offers significant profit opportunities and can provide substantial environmental benefits. Process plan selection is one of the most important operational decisions in remanufacturing because it directly affects the success rate of remanufacturing as well as cost and quality. There is a need for a decision-making aid which can optimize process plan selection in the presence of wide-ranging alternatives. In response to this need, a decision method has been developed that combines the strengths of quality function deployment (QFD) and fuzzy linear regression. In this method, quality function deployment is employed to not only frame the problem but also to establish relationships between remanufacturing performance and process quality characteristics of interest. Fuzzy linear regression is used to determine the functional relationships between remanufacturing performance and process quality characteristics and obtain the optimal solution. If desired, the most suitable process plan alternative may be determined. To assess the usefulness and practicality of the proposed method, an illustrative example is given and the results are discussed.
Starting Page	1551
Ending Page	1558
Page Count	8
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	72
Issue Number	9-12
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2014-03-26
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Remanufacturing Process plan Process quality characteristics Decision-making Industrial and Production Engineering Production/Logistics/Supply Chain Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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