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2212-8271 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the scientific committee of 48th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS 2015
doi: 10.1016/j.procir.2016.01.195
Procedia CIRP 41 ( 2016 ) 258 – 263
ScienceDirect
48th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS 2015
Product Recovery Configuration Decisions for Achieving Sustainable
Manufacturing
Swee S. Kuik*, Toshiya Kaihara, Nobutada Fujii
Graduate School of System Informatics, Kobe University, 1-1 Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan
* Corresponding author. Tel.: +81-078-803-6250 ; fax: +81-078-803-6250. E-mail address: swee.kuik@outlook.com
Abstract
In response to the increased used product disposal and scarcity of natural resources, the end-of-life (EOL) management has now becoming an
important research field in manufacturing systems. The recovery operations for implementation are now more complicated than the traditional
manufacturing as uncertainty of numerous sources do always exist. The process of selecting an appropriate combination of used components for
a manufactured product is known as the product recovery configuration. For product recovery configuration selections, there are several possible
alternatives, such as those parts and/or components to be reused, rebuilt, recycled and disposed. Each of these disposition alternatives may need
to undergo various manufacturing processes in the industries. Due to the complexities of recovery operations, current recovery decision models
focus mainly on the assessment in terms of cost, time, waste and quality separately. This article presents an integrated model to determine an
optimal recovery plan for a manufacturer, which is to maximize its recovery value when producing a remanufactured product by considering
practical constraints of the manufacturing lead-time, waste and quality as a whole. In the numerical example, the optimization model was solved
using genetic algorithm. The obtained results showed that the selection of different product recovery configurations might have direct impact on
the achievable recovery value of a remanufactured product for the manufacturer. Finally, the future works and contributions of this study are also
briefly discussed.
© 2015 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the Scientific Committee of 48th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS
2015.
Keywords: sustainable manfuacturing; product recovery; end-of-life management; product configurations
1. Introduction
Sustainable manufacturing and product recovery operations
are now getting more attention than ever before [1, 2]. This
research trend refers to how manufacturers can create the
manufactured products, services and operational processes that
is to meet the economic and environmental perspectives [3, 4].
There are also numerous influential factors when implementing
sustainable manufacturing and product recovery operations [5-
7], such as shorten commercial product lifecycle, reverse
engineering for rapid returned product obsolescence, change in
environmental rules and regulations, increased cost in waste
management, increased cost of virgin material usage, etc. For
end-of-life (EOL) decisions, there are four possible alternatives
that may be considered by manufacturers for product recovery
operations upon return [8-10]. These include products to be
directly reused, rebuilt, recycle and disposed entirely. Ilgin and
Gupta [3] suggested more research effort for EOL decision
making and evaluation is still needed for improvement. In
recent years, the European environmental committees are also
more focused on the policy with extended producer
responsibility (EPR) [4, 11]. This policy is primarily concerned
with the environmental impacts for EOL treatment when
producing remanufactured products by manufacturers upon
receiving from customers and/or retailers. The EPR
enforcement also aims to encourage global manufacturers for
achieving an increased utilization value of product recovery
processes [2, 4, 10]. To meet this stringent requirement,
© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the scientifi c committee of 48th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS 2015
259
Swee S. Kuik et al. / Procedia CIRP 41 ( 2016 ) 258 – 263
manufacturers are now facing some significant challenges for
decision making and selection of those product recovery
configurations [5, 10, 12, 13]. Therefore, the primary focus of
this article is twofold: (i) to develop an integrated EOL decision
model for manufacturers, and (ii) to establish an optimal
recovery plan when implementing product recovery operations.
In addition, we also highlight the need for developing trade-off
decisions when producing remanufactured products.
This article is organized as follows. Section 2 presents the
literature review on EOL strategies and related issues with
product returns management processes. Section 3 formulates
the EOL decision model for manufacturers. Then, a numerical
application is demonstrated to show its practical flexibility and
usefulness of the proposed decision model for product recovery
configuration selections. Finally, contributions of the study and
future works are presented.
