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This paper identifies the major research concepts, techniques, and models covered in the cross-docking literature. A systematic literature review is conducted using the BibExcel bibliometric analysis and Gephi network analysis tools. A research focus parallelship network (RFPN) analysis and keyword co-occurrence network (KCON) analysis are used to identify the primary research themes. The RFPN results suggest that vehicle routing, inventory control, scheduling, warehousing, and distribution are most studied. Of the optimization and simulation techniques applied in cross-docking, linear and integer programming has received much attention. The paper informs researchers interested in investigating cross-docking through an integrated perspective of the research gaps in this domain. This paper systematically reviews the literature on cross-docking, identifies the major research areas, and provides a survey of the techniques and models adopted by researchers in the areas related to cross-docking.
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sustainability
Review
Cross-Docking: A Systematic Literature Review
Reza Kiani Mavi 1, * , Mark Goh 1,2 , Neda Kiani Mavi 1, Ferry Jie 1, Kerry Brown 1,
Sharon Biermann 3and Ahmad A. Khanfar 1
1School of Business and Law, Edith Cowan University, Joondalup, WA 6027, Australia;
n.kianimavi@ecu.edu.au (N.K.M.); f.jie@ecu.edu.au (F.J.); k.brown@ecu.edu.au (K.B.);
a.khanfar@ecu.edu.au (A.A.K.)
2NUS Business School and The Logistics Institute-Asia Pacific, National University of Singapore,
Singapore 119245, Singapore; bizgohkh@nus.edu.sg
3Planning and Transport Research Centre (PATREC), The University of Western Australia, Perth, WA 6009,
Australia; sharon.biermann@uwa.edu.au
*Correspondence: r.kianimavi@ecu.edu.au
Received: 7 May 2020; Accepted: 9 June 2020; Published: 11 June 2020


Abstract:
This paper identifies the major research concepts, techniques, and models covered in the
cross-docking literature. A systematic literature review is conducted using the BibExcel bibliometric
analysis and Gephi network analysis tools. A research focus parallelship network (RFPN) analysis
and keyword co-occurrence network (KCON) analysis are used to identify the primary research
themes. The RFPN results suggest that vehicle routing, inventory control, scheduling, warehousing,
and distribution are most studied. Of the optimization and simulation techniques applied in
cross-docking, linear and integer programming has received much attention. The paper informs
researchers interested in investigating cross-docking through an integrated perspective of the research
gaps in this domain. This paper systematically reviews the literature on cross-docking, identifies the
major research areas, and provides a survey of the techniques and models adopted by researchers in
the areas related to cross-docking.
Keywords: cross-docking; systematic literature review; co-occurrence analysis; logistics; Gephi
1. Introduction
To date, there has been a flurry of research eort on understanding logistics and supply chain
management and their various sub-domains, in particular, research techniques, modelling and
frameworks, and the theoretical lenses used for extracting insights from the physical phenomena.
However, not all sub-domains have been well studied and assessed in relation to their research
value and opportunities, notably those aspects which are critical to the core of logistics operations.
Cross-docking, a technique popularized in the 1990s, is one of them.
1.1. Cross-Docking
The activity of cross-docking is the logistics process of transhipping inventory in a flow-centre by
unloading the shipments from the inbound trucks directly to the outbound trucks to reduce shipment
time and cost [
1
,
2
] by eliminating storage and order picking activities, thus accelerating the flow of
the shipping cycle [
1
]. The Material Handling Industry of America defines cross-docking as “the
process of moving merchandise from the receiving dock to shipping [dock] for shipping without
placing it first into storage locations” [
2
]. Industries implement a cross-docking strategy to improve
the “just-in-time” deliveries within their supply chain in order to minimize the number of inbound
and outbound trucks and enhance sustainability [
3
]. A cross-docking station is a site where inventory
is unloaded, consolidated, and then directly reloaded onto outgoing trucks [
4
]. Under a cross-docking
Sustainability 2020,12, 4789; doi:10.3390/su12114789 www.mdpi.com/journal/sustainability
Sustainability 2020,12, 4789 2 of 19
strategy, the inventory is stored only for a short time before being reloaded onto outgoing trucks; the
inventory does not stay beyond 24 hours within the cross-dock station [1,5].
