a reasonable green logistics evaluation system for agricultural products plays a key role in the development of green agricultural products

1. Creative Design and Manufacture using Big Data - Research Article
Advances in Mechanical Engineering
2019, Vol. 11(1) 1–9
Ó The Author(s) 2019
DOI: 10.1177/1687814018816878
journals.sagepub.com/home/ade
An evaluation method for green
logistics system design of agricultural
products: A case study in Shandong
province, China
Songyuan Ni1
, Yu Lin2
, Yongxing Li2,3
, Hui Shao2
and
Shiguang Wang2
Abstract
Recently, the environmental issue caused by logistics of agricultural products has attracted a great deal of attention. In
order to solve the problem, much of work focuses on green logistics to decrease environmental pollution. However, the
green logistics evaluation system of agricultural products is insufficient. Therefore, establishing a reasonable green logis-
tics evaluation system for agricultural products plays a key role in the development of green agricultural products. In this
work, domestic and international environmental factors which affect the development of the green logistics of agricul-
tural products are analyzed based on reduction, reuse, and recycling principle of circular economy. In addition, a series
of evaluation indicators for green logistics of agricultural products are developed. A fuzzy analytic hierarchy process
method is proposed to make a comprehensive evaluation for green logistics of agricultural products based on evaluation
indicators. The method combined analytic hierarchy process and fuzzy theory, where a fuzzy transformation operator is
introduced. The proposed method is applied for decision-maker in view of knowledge management. In order to verify
the applicability of approach, the approach is applied to green logistics of Shandong agricultural products.
Keywords
Green logistics, agricultural products, fuzzy comprehensive evaluation, decision-making
Date received: 16 September 2018; accepted: 12 November 2018
Handling Editor: ZhiWu Li
Introduction
With the improvement of living standards in China,
there are great changes in many aspects, for instances,
material food and consumption structure. Consumer
demands for fresh produce, aquatic products, fruits,
and vegetables are increasing strongly, which increases
the vigor to develop logistics industry of agricultural
products. Nowadays, the growth momentum of con-
sumer demand is strong, and the development space
for logistics of agricultural products is great in China.
But there are still problems, such as late starting, rela-
tively backward technology, and imperfect industry
standard system, which lead to high cost and
environmental pollution. Aiming at environmental pol-
lution problem, much of work focuses on green logis-
tics to decrease the environmental pollution of
agricultural products. Many studies are performed on
1
College of Engineering and Technology, Northeast Forestry University,
Harbin, China
2
School of Transportation, Jilin University, Changchun, China
3
School of Civil and Environmental Engineering, Nanyang Technological
University, Singapore
Corresponding author:
Yu Lin, School of Transportation, Jilin University, Changchun 130022,
China.
Email: linyu773@163.com
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License
(http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without
further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/
open-access-at-sage).

2. green logistics of agricultural products (APGL).
Rostamzadeh and colleagues1–3
thought that green
logistics evaluation indexes should include green pur-
chasing, green transportation, and green storage.
Ubeda and colleagues4,5
pointed out that the green
logistics system is a friendly and efficient logistics sys-
tem coordinated with the environment. Sarkis and col-
leagues6,7
argued that green marketing is a business to
determine the target market demand, more cost effec-
tive than competitors to provide customers to meet the
needs of goods. Aldakhil et al.8
put forward that, in
order to achieve the green development of logistics,
great efforts need to be taken in four aspects. The four
aspects are the freight strength, different modes of car-
bon consumption, vehicle utilization, and energy con-
sumption of carbon. Bajdor and colleagues9–11
presented that green logistics is an extension of the con-
cept of sustainable development and summarized the
characteristics of green logistics. Georgiana and col-
leagues12,13
described the logistics activities and the
resulting environmental impact based on the green
logistics and green supply chain management theory.
