Remanufacturing has recently received significant interest due to its environmental and economic benefits. Traditionally, the reassembly processes in remanufacturing systems are managed using a product-oriented model. When a product is returned and disassembled, the used components may be processed incorrectly, and the quality of the remanufactured products may not meet customer needs. To solve these problems, a component-oriented reassembly model is proposed. In this model, returned components are inspected and assigned scores according to their quality/function and categorized in a reassembly inventory. Based on the reassembly inventory, components are paired under the control of a reassembly strategy, and these pairs are then assembled into reassembly chains. Each chain represents a product. To evaluate the performance of different reassembly strategies under uncertain conditions, we describe the reassembly problem using an agent-environment system. The platform is modeled as a Markov decision process (MDP), and a reassembly score iteration algorithm (RSIA) is developed to identify the optimal reassembly strategy. The effectiveness of the method is demonstrated via a case study using the reassembly process of diesel engines. The results of the case study show that the component-oriented reassembly model can improve the performance of the reassembly system by 40%. A sensitivity analysis is carried out to evaluate the relationship between the parameters and the performance of the reassembly system. The component-oriented model can reassemble products to meet a larger variety of customer needs, while simultaneously producing better remanufactured products.

References

References
1.
Ortegon
,
K.
,
Nies
,
L.
, and
Sutherland
,
J. W.
,
2014
, “
Remanufacturing
,”
CIRP Encyclopedia of Production Engineering
, The International Academy for Production Engineering, L. Laperrière, and G. Reinhart, eds., Springer, Berlin.
2.
Goodall
,
P.
,
Rosamond
,
E.
, and
Harding
,
J.
,
2014
, “
A Review of the State of the Art in Tools and Techniques Used to Evaluate Remanufacturing Feasibility
,”
J. Cleaner Prod.
,
81
, pp.
1
15
.
3.
Liu
,
M.
,
Ke
,
Q.
,
Song
,
S.
, and
Zhou
,
X.
,
2013
, “
Active Remanufacturing Timing Determination Based on Failure State Assessment
,”
Re-Engineering Manufacturing for Sustainability
,
Springer
,
Singapore
, pp.
615
619
.
4.
Song
,
S.
,
Liu
,
M.
,
Ke
,
Q.
, and
Huang
,
H.
,
2015
, “
Proactive Remanufacturing Timing Determination Method Based on Residual Strength
,”
Int. J. Prod. Res.
,
53
(
17
), pp.
5193
5206
.
5.
Su
,
C.
, and
Xu
,
A.
,
2014
, “
Buffer Allocation for Hybrid Manufacturing/Remanufacturing System Considering Quality Grading
,”
Int. J. Prod. Res.
,
52
(
5
), pp.
1269
1284
.
6.
Sakai
,
T.
, and
Takata
,
S.
,
2012
, “
Reconfiguration Management of Remanufactured Products for Responding to Varied User Needs
,”
CIRP Ann.
,
61
(
1
), pp.
21
26
.
7.
Galbreth
,
M. R.
, and
Blackburn
,
J. D.
,
2006
, “
Optimal Acquisition and Sorting Policies for Remanufacturing
,”
Prod. Oper. Mange.
,
15
(
3
), pp.
384
392
.
8.
Tolio
,
T.
,
Bernard
,
A.
,
Colledani
,
M.
,
Kara
,
S.
,
Seliger
,
G.
,
Duflou
,
J.
,
Battaia
,
O.
, and
Takata
,
S.
,
2017
, “
Design, Management and Control of Demanufacturing and Remanufacturing Systems
,”
CIRP Ann. Manuf. Technol.
,
66
(
2
), pp.
585
609
.
9.
Zikopoulos
,
C.
, and
Tagaras
,
G.
,
2008
, “
On the Attractiveness of Sorting Before Disassembly in Remanufacturing
,”
IIE Trans.
,
40
(
3
), pp.
313
323
.
10.
Jin
,
X.
,
Ni
,
J.
, and
Koren
,
Y.
,
2011
, “
Optimal Control of Reassembly With Variable Quality Returns in a Product Remanufacturing System
,”
CIRP Ann. Manuf. Technol.
,
60
(
1
), pp.
25
28
.
11.
Ferguson
,
M.
,
Guide
,
V. D. J.
,
Koca
,
E.
, and
Souza
,
G. C.
,
2009
, “
The Value of Quality Grading in Remanufacturing
,”
Prod. Oper. Manage.
,
18
(
3
), pp.
300
314
.
12.
Ricoh,
2018
, “
Recycling of Highly Functional Components
,” Ricoh, Tokyo, Japan, accessed Dec. 12, 2018, https://www.ricoh.com/environment/product/resource/01_01.html
13.
Denizel
,
M.
,
Ferguson
,
M.
,
Souza
,
G.
, and
Gil
,
C.
,
2010
, “
Multiperiod Remanufacturing Planning With Uncertain Quality of Inputs
,”
IEEE Trans. Eng. Manage.
,
57
(
3
), pp.
394
404
.
14.
Behdad
,
S.
,
Williams
,
A. S.
, and
Thurston
,
D.
,
2012
, “
End-of-Life Decision Making With Uncertain Product Return Quantity
,”
ASME J. Mech. Des.
,
134
(
10
), p.
100902
.
15.
Aras
,
N.
,
Boyaci
,
T.
, and
Verter
,
V.