In addition to cross-docking, there are other distribution strategies that are commonly used
to distribute products from suppliers to customers, such as direct shipment [
6
,
7
], milk-runs [
6
,
8
],
and warehousing [
6
]. A direct shipment strategy is based on sending shipments directly from the
source to the destination [
6
] where trucks can perform single or multiple pick-ups and deliveries [
7
].
In a milk-run strategy, shipments are distributed into routes where trucks visit multiple origins
and destinations sequentially [
2
,
8
]. Both direct shipment and milk-run strategies do not involve
intermediary logistics facilities; hence, there are lower implementation costs. However, these two
strategies are not ecient in the case of small shipment sizes and when customers are located in remote
geographic areas as these will lead to longer transportation times with partially empty trucks [
6
].
In a warehousing strategy, trucks are loaded with products from suppliers; then, these are sent to a
warehouse or distribution centre for unloading and storing. After that, the products are retrieved,
assembled, and shipped upon customer order [
2
,
6
]. However, a warehousing strategy applies storage
and material handling costs [
6
,
9
,
10
] in addition to the possibility of having partially empty trucks
or the added cost of assembling loads from storage [
6
]. Urz
ú
a-Morales et al. (2020) developed an
optimization model to choose the physical location of the cross-docking centres to improve the design
of the urban logistics system and enhance the eciency of the distribution process and minimize its
negative environmental impacts [11].
1.2. Advantages and Applications of Cross-Docking
The distribution process accounts for 30% of the product sale cost, and this aspect increases
overheads of the overall supply chain process [
12
] and impacts negatively on the suppliers and
manufacturing process due to high competition and ease of reach to several markets [
13
]. Consequently,
there is a need to reduce the distribution costs and increase distribution eciency [
14
]. A robust
cross-docking strategy can overcome the high costs of storing and handling [
9
] through consolidating
shipments from various origins and then sending the goods to a cross-dock location where the
shipments are unloaded and immediately recombined with loads sharing the same destination [
4
,
10
].
Cross-docking can increase eciency on the usage of truck capacity and full truckloads [
10
] by
consolidating dierent-sized shipments bound for the same destination [7,15].
A cross-docking strategy has other advantages such as reducing the costs of warehousing,
inventory-holding, handling, labour, and transportation [
2
], reducing delivery lead time from suppliers
to customers [
5
,
16
], reducing storage space, reducing risks of product damage and obsolescence,
consolidation of shipment, improving resource utilization, reducing overstock [
2
], providing more
control over delivery schedules [
8
], improving service level [
9
,
16
], and lifting the rate of inventory flow
and sales turnover [5]. Thus, it is a popular firm-level strategy [15].
There are several reported applications of cross-docking in the literature. For instance, in the
food industry, cross-docking of food distribution provides better eciencies as well as significant
cost savings [
17
]. There are other successful implementations of cross-docking in retail such as
Walmart [
2
] and Oce Depot [
1
,
18
], besides those of Toyota, Goodyear, Eastman Kodak, Dots,
and LLC [
2
]. Wal-Mart was the first firm to propose this strategy and they improved their profit
and increased market share [
5
]. In 1992, Wal-Mart was the most profitable retailer globally due to
applying cross-docking on 85% of its inventory, which also contributed to a 2–3% cost saving [
1
].
The cross-docking approach is also practised in the pharmaceutical supply chain to manage speed
to market and pharma waste through better scheduling and improving medication room response
time and eliminating unnecessary activities [
19
]. Khorasani et al. (2018) reported that cross-docking is
ecient for moving numerous items within short periods of time [
19
]. Cattani, Souza and Ye (2014)
suggested applying a cross-docking system at the fulfilment centres for online retailing to reduce the
delivery times as well as the shelving and handling expenses, and shipment costs in some cases [20].