Pishvaee and colleagues14,15
analyzed the uncertain fac-
tors of green logistics network and established the fuzzy
mathematical model. Bosona and colleagues16–18
put
forward to establish the manufacturer cluster belt,
which can reduce the waste of resources in transporta-
tion and improve the efficiency of green logistics opera-
tion. Sun and colleagues18,19
thought that enterprises,
consumers, and other participants should be put into
action to develop green logistics. Rezaei and col-
leagues20–22
established the evaluation index system of
logistics distribution performance. The fuzzy compre-
hensive evaluation method was used to evaluate the
performance of fresh agricultural products. Wu
et al.23,24
analyzed the difference of combing ecological
economy, circular economy, green economy, and the
development of low-carbon economy. Tian25
pointed
out that green logistics can greatly reduce the cost of
logistics operations and proposed a highly efficient
low-cost logistics model.
By analyzing the existing investigations, there are
fewer evaluation researches of APGL, especially for the
evaluation of logistics system in agricultural products
area.26–29
In this study, we analyzed the domestic and
international environmental factors that affect the
development of APGL according to the reduction,
reuse, and recycling (3R) principle of circular economy
and developed a series of evaluation indicators for
APGL. A fuzzy analytic hierarchy process (FAHP)
method is proposed to make a comprehensive evalua-
tion for APGL based on evaluation indicators. The
method combined analytic hierarchy process (AHP)
and fuzzy theory, in which a fuzzy transformation
operator is introduced. In order to verify the applicabil-
ity of the evaluation system, the approach is applied to
green logistics of Shandong agricultural products. The
result reflected the actual situation of green logistics of
Shandong agricultural products. In view of the result,
some relevant policy suggestions are presented accord-
ing to the obtained analysis results.
Evaluation index system
The evaluation index, which is used for assessing, eval-
uating, and comparing system quality, is a class of sta-
tistical index. While selecting the evaluation index of
APGL, the frequency analysis method and expert
consultation method are chosen to construct index
set.30–32
The index set, including 27 elements, can be
summarized as internal factor set and external factor
set. Figure 1 shows the relationship between the inter-
nal factor set and the external factor set. The detailed
descriptions of indexes are shown in Figures 2 and 3.
The above indexes highlight the characteristics of
APGL, but not all of them are concise and clear, and
some of them lack objectivity and operability.
Therefore, it is necessary to optimize index system to
Influence factors of APGL
Internal
factor
External
factor
Figure 1. Relationship between internal factor set and external
factor set.
Internal
factor
Preservation rate of agricultural products
Processing rate of agricultural products
Circulation rate of agricultural products
Storage level of agricultural products
Logistics transportation efficiency
Traceability of information
Energy consumption
Waste recovery rate
Vehicle exhaust emissions
Green logistics technology use degree
Logistics cost of agricultural products
Total value of agricultural products logistics
Figure 2. Internal factor set.
2 Advances in Mechanical Engineering

3. make the index more precise, scientific, reasonable, and
easy to operate. To do so, three steps are adopted to
determine final indexes: (1) analyzing indexes by cluster
analysis and determining the layer of indexes; (2) ana-
lyzing each layer indexes by principal component anal-
ysis method and removing the index with lower
contribution rate; and (3) regrouping the final index by
factor analysis method.
Finally, an index system is constructed, which
includes three layers, that is, target layer, criteria layer,
and index layer. The meaning of the target layer is the
evaluation result of evaluation object. In this study, the
goal is to make an evaluation of APGL about
Shandong province. The criterion layer is a set of judg-
ment criteria, which reflects the evaluation object
including political factors, economic factors, social fac-
tors, technical factors, internal management factors,
and environmental protection.30,33–35
The index layer is
a series of specific factors based on the criterion layer.
The index hierarchical structure is shown in Figure 4.
FAHP
The AHP, which is proposed by Saaty in the early
1970s, is a hierarchical weight decision analysis
method.36–38
Based on AHP, we can decompose the
elements associated with decisions into goals, criteria,
plans, and so on and develop qualitative and quantita-
tive analyses. AHP is applied to calculate index weights
for APGL. The application process of AHP is divided
into four steps, which are constructing index hierarchy,
establishing judgment matrix, single-level sorting, and
consistency checking. More detailed descriptions are
provided in next steps.
In order to make a better evaluation, the index hier-
archy is constructed. The highest target level is the per-
fection degree of the evaluation index system; the
middle is the standard layer, that is to say, five aspects
of APGL is evaluated; and the last layer is the index
layer, which is the specific evaluation index.
