Journal of Intelligent Manufacturing

, Volume 30, Issue 1, pp 19–32 | Cite as

A hybrid approach of rough set and case-based reasoning to remanufacturing process planning

  • Zhigang JiangEmail author
  • Ya Jiang
  • Yan Wang
  • Hua Zhang
  • Huajun Cao
  • Guangdong Tian


Remanufacturing, a process returning used products to at least as good as new condition, is increasingly recognized as an important part of the circular economy. Since returned used components for remanufacturing have varying conditions and different defects, remanufacturing is very time-consuming and labor-intensive. There is an urgent need to reuse knowledge generated from existing parts remanufacturing to rapidly create sound process planning for the new arrival of used parts. A hybrid method combing rough set (RS) and cased-based reasoning (CBR) for remanufacturing process planning is presented in this paper. RS is employed for features reduction and rapid determination of features’ weights automatically, and CBR is utilized to calculate the similarity of process cases to identify the most suitable solution effectively from case database. The application of the methodology is demonstrated in an example of remanufacturing process for a saddle guide. The results indicated that the quality of remanufactured products has been improved significantly. The method has been implemented in a prototype system using Visual Studio 2010 and Microsoft SQL Server2008. The results suggested that the hybrid RS–CBR system is feasible and effective for the rapid generation of sound process planning for remanufacturing.


Remanufacturing Process planning Rough set Case-based reasoning 

List of Symbols


An equivalence relation on \(\Omega \)


A set of condition features


The \(k{\mathrm{th}}\) condition feature

\(C_i^X \)

The \(i{\mathrm{th}}\) features of case X

\(C_i^Y \)

The \(i{\mathrm{th}}\) features of case Y


A decision features set


Information function


Case number


Value of \(j{\mathrm{th}}\) feature of reconditioning process


The number of features


Maximum assignment value of the feature


Name of \(j{\mathrm{th}}\) feature of reconditioning process


Name of \(i{\mathrm{th}}\) essential feature


The\( j{\mathrm{th}}\) process case


Each feature


Vector set of features of reconditioning process


The \(j{\mathrm{th}}\) feature of reconditioning process


An arbitrary feature


Vector set of essential features of used cores


The \(i{\mathrm{th}}\) essential feature


Corresponding solution


A nonempty and finite set of objects


Value of \(i{\mathrm{th}}\) essential feature


Value set of q

\(w(C_k )\)

The feature weights of condition feature \(C_k \)


Weight of \(i{\mathrm{th}}\) essential feature


Weight of \(j{\mathrm{th}}\) feature of reconditioning process

\(W_D (C_k)\)

The significance of condition feature \(C_k \)


New case


Source case

\(\psi \)

Proportional factor

\(\Omega \)

A finite set of features

\(\varepsilon \left( {P_j } \right) \)

The confidence of the \(j{\mathrm{th}}\) process case



The work described in this paper was supported by the National Natural Science Foundation of China (51205295, 51405075), Wuhan Youth Chenguang Program of Science and Technology (2014070404010214). These financial contributions are gratefully acknowledged.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Zhigang Jiang
    • 1
    Email author
  • Ya Jiang
    • 1
  • Yan Wang
    • 2
  • Hua Zhang
    • 1
  • Huajun Cao
    • 3
  • Guangdong Tian
    • 4
  1. 1.School of Machinery and AutomationWuhan University of Science and TechnologyWuhanChina
  2. 2.Department of Computing, Engineering and MathematicsUniversity of BrightonBrightonUK
  3. 3.State Key Laboratory of Mechanical TransmissionChongqing UniversityChongqingChina
  4. 4.Transportation CollegeJilin UniversityChangchunChina

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