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Research Papers

Variability and Component Criticality in Component Reuse and Remanufacturing Systems

[+] Author and Article Information
Vijitashwa Pandey

Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, 104 S. Mathews Avenue, Urbana, IL 61801vpandey2@illinois.edu

Deborah Thurston

Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, 104 S. Mathews Avenue, Urbana, IL 61801thurston@illinois.edu

J. Comput. Inf. Sci. Eng 10(4), 041004 (Dec 17, 2010) (8 pages) doi:10.1115/1.3514077 History: Received March 04, 2008; Revised May 06, 2010; Published December 17, 2010; Online December 17, 2010

Different operations, such as take-back, cleaning, and repair, lead to high system variability rendering remanufacturing systems difficult to manage. Even when a product is successfully remanufactured, there remains the problem of customer perception of remanufactured products being not able to perform as well as new ones. The possibility of several different options (reusing, remanufacturing, and recycling) further compound the complexity of the information set that should be considered for effective remanufacturing. This paper develops a method that can be employed for making component level decisions that accounts for aforesaid issues. A metric is proposed that measures the randomness or variability imposed by a reuse alternative. A measure of effective age is also proposed, extending the lines of previous research. A washing machine example illustrates the method and how the two measures can be incorporated into a design decision model.

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

Grahic Jump Location
Figure 1

A schematic of different mix of operations for the components of a product

Grahic Jump Location
Figure 2

A social network representation of a washing machine showing different flows

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Figure 3

Example of an unbalanced product, the product is unacceptable as component 1 influences the perceived age more

Grahic Jump Location
Figure 4

Sensitivity of the optimized age and variability values for different values of kage and kvar

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