By establishing the hierarchical model, the elements
of each layer can be compared (pairwise), and then, a
comparison judgment matrix can be obtained.
Generally speaking, the form of judgment matrix is as
follows
BK C1 C2 Cn
C1 C11 C12 C1n
C2 C21 C22 C2n
.
.
. .
.
. .
.
. ..
. .
.
.
Cn Cn1 Cn2 Cnn
where BK is the upper target and Cij is the specific eva-
luation index: Cij . 0, Cij = 1/Cji (i 6¼ j), Cii = 1 (i,
j = 1,2, ., n).
The decision matrix is usually transformed to numer-
ical judgment matrix so that it can be calculated easily.
In general, the nine-point scale is adopted for compari-
son standard of proportion scale, which is shown in
Table 1.
In fact, the ranking problem of AHP is equivalent to
solve feature vector of the judgment matrix. The steps
are summarized as follows:
1. Calculating the product of each row element of
the judgment matrix: Mi
Mi =
Y
n
j = 1
aij, i = 1, 2, . . . , n
2. Calculating the N root mean square of Mi:
Wi
Wi =
ffiffiffiffiffiffi
Mi
n
p
3. Normalization of vector:
W =
W1,
W2, . . . ,
Wn
½
T
Wi =
Wi
P
n
j = 1
Wj
where
W =
W1,
W2, . . . ,
Wn
½
T
is the feature vector.
External
factor
Perfection of relevant laws and regulations
Perfection of industry standard system
Industry standard degree
Government support
Green channel for agricultural products
Overall planning and construction of agricultural
products logistics
Consumption of agricultural products
Demand for agricultural products
Consumer attitudes towards green agricultural
products
Quality of agricultural products logistics
practitioners
Relevant facilities improvement and utilization rate
Education and training of industry related
knowledge
The government's application level of logistics
information technology
Information sharing degree of logistics information
platform
Figure 3. External factor set.
Ni et al. 3

4. 4. Calculating the maximum eigenvalue of the
judgment matrix: lmax
lmax =
X
n
i = 1
(AW)i
nWi
Judgment matrices may not necessarily be consistent.
Thus, making a consistency checking for each judgment
matrix is needed.39
The consistency index and consis-
tency ratio are calculated by the following formulas,
respectively
Environmental
protection of APGL
Internal management
factors
Technical factors
Social factors
Economic factors
Political factors
Overall planning and construction of APGL
Government support
Industry standard degree
Perfection of relevant laws and regulations
Consumption of agricultural products
Green channel for agricultural products
Demand for agricultural products
Marketization of agricultural products
Consumer attitudes towards green
agricultural products
Quality of agricultural products logistics
Green logistics technology use degree
Preservation rate of agricultural products
Processing rate of agricultural products
Traceability of information
Total value of agricultural products logistics
Logistics cost of agricultural products
Agricultural logistics transportation
Circulation rate of agricultural products
Vehicle exhaust emissions
Waste recovery rate
Evaluation index
system of APGL
Target layer Criterion layer Index layer
Figure 4. Evaluation index system of APGL.
Table 1. Evaluation of classification table.
Ratio of factors A and B Comparison of quantized value
Factors A and B are equally important 1
Factor A is slightly more important than Factor B 3
Factor A is more important than Factor B 5
Factor A is very important compared to Factor B 7
Factor A is definitely more important than Factor B 9
AB adjacent judgment intermediate value 2, 4, 6, 8
Backward count of the upper figure is the reciprocal comparison of the two factors
4 Advances in Mechanical Engineering

5. CI =
lmax n
n 1
; CR =
CI
RI
0:10
where n is the number of dimensional matrix and RI is
the average random consistency index; for the matrix
n = 1–9, the reference values are shown in Table 2.
The consistency of the judgment matrix is depended
on the value of RI. Generally, when CR is less than 0.1,
the judgment matrix meets satisfactory consistency
standards, and the result of single-level sorting is accep-
table. Otherwise, the judgment matrix will be adjusted
to achieve satisfactory consistency.