2. Literature Review
The existing literature on the decision disposition models for
product recovery configuration selections in the EOL stages is
growing significantly [7, 11, 14-16]. In this context, Iakovou et
al. [17] proposed the methodological framework for EOL
product returns and management, which is applicable for
electronic industries. This guiding framework is also known as
the multi-criteria matrix evaluation. This framework considers
the significant aspects of remanufactured product residual
value, environmental burden, weight, quantity and ease of
disassembly of each component. Kuik et al. [9] discussed the
importance of trade-off decision making in order to achieve
significant cost savings when implementing the product
recovery operations. However, there are numerous practical
limitations and technical constraints, such as manufacturing
lead-time and other recovery costs that are ignored in the
modelling. In recent years, Nagalingam et al. [10] proposed a
decision model for evaluating various product recovery
configuration options for manufactured products in
manufacturing industries. Ziout et al. [18] summarized the
primary aspects for developing an integrated performance
evaluation model to access and select various recovery
configurations for remanufactured products. However, the
trade-off assessment for recovery operations is still lacking and
the aspects are not clearly defined [4, 7, 18]. Therefore, there is
a need for considering trade-off decision in terms of cost, time,
waste and quality when evaluating and maximizing utilization
value for the product recovery configuration options [2, 18-20].
2.1. Product Returns for Recovery
Material handling with recycling option is considered as one
of the primary EOL strategies by manufacturers. In order to
achieve better business opportunities and high product
recovery savings, other potential recovery options should also
be considered, such as considering with reused and/or rebuild
components. These product recovery options are very crucial
in decision making for manufacturers to attain their global
market competiveness. The risks for implementing product
recovery operations are quite similar to the traditional
processes for a manufactured product. One of the primary
problems is about the uncertainty of returned product quality
and quantities. There are many practitioners and researchers,
who examined the aspects based on the entire product lifecycle
by minimizing total associated costs along a supply chain [5,
10, 12, 13]. However, these models are usually oversimplified
and ignored in terms of manufacturing lead-time, weight
proportions and quality in reliability.
In addition, practitioners and researchers [4, 9, 21, 22] also
summarized the key aspects from the available literature for
performance evaluation that should be based on the recovery
cost, waste, time and quality when developing an integrated
performance evaluation. Due to the complexity in managing
waste and recovery operations, the EOL dispositions for
manufacturers can vary significantly in practice, as it depends
largely on certain constraints and specifications [4, 21, 22].
2.2. Sustainable Initiative
Over past decades, the manufacturing system has seen
significant changes due to the sustainable initiative. Especially,
manufacturers are now stressed on improving product recovery
management due to the increased cost of disposal treatment [1,
22-26]. One way to achieve better profit margin is to identify
and determine the appropriate product recovery configurations
for remanufacturing strategies. In this context, Hu et al. [25]
and Guo et al. [27] studied on the aspects of disassembling
methods for EOL recovery plan for achieving high profit. Yang
et al. [28] also proposed a framework for evaluating product
family that may be useful and practical for consumer products.
In their model formulations, the practical constraints, which are
related to the environmentally conscious design and EOL
management are also examined. However, those models are
largely based on the industry oriented models for determining
EOL scenarios. There is limited focus on the aspects of cost,
time, waste and quality as a whole for deciding EOL scenarios.
3. Mathematical Model
This section presents the mathematical formulation of an
optimisation model for product redesign in the EOL decision
making if the original product design specifications and their
related recovery processes are known.
The choices of the decision disposition for discarded
products is classified into four disposition alternatives. These
include those parts and/or components for a manufactured
product to be disassembled for post-use stages. In this model, a
product recovery configuration selection consists of the some
new, reused, rebuilt and recycled components to be assembled
when producing a remanufactured product. The following is
the summary of indices and parameters and binary decision
variables used for formulating optimisation model in this study.