Sustainability 2020,12, 4789 3 of 19
1.3. Literature and Studies on Cross-Docking
Research studies in this field applied several models in cross-docking aiming to solve dierent
problems and improve cross-docking operations. These studies include scheduling, dock door
assignment, transhipment, vehicle routing, product allocation, and layout design and network.
Studies have investigated the vehicle routing problem to increase the eciency of decision making
at the operational level and to improve the tactical transportation plan [
21
]. These studies followed
dierent approaches such as developing a model for the vehicle routing problem by considering the
pickup and delivery routes for product transportation (Maknoon and Laporte, 2017); and investigating
the product mix allocation problem [22].
Scheduling problems in cross-docking have been well investigated by several studies. Many aspects
of the class of scheduling problems were considered in previous studies including proposing a model to
solve the truck scheduling problem in a multi-door cross-docking system [
16
]; investigating Just-in-Time
(JIT) scheduling with time windows penalties to minimize the storage time [
23
]; developing a model
for ecient scheduling of the inbound trucks to arrive on time and for outbound trucks to depart
on time [
24
]; and, developing a scheduling model for inbound and outbound trucks to minimise the
travel distance [
25
]. Truck scheduling problems in the cross-docking centres have been investigated
to identify the potential for improving the operations of cross-docks [
26
]. On the sustainability of
supply chains, Chargui et al. (2019) optimized the Rail–Road Physical Internet-Hub cross-docking
terminal operations by scheduling the outbound trucks at the docks [
27
]. Furthermore, studies in this
area have investigated problems related to the scheduling of transhipment operation in a multiple
inbound and outbound dock configuration to minimise the total cost of inventory holding and truck
replacement [
28
], the parcel hub scheduling problem to minimize the time of transfer operations [
29
],
and studied the inbound and outbound truck sequencing to minimize the total turnaround time [30].
More studies have emerged in the field of cross-docking. There are studies utilizing mathematical
models to solve problems such as developing a model that schedules the inbound trucks without
aecting the outbound trucks’ schedule to solve the problem of the randomly arriving trucks exceeding
the number of outbound trucks [
31
]; a model for assigning and clustering the destinations to the
shipping doors of the mail distribution centre to increase the eciency of the sortation centre [
32
];
a model to economise decision making regarding the cross-docking location selection problem [
33
];
designing a cross-docking layout model to determine the temporary storage location for incoming unit
loads through minimizing the travel distance of the forklift trucks with unit loads [
34
]; and developing
two models to determine a production-distribution strategy to reduce the total cost percentage and to
help in selecting the best approach of distribution for a manufacturer’s production process [35].
Furthermore, other studies investigated the assignment problems for fully loaded incoming trucks
to reduce the total handling cost [
36
], developed an approach for assigning destinations to dock doors
to minimize the number of workers in the loading operations [
37
], and compared three cross-docking
designs using a genetic algorithm to increase the eciency in the logistics distribution network [38].
1.4. Literature Gap
Upon reviewing the cross-docking models, review studies have focused on the cross-docking
operations perspective, i.e., the truck scheduling problem at the cross-docking terminals [
26
],
investigated and compared the practices between industry and academic studies by identifying the
cross-docking operations in the literature and industry practice [
1
], reviewing models on cross-docking
terminal planning [
39
], reviewing characteristics and problem types related to cross-docking [
2
],
and scheduling problems [15].
More than 85% of the academic papers on cross-docking are published post-2004 [
40
]. In addition,
only a few studies have considered the integration of dierent models to solve problems related to
cross-docking [
12
] and these studies focused on applying models to cross-docking operations [
41
].
Therefore, this paper aims to identify the recent trends in the cross-docking literature by presenting a
comprehensive review of the cross-docking models and applications.
Sustainability 2020,12, 4789 4 of 19
1.5. Research Statement and Objectives
The study reviews the extant research on cross-docking models and identifies the gaps in the
knowledge of cross-docking models and informs the future research directions in cross-docking.
This paper addresses the research gap by analysing and identifying the research foci on cross-docking.