Comprehensive evaluation method
The fuzzy comprehensive evaluation method was estab-
lished to handle fuzzy information that existed in the
evaluation process of APGL. The comprehensive eva-
luation method integrates the advantages of fuzzy eva-
luation and AHP.39,40
In this article, the comment set and factor set can be
written as
U = fU1, U2, . . . , Umg; V = fV1, V2, . . . , Vng
where U is the factor set that is used to describe the
object to be evaluated (i.e. evaluation index) and V is
the comment set that is used to describe the state of
each factor (i.e. evaluation grade); the comment set is
V = {good, better, general, poor, bad}, and the corre-
sponding scoring set is {1.0, 0.8, 0.6, 0.4, 0.2}
Rk
=
Rk
11 Rk
12 Rk
1n
Rk
21 Rk
22 Rk
2n
.
.
. .
.
. ..
. .
.
.
Rk
m1 Rk
m2 Rk
mn
2
6
6
6
4
3
7
7
7
5
where Rk
is the judgment matrix and Rk
ij represents the
degree of membership of the jth-level comment on the
ith evaluation index of the kth unit. In this article, the
frequency distribution of each index in each scoring
level is taken as the membership degree.
Using the synthesis of fuzzy matrix, we get the com-
prehensive evaluation model B, that is
B = A B = (B1, B1, . . . , B1)
Finally, the maximum membership method is
adopted to get the final evaluation level. Where, ‘‘*’’ is
the fuzzy transformation, and the operator ‘‘*’’ has
many types. The commonly used composition opera-
tors have the following four kinds
In this article, we select the fourth operator,
M ( , ), as the calculation operator of fuzzy evalua-
tion model.
Case study
In recent years, logistics enterprises have been commit-
ted to the low-carbon development and achieved certain
results. Currently, the APGL are getting more atten-
tion. In order to better analyze the status of APGL, the
evaluation index system is presented. Shandong prov-
ince, as a large agricultural province, has a large popu-
lation, where consumers’ demands for agricultural
products are higher. Thus, we performed our approach
for Shandong agricultural logistics.
In this case, the evaluation system is divided into
three layers: the target layer, T; the criterion layer, S;
and the index layer, A. In addition, set S1 as the politi-
cal factor, S1 = {A1, A2, A3, A4, A5} = {Perfection of
relevant laws and regulations, Industry standard
degree, Government support, Green channel for agri-
cultural products, Overall planning and construction of
agricultural products logistics}; S2 as an economic fac-
tor, S2 = {A6, A7, A8} = {Consumption of agricultural
products, Demand for agricultural products,
Marketization of agricultural products logistics}; S3 as
a social factor, S3 = {A9, A10} = {Consumer attitudes
toward green agricultural products, Quality of agricul-
tural products logistics practitioners}; S4 as the techni-
cal factor, S4 = {A11, A12, A13, A14} = {Processing rate
of agricultural products, Preservation rate of agricul-
tural products, Utilization degree of green logistics
technology, Traceability of information}; S5 as the
internal management factor, S5 = {A15, A16, A17,
A18} = {Transportation efficiency of agricultural logis-
tics, Logistics cost of agricultural products, Total value
of agricultural products logistics, Circulation rate of
Table 2. Average random consistency index.
n 1 2 3 4 5 6 7 8 9
RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45
Ni et al. 5

6. agricultural products}; and S6 as the environmental
protection of agricultural products logistics, S6 = {A19,
A20} = {Vehicle exhaust emissions, Waste recovery
rate}.
Consistency check
Judgment matrixes were obtained based on expert con-
sultation and field investigation. The final results are
shown in Tables 3–9.
The index weight calculated by the square root
method is shown in Tables 10–16. In addition, the con-
sistency test results are shown in Table 17.
Comprehensive evaluation
Some indices are difficult to get a specific value, that is,
A1 and A14. Thus, an expert investigation method is
adopted for this. In contrast, other indices are easily
described by a definite value, that is, A6 and A16.
Therefore, field investigation and questionnaire survey
are applied to get this. Each evaluation factor including
political factors (R1), economic factors (R2), social fac-
tor (R3), technical factor (R4), internal management
Table 3. First-grade judgment matrix.
T S1 S2 S3 S4 S5 S6
S1 1 1 3 1/3 1/5 1
S2 1 1 3 1/3 1/5 1
S3 1/3 1/3 1 1/3 1/5 1/4
S4 3 3 3 1 1/3 1
S5 5 5 5 3 1 5
S6 1 1 4 1 1/5 1
Table 4. Second-grade judgment matrix (political factors).