Decision variables:
n
Number of components
i
Index set of product component where
1,2,3,...in
r
Index of virgin component,
1r
; reused component
2r
; rebuilt component,
3r
and recycled
component,
4r
r,i
X
= 1 if component,
i
is virgin, reused,
260 Swee S. Kuik et al. / Procedia CIRP 41 ( 2016 ) 258 – 263
rebuilt, or recycled, otherwise it is 0
Indices and parameters:
REC
V
Achievable recovery value for a manufactured
product
op
Cost associated with
th
op
operational process for
a product
s
Cost associated with
th
s
collection related activity
for a product
REC
TC
Total cost for recovery for a product
VIR
TC
Total cost without recovery for a product
1,i
C
Raw material acquisition cost for component,
i
2,i
C
Manufacturing cost for component,
i
3,i
C
Assembly cost for component,
i
4,i
C
Direct reuse associated cost for component,
i
5,i
C
Disassembly cost for component,
i
6,i
C
Rebuilt cost for component,
i
7,i
C
Recycling cost for component,
i
8,i
C
Disposal cost for component,
i
collect
TC
Collection related costs with recovery for a product
1,collect
C
Financial incentives for a product incurred by
manufacturer
2,collect
C
Administrative cost for a product incurred by
manufacturer
3,collect
C
Sorting cost for a product incurred by manufacturer
4,collect
C
Transportation cost for a product incurred by
manufacturer
REC
MLT
Manufacturing lead-time with recovery for a
product
VIR
MLT
Manufacturing lead-time without recovery for a
product
g
Lead-time associated with
th
g
operational process
for a product
MLT
P
Lead-time ratio in recovery against manufacturer’s
target
1,i
T
Lead-time for manufacturing of component,
i
2,i
T
Lead-time for assembling component,
i
3,i
T
Lead-time for direct reusing component,
i
4,i
T
Lead-time for disassembling component,
i
5,i
T
Lead-time for rebuilding component,
i
6,i
T
Lead-time for recycling of component,
i
7,i
T
Lead-time for processing disposable component,
i
REC
W
Weight recovery proportion for a product
TOT
W
Weight proportion for a product
W
P
Weight recovery proportion ratio against
manufacturer’s target
r,i
Z
Weight for virgin/reused/rebuilt/recycled
component,
i
REC
QR
Quality in terms of reliability characteristic with
recovery for a product
VIR
QR
Quality in terms of reliability characteristic without
recovery for a product
QR
P
System reliability ratio against manufacturer’s target
r,i
b
Weibull parameter for component,
i
r
T
Characteristic life for component,
i
l
Allowable lifecycle before wear-out for reused or
rebuilt component,
i
r,i
G
Mean operating hours for component,
i
Maximize
VIRREC REC Collect
VTCTCTC
(1)
where
1, 8, ,
{1...3}
VIR i i op i
iI op
TC X C C
ª§ ·º
¨¸
«»
¬© ¹¼
¦¦
(2)
2, , 3, ,
{3...5} { 3...6}
4, 7, ,
{2...5}
i opi i opi
op op
REC
iI
i i op i
op
XCXC
TC
XC C
§·§·
ªº
¨¸¨¸
«»
©¹©¹
«»
§·
«»
¨¸
«»
¬© ¹ ¼
¦¦
¦¦
(3)
s,
{1...4}
Collect collect
s
TC C
¦
(4)
subject to
REC
MLT
VIR
MLT
MLT
P
d
(5)
REC
W
TOT
W
W
P
d
(6)
REC
QR
VIR
QR
QR
P
d
(7)
2, 3, 4, 1
iii
XXX
(8)
^`
1, 2, 3, 4,
,,, 0,1
iiii
XXXX
(9)
where
261
Swee S. Kuik et al. / Procedia CIRP 41 ( 2016 ) 258 – 263
2, , 3, ,
{2...4 } {2...5 }
4, 6, ,
{1...4 }
igiigi
gg
REC
iI
ii gi
g
XTXT
MLT
XT T
ªº
«»
«»
«»
¬¼
¦¦
¦¦
(10)
VIR 1 , 7, ,
{1,2}
ii gi
iI g
MLT X T T
ªº
«»
¬¼
¦¦
(11)
>@
REC 2,2, 3,3, 4,4,ii ii ii
iI
WXZXZXZ
¦
(12)
>@
TOT 1,2,3,4,iiii
iI
WZZZZ
¦
(13)
2, 3,
2, 3,
2, 3,
4,
4,
4,
2, 3,
REC
4,
bb
ii
ii
ii
bi
i
i
ii
iI
i
Xe Xe
QR
Xe
GG
TT
G
T
§· §·
¨¸ ¨¸
©¹ ©¹
§·
¨¸
©¹
ª§ · § ·º
¨¸¨¸
«»
¨¸¨¸
«»
©¹©¹
«»
§·
«»
¨¸
«»
¨¸
¬¼
©¹
(14)
1,
1,
1,
1,
bi
i
i
VIR i
iI
QR X e
G
T
§·
¨¸
©¹
ª§ ·º
¨¸
«»
¨¸
«»
¬© ¹¼
(15)
As shown in Eq. (1), the objective function is defined as the
difference of the recovery associated costs for a manufactured
product including the collection related costs and the virgin
associated costs for a manufactured product. In this model, Eq.