The objective of this review is thus to identify the (1) major research areas of cross-docking, (2) techniques
and models, and (3) opportunities for future research on cross-docking.
The rest of this paper is set as follows: Section 2proposes the method of reviewing the literature.
Section 3presents the results and discussion. Section 4concludes the paper.
2. Review Method
From the research objectives, the research questions have been framed to determine the approaches
for use in this review. The research questions are, namely, (1) What are the major research areas that
have been covered in the area of cross-docking? (2) What are the techniques and models that have
been researched on cross-docking? and (3) What are future research areas in cross-docking?
The approach followed in this paper to review the literature is depicted in Figure 1.
Figure 1. Methodological framework of the review.
Sustainability 2020,12, 4789 5 of 19
To achieve the research objectives and questions, we conducted both the research focus parallelship
network (RFPN) analysis and the keyword co-occurrence analysis. This review proposes two phases
for selecting and analysing the research papers.
2.1. Phase I: Locating, Evaluating and Screening Studies
The papers selected for review were identified and searched from the following well-known
academic databases in which most of the cross-docking and supply chain journals publish, namely
ScienceDirect, Springer, Emerald, Wiley, Taylor and Francis, IEEE explorer, ACM, and Sage.
Table 1shows the keyword search formulation criteria used. The structure defines popular
keywords to obtain a broader range of studies. Accordingly, Level 1 defines the search context
“cross-docking.” Level 2 used the keyword “supply chain”, and level 3 outlines the keyword “logistics.”
The publishing dates were limited to papers published by 30 October 2018, with no restrictions on the
start date of publishing.
Table 1. Search criteria.
Keyword Formulation
Level 1 Crossdock* OR Cross-dock* OR Cross dock* OR
Level 2 Supply chain and Crossdock* OR Supply chain and
Cross-dock* OR Supply chain and Cross dock*
Level 3 Logistics and Crossdock* OR Logistics and
Cross-dock* OR Logistics and Cross dock*
To achieve the research objectives and questions, we conducted both the RFPN analysis and
keyword co-occurrence analysis. This review proposes two phases for selecting and analysing the
research papers, as follows.
A total of 609 articles were identified, and exclusion criteria were used to shortlist and screen
the papers. The following papers were excluded: open access papers, editorial notes, and conference
papers including Procedia and IFAC (The International Federation of Automatic Control). As a result,
141 papers were shortlisted for this study, over a time-window of 21 years, from 1997 to 2018 inclusive.
2.2. Phase II: Data Extraction and Co-Occurrence Analysis
We extracted the data for bibliometric analysis using the bibliometric tool BibExcel. BibExcel,
developed by Persson [
42
,
43
] facilitates data management and analysis [
44
]. BibExcel generates data
files [
43
] from large amounts of data and provides a statistical analysis [
45
], and imports the data from
research databases such as Web of Science and Scopus in formats compatible with other software such
as SPSS and Excel [44], and network analysis tools such as Pajek and Gephi [45].
The data is entered into Bibexcel in the RIS format, with information on the title, authors, journal,
publication year, and keywords. The keywords were retrieved, reviewed, and standardized because
many keywords described the same meaning but were worded dierently.
Next, the following analyses are performed using Bibexcel:
1. Journal analysis to identify the distribution of the articles in the journals;
2. Year analysis to identify the distribution of articles throughout this period;
3. Country analysis to identify the contributing countries in this field.
Thereafter, a semantic network analysis is conducted to identify the relationship between attributes
such as papers and keywords [
44
]. There are many network analysis tools such as Gephi, VOSviewer,
and Pajek [
45
]. This study uses the Gephi network analysis tool because of its ability to handle dierent
data formats; it has powerful filtering techniques [
45
] and can manipulate large datasets [
44
] in addition
to its ability to develop visual illustrations [
46
] for large networks which hastens the exploration
Sustainability 2020,12, 4789 6 of 19
work [
46
]. Then, a clustering technique is conducted to identify and classify the sub-fields of our
research topic [44,46], and the papers on similar topics are identified.