Political factors, S1 Specific indicators
A1 A2 A3 A4 A5
A1 1 3 1/5 1/3 1
A2 1/3 1 1/5 1/3 1
A3 5 5 1 1 3
A4 3 3 1 1 3
A5 1 1 1/3 1/3 1
Table 5. Second-grade judgment matrix (economic factors).
Economic factors, S2 Specific indicators
A6 A7 A8
A6 1 1 5
A7 1 1 5
A8 1/5 1/5 1
Table 6. Second-grade judgment matrix (social factors).
Social factors, S3 Specific indicators
A9 A10
A9 1 3
A10 1/3 1
Table 7. Second-grade judgment matrix (technical factors).
Technical factors, S4 Specific indicators
A11 A12 A13 A14
A11 1 1/5 1/3 1
A12 5 1 2 3
A13 3 1/2 1 2
A14 1 1/3 1/2 1
Table 8. Second-grade judgment matrix (internal management
factors).
Internal management factors, S5 Specific indicators
A15 A16 A17 A18
A15 1 1/3 3 2
A16 3 1 5 3
A17 1/3 1/5 1 1/2
A18 1/2 1/3 2 1
Table 9. Second-grade judgment matrix (environmental
protection of APGL).
Environmental protection of APGL, S6 Specific indicators
A19 A20
A19 1 1
A20 1 1
Table 10. First-grade index weight.
W S1 S2 S3 S4 S5 S6
Weight 0.09 0.10 0.05 0.19 0.45 0.12
Table 11. Second-grade index weight (political factors).
W1 A1 A2 A3 A4 A5
Weight 0.12 0.08 0.39 0.31 0.10
6 Advances in Mechanical Engineering

7. factor (R5), and environmental protection of agricul-
tural products logistics (R6) can be written as follows
R1 =
0:04 0:16 0:42 0:3 0:08
0:02 0:28 0:44 0:2 0:06
0:2 0:4 0:2 0:1 0:1
0:32 0:3 0:22 0:08 0:08
0:12 0:3 0:42 0:1 0:06
2
6
6
6
6
6
6
4
3
7
7
7
7
7
7
5
,
R2 =
0:16 0:3 0:3 0:1 0:1
0:2 0:3 0:22 0:1 0:18
0:3 0:3 0:26 0:08 0:06
2
6
4
3
7
5
R3 =
0:24 0:3 0:3 0:1 0:6
0:12 0:32 0:3 0:14 0:12
,
R4 =
0:18 0:32 0:3 0:12 0:08
0:12 0:3 0:3 0:18 0:1
0:1 0:32 0:3 0:2 0:08
0:08 0:3 0:4 0:18 0:04
2
6
6
6
4
3
7
7
7
5
R5 =
0:06 0:3 0:3 0:2 0:1
0:1 0:2 0:3 0:2 0:2
0:08 0:2 0:28 0:22 0:22
0:1 0:3 0:3 0:16 0:14
2
6
6
6
4
3
7
7
7
5
,
R6 =
0 0:12 0:3 0:4 0:18
0 0:1 0:3 0:32 0:28
Based on the weight set Wi and fuzzy evaluation
matrix Ri, we can easily get the fuzzy evaluation vector
by comprehensive evaluation formula Bi, which can be
written as
B1 = (0:12, 0:08, 0:39, 0:31, 0:10)
0:04 0:16 0:42 0:3 0:08
0:02 0:28 0:44 0:2 0:06
0:2 0:4 0:2 0:1 0:1
0:32 0:3 0:22 0:08 0:08
0:12 0:3 0:42 0:1 0:06
2
6
6
6
6
6
6
4
3
7
7
7
7
7
7
5
= 0:1956, 0:3206, 0:1258, 0:1258, 0:0842
ð Þ
The other evaluation vectors are shown in Table 18.