(2) is expressed as the total cost without recovery is expressed
as the summation of sequential operational processes for
making a manufactured product using virgin components only
Eq. (3) is also expressed as total recovery associated cost for a
manufactured product based on three cost related elements,
such as the reuse processing costs, rebuilt processing costs, and
recycle processing costs and collection related costs. Eq. (4) is
the collection activity related costs for a manufactured product.
In addition, the derived Eqs. (5) and (6) are established based
on manufacturing constraints, such as manufacturing lead-
time, weight recoverable proportions, and quality in terms of
reliability characteristic for a remanufactured product.
In this study, we used the genetic algorithm (GA), which
has been applied successfully in many industrial applications
in this context [13, 18, 29]. It is also known as the approach for
biological evolution to determine the survivor of fit test. This
algorithm is usually applied for resolving the non-linear, non-
differential and discontinuous situations if necessary [30-32].
The following example demonstrates the use of GA to solve the
optimisation model as discussed in this article.
4. Numerical Example
In this case application, a total of 20 separate components is
required to be assembled (i.e. for simplicity, we used the comp.
no. of Z1 to Z20) for producing a remanufactured product. Two
pre-determined types of the product recovery configurations
are selected based on the Nagalingam et al. [10] and these pre-
determined configurations also depends on the manufacturers’
capabilities and practical facilities’ constraints. There are
named as the Type-I customised and Type-II customised
configurations. These customised types of the product recovery
configurations are then used for comparisons. The potential
disposition of the Type-I product recovery configuration is
used about 6 separate reused components (i.e. Comp. no. Z1,
Z3, Z6, Z9, Z11, and Z13), 6 separate rebuilt components (i.e.
Comp. no. Z2, Z4, Z5, Z8, Z19, and Z20), and 8 separate
recycled components (i.e. Comp. no. Z7, Z10, Z12, Z14, Z15,
Z16, Z17, and Z18). While, the potential disposition of the
Type-II product configuration is used about 9 separate reused
components (i.e. Comp. no. of Z1, Z3, Z7, Z10, Z11, Z12, Z13,
Z14, and Z15), 5 separate rebuilt components (i.e. Comp. no.
of Z4, Z6, Z9, Z17, and Z19), and 6 recycled components
(Comp. no. of Z2, Z5, Z8, Z16, Z18, Z19, and Z20).
However, for both cases, three manufacturing constraints
are required to be satisfied as shown in Eq. (5)-(7) including
reduction of the manufacturing lead-time by approximately
30%, total recovery weight proportion approximately 65%
reduction, and quality in terms of reliability approximately
0.91.
Table 1. Data analysis of the Type-I configuration
Type- I
No 1
No 2
No 3
No 4
V
$18.91
$19.11
$19.20
$19.21
MLT
19.11%
17.83%
18.38%
19.21%
WM
43.70%
37.19%
34.95%
42.57%
QR
0.8987
0.8692
0.8343
0.8269
Qty. Rec.
8
7
7
8
Table 2. Data analysis of the Type-II configuration
Type- II
No 1
No 2
No 3
No 4
V
$17.96
$18.23
$18.33
$18.64
MLT
18.27%
18.24%
18.63%
18.23%
WM
49.47%
37.57%
33.39%
33.73%
QR
0.7697
0.7954
0.8022
0.8414
Qty. Rec.