Using the network analysis, the following steps are performed: RFPN analysis.
Here, the relationship between the articles are analysed by delineating the common keywords
between these articles. Next, the information is presented as a network visualization using Gephi.
Consider a weighted, undirected, and symmetric network N formed using S, the set of papers
(S =1,
. . .
,i,
. . .
,j,
. . .
,m), and K the set of keywords (K =1,
. . .
,p,
. . .
,q,
. . .
,n) represented by a graph
G=(V, E), where V is the set of nodes and E the set of edges, respectively [
46
]. We present this network
as a co-occurrence matrix with its rows and columns representing the nodes and its elements being the
frequency of co-occurrence between each pair of nodes [47].
The network consists of nodes and edges. Each node represents an article ior j, and the edge
represents at least one keyword which is shared between two articles. The edge weight between two
nodes is equal to the number of co-occurring keywords between the two articles. The co-occurrence
matrix A of size m×m and element aij can be written as:
aij=
n
X
k=1
gijk (1)
if there is an edge from a paper ito j; 0, otherwise, where
n
X
k=1
gij (2)
represents the frequency of co-occurring keywords between nodes iand jand g
ijk
takes a value of 1 if
keyword kis listed in papers iand j, and gijk takes a value 0 otherwise [46].
This study constructs these relationships through two steps. First, we perform a co-occurrence
matrix using Bibexcel and then import the .csv file into Gephi to create a network visualization for
RFPN analysis. Second, we perform modularity computation to identify the subfields of the research
domain and cluster them. This research used Force Atlas 2 to determine the layout of clusters and
to determine which papers have common themes. Besides, modularity analysis helps researchers to
identify the number of clusters.
Keyword occurrence analysis determines the appearance of pairs of keywords over a consecutive
number of bibliographic records. If two keywords occur simultaneously in an article, then both articles
have a semantic relationship [
46
]. Such an analysis helps to identify the research themes and areas of
focus [43], and to obtain the relationship between the articles [46].
This study performs keyword co-occurrence analysis for each cluster separately and then presents
the analysis by network visualization using Gephi. The KCON analysis portrays the relationships
among the papers through their keywords. The core keyword of the cluster is positioned at the centre
of the network and the strength of its link to other keywords is revealed through the arrows connecting
the keywords to each other. Consider a network N represented by a co-occurrence matrix with its
rows and columns representing the nodes and its elements recording the frequency of co-occurrence
between each pair of nodes [46].
A network consists of nodes and edges. Each node denotes a keyword por q. An edge indicates
that the two linked keywords are listed in the same paper and the edge weight between two nodes
represents the number of papers that list both keywords. Its co-occurrence matrix Bof size n
×
n and
element bpq can be represented as follows:
Bpq=
m
X
s=1
hpqs (3)
Sustainability 2020,12, 4789 7 of 19
if there is an edge from paper pto q; 0, otherwise where
m
X
s=1
hpqs (4)
represents the frequency of papers listing both keywords pand qand hpqs takes a value of 1 if paper i
lists both keywords pand q; and takes a value 0 otherwise [46].
3. Results and Discussions
3.1. Keyword Retrieval and Standardization
Using BibExcel, 671 keywords are retrieved from 141 references. However, these keywords need
to be standardized as many keywords are describing the same meaning. First, the plural keywords
were converted to their singular form [
46
]. Next, similar keywords such as “Cross Dock,” Cross-Dock,”
and “Cross-Docking” were standardized to “Cross-Docking,” chosen based on the popularity of usage.
Finally, the keywords that describe the same field or specialised area were standardized to a keyword
of general usage; for example, terms such as “logistics,” “logistics approach,” “reverse logistics,” and
“logistics systems” were standardized to “logistics”.
After standardization, the 671 keywords reduced to 54 unique keywords. Table 2shows the top
20 most frequently occurring keywords.
Table 2. Top 20 keywords.