Let the fuzzy evaluation matrix R = (B1, B2, B3, B4,
B5, B6)T
, and we can get the final evaluation result B as
B = W R =
X
n
i = 1
(airij)
= (0:1118, 0:2495, 0:2820, 0:1919, 0:1644)
In this article, we know that the comment set is
V = {good, better, general, poor, bad}, and the corre-
sponding scoring set is {1.0, 0.8, 0.6, 0.4, 0.2}, so the
final result of fuzzy comprehensive evaluation is shown
as follows
M = B V = 0:5902
The values of others are shown in Table 19.
Based on the above analysis, we concluded that the
overall development level of APGL in Shandong is not
well. Among them, the political factor, the internal
management factor, and the environmental protection
Table 13. Second-grade index weight (social factors).
W3 A9 A10
Weight 0.75 0.25
Table 14. Second-grade index weight (technical factors).
W4 A11 A12 A13 A14
Weight 0.11 0.49 0.27 0.13
Table 15. Second-grade index weight (internal management
factors).
W5 A15 A16 A17 A18
Weight 0.24 0.52 0.09 0.15
Table 12. Second-grade index weight (economic factors).
W2 A6 A7 A8
Weight 0.45 0.45 0.10
Table 16. Second-grade index weight (environmental
protection of APGL).
W6 A19 A20
Weight 0.50 0.50
Table 17. Consistency check result.
N lmax CI RI CR Whether 0.10
T 6 6.264 0.053 1.24 0.043 Yes
S1 5 5.191 0.048 1.12 0.043 Yes
S2 3 3.000 0.000 0.58 0 Yes
S3 2 2.000 0.000 0 0 Yes
S4 4 4.034 0.011 0.90 0.013 Yes
S5 4 4.059 0.020 0.90 0.022 Yes
S6 2 2.000 0 0 0 Yes
Ni et al. 7

8. of APGL are still very deficient. The improvement in
economic, social, and technological progress is still a
general level. Therefore, some measures should be
taken to accelerate the development of APGL in
Shandong. For instance, (1) making the overall plan-
ning of green logistics system for agricultural products
and establishing a multi-lateral cooperation mechan-
ism; (2) strengthening the construction of logistics facil-
ities for agricultural products and accelerating the
promotion of green logistics technology; (3) improving
the efficiency and intensity of processing agricultural
products, and promoting the circular logistics of agri-
cultural products and packaging wastes; and (4) accel-
erating the cultivation of logistics professionals and
advocating green consumption.
Conclusion
In view of the current resources, environment, and
food safety issues, the agricultural products industry
badly needs to introduce green logistics. It is very
important to evaluate the performance of APGL.
This article constructs the evaluation index system of
APGL and proposes a fuzzy comprehensive evalua-
tion method based on AHP. AHP is applied to deter-
mine the weights of evaluation indexes, which helps
to avoid deviations caused by subjective factors. The
fuzzy evaluation method is adopted to evaluate
APGL based on the evaluation index. Besides, the
presented approach makes a detailed analysis of the
logistics of Shandong agricultural products. The
results show that the development level of APGL in
Shandong is not well, which is consistent with the
reality. To some extent, the results can serve as a ref-
erence for APGL to make the best talent decisions
and achieve long-term development strategies. In a
word, this study provides an effective evaluation
method for APGL.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
The author(s) disclosed receipt of the following financial sup-
port for the research, authorship, and/or publication of this
article: This work was supported by the Fundamental
Research Funds for the Central Universities under grant no.
2572014BB02 and the Heilong Jiang Postdoctoral Funds for
Scientific Research Initiation under grant no. LBH-Q16009.
ORCID iDs
Yu Lin https://orcid.org/0000-0002-4866-6325
Shiguang Wang https://orcid.org/0000-0003-2094-823X
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Table 18. Result of evaluation vector.
Evaluation vector Value
B2 (0.1440, 0.3000, 0.2600, 0.0980, 0.1320)
B3 (0.2100, 0.3050, 0.3000, 0.1100, 0.4800)
B4 (0.1160, 0.3065, 0.3130, 0.1788, 0.0846)
B5 (0.0886, 0.2390, 0.2982, 0.1958, 0.1688)
B6 (0.0000, 0.1100, 0.3000, 0.3600, 0.23)
Table 19. Result of fuzzy comprehensive evaluation.
M1 M2 M3 M4 M5 M6
Values 0.5947 0.6056 0.7740 0.6374 0.5708 0.4580
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