,
2004
, “
The Effect of Categorizing Returned Products in Remanufacturing
,”
IIE Trans.
,
36
(
4
), pp.
319
331
.
16.
Mashhadi
,
A. R.
,
Esmaeilian
,
B.
, and
Behdad
,
S.
,
2015
, “
Uncertainty Management in Remanufacturing Decisions: A Consideration of Uncertainties in Market Demand, Quantity, and Quality of Returns
,”
ASME J. Risk Uncert. Part B
,
1
(
2
), p.
021007
.
17.
Liu
,
M.
,
Liu
,
C.
, and
Zhu
,
Q.
,
2014
, “
Optional Classification for Reassembly Methods With Different Precision Remanufactured Parts
,”
Assem. Autom.
,
34
(
4
), pp.
315
322
.
18.
Shen
,
W.
,
Pang
,
K.
,
Liu
,
C.
,
Ge
,
M.
,
Zhang
,
Y.
, and
Wang
,
X.
,
2015
, “
The Quality Control Method for Remanufacturing Assembly Based on the Jacobian-Torsor Model
,”
Int. J. Adv. Manuf. Technol.
,
81
(
1–4
), pp.
253
261
.
19.
Mashhadi
,
A. R.
,
Behdad
,
S.
, and
Zhuang
,
J.
,
2016
, “
Agent Based Simulation Optimization of Waste Electrical and Electronics Equipment Recovery
,”
ASME J. Manuf. Sci. Eng.
,
138
(
10
), p.
101007
.
20.
Zhang
,
X.
,
Zhang
,
H.
,
Jiang
,
Z.
, and
Wang
,
Y.
,
2016
, “
A Decision-Making Approach for End-of-Life Strategies Selection of Used Parts
,”
Int. J. Adv. Manuf. Technol.
,
87
(
5–8
), pp.
1457
1464
.
21.
Jin
,
X.
,
Hu
,
S. J.
,
Ni
,
J.
, and
Xiao
,
G.
,
2013
, “
Assembly Strategies for Remanufacturing Systems With Variable Quality Returns
,”
IEEE Trans. Autom. Sci. Eng.
,
10
(
1
), pp.
76
85
.
22.
Mont
,
O.
,
Dalhammar
,
C.
, and
Jacobsson
,
N.
,
2006
, “
A New Business Model for Baby Prams Based on Leasing and Product Remanufacturing
,”
J. Cleaner Prod.
,
14
(
17
), pp.
1509
1518
.
23.
Si
,
X. S.
,
Wang
,
W.
,
Hu
,
C. H.
,
Chen
,
M. Y.
, and
Zhou
,
D. H.
,
2013
, “
A Wiener-Process-Based Degradation Model With a Recursive Filter Algorithm for Remaining Useful Life Estimation
,”
Mech. Syst. Signal Process.
,
35
(
1–2
), pp.
219
237
.
24.
Subramoniam
,
R.
,
Huisingh
,
D.
, and
Chinnam
,
R. B.
,
2010
, “
Aftermarket Remanufacturing Strategic Planning Decision-Making Framework: Theory & Practice
,”
J. Cleaner Prod.
,
18
(
16–17
), pp.
1575
1586
.
25.
Liu
,
M.
,
Liu
,
C.
,
Xing
,
L.
,
Mei
,
F.
, and
Zhang
,
X.
,
2016
, “
Study on a Tolerance Grading Allocation Method Under Uncertainty and Quality Oriented for Remanufactured Parts
,”
Int. J. Adv. Manuf. Technol.
,
87
(
5–8
), pp.
1265
1272
.
26.
Mazhar
,
M. I.
,
Kara
,
S.
, and
Kaebernick
,
H.
,
2007
, “
Remaining Life Estimation of Used Components in Consumer Products: Life Cycle Data Analysis by Weibull and Artificial Neural Networks
,”
J. Oper. Manage.
,
25
(
6
), pp.
1184
1193
.
27.
Okoh
,
C.
,
Roy
,
R.
,
Mehnen
,
J.
, and
Redding
,
L.
,
2014
, “
Overview of Remaining Useful Life Prediction Techniques in Through-Life Engineering Services
,”
Procedia CIRP
,
16
, pp.
158
163
.
28.
Hua
,
Y.
,
Liu
,
S.
, and
Zhang
,
H.
,
2015
, “
Remanufacturing Decision Based on RUL Assessment
,”
Procedia CIRP
,
29
, pp.
764
768
.
29.
Sutton
,
R. S.
, and
Barto
,
A. G.
,
1998
,
Reinforcement Learning: An Introduction
,
MIT Press
,
Cambridge, UK
.
30.
Sundin
,
E.
, and
Bras
,
B.
,
2005
, “
Making Functional Sales Environmentally and Economically Beneficial Through Product Remanufacturing
,”
J. Cleaner Prod.
,
13
(
9
), pp.
913
925
.
31.
Poole
,
D. L.
, and
Mackworth
,
A. K.
,
2017
,
Artificial Intelligence: Foundations of Computational Agents
,
Cambridge University Press
,
New York
.
32.
Li
,
T.
,
Liu
,
Z. C.
,
Zhang
,
H. C.
, and
Jiang
,
Q. H.
,
2013
, “
Environmental Emissions and Energy Consumptions Assessment of a Diesel Engine From the Life Cycle Perspective
,”
J. Cleaner Prod.
,
53
, pp.
7
12
.
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