9
6
8
6
Table 1 and 2 show the optimal recovery utilization values
for both Type-I and Type-II product recovery configurations
using GA with four best possible outcomes obtained. Table 1
shows the total number of the recovered components for
producing a remanufactured product is about 7-8 out of 20
separate components. Table 2 shows that the total number of
the recovered components for producing a remanufactured
product is about 6-9 out of 20 separate components.
Fig. 1 (Type-I product configuration) and Fig. 2 (Type-II
product configuration) illustrate the data obtained from
different types of recovery configurations by considering their
recovery values with the constraint functions of manufacturing
lead-time, weight in recovery proportion and reliability. There
are slight decrease in terms of reliability, weight recovery
proportion and lead-time when recovery value is increased (in
between $18.91 and $19.21).
Meanwhile, the results from Type-II product configuration
show that there are slight increase in terms of reliability and
weight reduction in recovery proportion when value of
recovery is increased (in between $17.96 and $18.64).
However, the selection of either the Type-I or the Type-II
262 Swee S. Kuik et al. / Procedia CIRP 41 ( 2016 ) 258 – 263
product recovery configuration depends on the individual
circumstance and manufacturers’ capabilities.
In this numerical case scenario, the Type-I product recovery
configuration was selected by the manufacturer. The primary
reason is that the recovery utilisation value is higher than the
Type-II product recovery configuration. Further, the Type-I
product recovery configuration is also satisfied with the
manufacturing constraints as defined by the manufacturer.
Fig. 1. Type-I product configuration analysis
In summary, the proposed optimisation model of this study
has demonstrated its practical usefulness and flexibility when
analysing and comparing various types of the product recovery
configurations. Due to practical and technical limitations, not
all separate components are able to be directly reused, rebuilt
and recycled for a remanufactured product.
In practice, some separate components need the substantial
efforts for the design for disassembly and/or assembly and
therefore, they are not worth to do it. In addition, those separate
components may also deteriorate faster than what the
manufacturers expect due to poor design. This optimisation
model may also be suitable for manufacturers to define their
pre-determined potential product recovery configuration for
analysing the decision trade-off scenario of manufacturing
lead-time, waste minimisation and quality in terms of reliability
characteristic.
In general, the selection of the optimal recovery plan for a
remanufactured product is regarded as a significant issue
within manufacturing system. The future work may also
consider the incorporation of energy consumption and carbon
emissions to the modelling.
Fig. 2. Type-II product configuration analysis
5. Concluding Remarks
In this article, the proposed model for this research study has
addressed the practical limitations for deciding and selecting
product recovery configurations in terms of recovery cost,
manufacturing lead-time, waste and quality. The contribution
of this study is that the developed optimisation model can assist
manufacturers to identify and select most appropriate recovery
configurations for implementation in manufacturing system.
Acknowledgements
The authors would like to acknowledge the research funding
support from the Japan Society for the Promotion of Science
(JSPS) in association with the Australian Academy of Science
and Australian Research Council for the Research Fellowship
Program.
Appendix A. Data for numerical application
The parameters used in this study are listed in Table A1-A4.
In this scenario, a manufactured product consists of 20 separate
components. For EOL decision, each of these components can
be reused, rebuilt and recycled.
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Swee S. Kuik et al. / Procedia CIRP 41 ( 2016 ) 258 – 263
Table A1. Cost parameters used for modeling for 20 separate components
Table A2. Time parameters used for modeling for 20 separate components
Table A3a and b. Weight Proportion parameters and quality parameters for 20
separate components
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Comp.T1T2T3T4T5T6T7
11.295 1.385 0.532 0.776 1.737 0.9183 2.274
21.079 1.405 0.342 0.975 1.707 0.0866 3.240
31.825 1.838 0.602 0.436 2.253 0.4147 2.773
41.786 1.310 1.172 0.558 2.159 0.1144 2.988
50.654 1.695 0.599 0.924 1.612 0.4572 2.888
60.810 1.778 0.226 1.175 1.736 0.1538 2.711
71.769 1.897 0.435 1.087 2.139 0.5321 2.631
81.793 1.949 0.294 1.098 2.316 0.3529 2.580
90.720 1.574 0.366 0.705 2.290 0.3109 2.930
10 0.989 1.261 0.583 0.985 2.236 0.6039 2.811
11 1.314 2.055 0. 938 0.737 1.930 0. 3536 2. 867
12 0.729 2.325 0. 727 0.390 2.059 0. 2150 3. 459
13 1.687 1.874 0. 255 1.109 1.826 0. 4731 2. 205
14 1.215 1.874 0. 134 1.090 2.263 0. 2550 3. 094
15 1.690 1.736 0. 835 0.550 1.824 0. 2777 2. 865
16 1.568 1.931 0. 419 1.011 2.340 0. 0465 3. 113
17 1.562 1.799 0. 387 0.842 2.163 0. 5857 2. 590
18 1.691 1.885 0. 319 1.164 2.280 0. 5331 2. 165
19 0.645 1.501 0. 463 1.142 2.070 0. 6611 3. 452
20 0.766 2.009 0. 430 0.547 1.957 0. 7872 2. 700
Lead Time Parameters
Comp.C1C2C3C4C5C6C7C8
1
2.59 1.37 1.65 1.65 1.62 3. 21 1.23 2.60
2
2.47 1.23 0.66 0.55 0.63 3. 35 1.35 2.55
3
3.54 2.35 0.52 0.65 0.52 2. 66 0.48 0.24
4
0.58 2.31 0.84 0.65 0.45 2. 36 0.24 2.01
5
0.62 2.47 0.35 0.26 0.23 4. 25 0.17 2.12
6
1.71 0.32 0.41 0.26 0.25 3. 12 0.23 1.91
7
0.59 0.93 0.36 0.26 0.35 2. 02 0.35 2.11
8
0.45 0.82 0.25 0.62 0.42 1. 32 0.33 2.32
9
0.66 1.01 0.52 0.63 0.43 2. 21 0.53 2.52
10
1.45 2.35 0.92 0.68 0.11 3. 65 1.01 2.62
11
1.25 2.99 0.85 0.66 0.74 4. 96 1.22 2.23
12
2.82 2.33 0.75 0.63 0.89 3. 22 1.39 2.54
13
0.63 2.87 0.49 0.54 0.35 3. 95 1.61 1.95
14 1.05
0.93 0.36 0.62 0.25 4. 65 0.38 3.85
15 0.75
0.82 0.25 0.63 0.35 3. 06 0.29 2.73
16 1.08
1.01 0.52 0.68 0.24 3. 04 0.57 2.56
17 1.29
2.35 0.92 0.66 0.43 3. 94 1.11 1.67
18 1.10
2.64 0.85 0.63 0.12 2. 84 1.19 1.22
19 1.22 2.14
0.66 1.01 0.78 2.87 1.24 2. 54
20 2.61 0.87
0.63 2.35 0.82 2. 47 1.02 2.25
Cost Para meters, $
Comp. Weigh t/kg
10.3518
21.2123
31.1070
40.0214
50.5433
61.1726
71.4408
81.0972
91.3342
10 1.5948
11 1.1256
12 0.7575
13 1.1879
14 1.1101
15 1.9045
16 0.2517
17 0.5914
18 0.9937
19 1.9983
20 0.3029
Virgin/Rcycle Reman/Reuse
Comp. Relia bility Reliability
10.9991 0.9499
20.9972 0.9560
30.9976 0.9576
40.9974 0.9569
50.9977 0.9574
60.9979 0.9583
70.9961 0.9506
80.9975 0.9549
90.9983 0.9623
10 0.9973 0.9567
11 0.9974 0.9548
12 0.9979 0.9573
13 0.9981 0.9619
14 0.9981 0.9597
15 0.9971 0.9532
16 0.9972 0.9618
17 0.9974 0.9594
18 0.9975 0.9569
19 0.9976 0.9549
20 0.9973 0.9583