Keyword Frequency
Cross-Docking 110
Scheduling 55
Logistics 49
Distribution 43
Linear and Integer Programming 36
Vehicle Routing 32
Supply Chain Management 29
Docking 17
Heuristic 17
Hybrid Metaheuristics 16
Genetic Algorithm 13
Transportation 13
Tabu Search 12
Performance Management 12
Inventory Management 11
Simulated Annealing 10
Networks Planning 9
Location 9
Production 8
Warehousing 8
As cross-docking seeks to reduce time and shipment cost by reducing inventory holding [
1
],
it is associated with other vital logistics aspects such as scheduling, warehousing, and distribution.
Scheduling is significant to cross-docking as the proper scheduling of the inbound and outbound trucks,
outdoor and indoor assignments, and routing of delivery vehicles means more ecient cross-docking
operations, which further reduces the costs of inventory holding and transportation. Eciency
in cross-docking operations can also positively impact on distribution operations by expediting
distribution, minimizing transit times, and thus reducing the distribution costs and increasing customer
satisfaction [39].
Sustainability 2020,12, 4789 8 of 19
3.2. Journal Analysis
The 141 articles considered in our analysis were published in 51 subject journals. Figure 2shows
the distribution of papers by journal for the top 10 most published journals. More than half of the
141 papers are published in the top 20 journals.
Figure 2. Distribution of papers by journal.
Computers and Industrial Engineering (CAIE) is a quality academic journal with an H index of
111 (Q1) using the SCImago journal rank indicator. CAIE publishes articles in industrial engineering.
Scholars publish cross-docking papers with CAIE because it encourages authors to contribute to
problem-solving methodologies, models, and related concepts [
48
]. Computers & Operations Research
(COR) is another high-quality academic journal with an H index of 133, which encourages scholars to
contribute to methods for determining the viable solutions to problems using techniques in computing
and operations research on areas such as logistics and transportation. Researchers publish cross-docking
articles in COR because the optimization problems related to cross-docking are a part of operations
research [49].
3.3. Year Analysis
The 141 articles considered in our analysis are distributed throughout the past two decades
between June 1997 and October 2018. Nearly 2/3 of the papers appear in the past 6 years. Table S1
shows the publication frequency.
3.4. Contributing Country Analysis
The contributing countries have been analysed to determine which countries have made the most
contribution to this research area.
The countries are extracted from the author aliations. Each author is considered separately as
well as by country. Some authors have more than one country aliation in the same paper. This study
considers each country as a contribution even if they were for one author from one paper. Moreover,
dierent countries for dierent authors from one paper are taken into consideration and each one is
considered as a contributing country. The total number of unique contributing countries is 40. Table S2
shows the number of papers published annually by the top 10 contributing countries. Clearly, MIT in
the US is the world leader in the engineering and technology subject area (www.topuniversities.com).
Also, many Iranian scholars are very interested in performing mathematical analysis of real-world
problems and have strong capabilities in delivering research related to a variety of engineering topics
(www.usnews.com). Cross-docking needs extensive mathematical modelling and programming for
Sustainability 2020,12, 4789 9 of 19
scheduling, vehicle routing, and network optimization. Hence, it is an interesting research area
for them.
3.5. RFPN Analysis
This study performs the RFPN analysis in two steps. First, we construct the co-occurrence matrix
using BibExcel. Next, we create the network visualization for the RFPN by importing the .csv file into
Gephi. After importing the .csv file into Gephi, a network of 141 nodes and 8,059 edges was created.
Figure S1a–c represent the raw network with 141 nodes, the Force Atlas 2 layout with no outliers, and
the final network.
The RFPN visualization is shown in Figure S2 and shows the paper ID (see Supplementary
Materials S1: List of Reviewed Papers to identify the paper ID). The node sizes are proportional to
their eigenvector centrality score ranging from 0 to 1. A paper with an eigenvector centrality value of 1
is the most connected node in the network [46].
3.6. RFPN Clustering
This section identifies the sub-fields of the research domain by computing the modularity and
then forming the RFPN network into 4 clusters as shown in Figure S3. Cluster 1 comprises 47 papers,
while clusters 2, 3, and 4 have 40, 25, and 29, respectively, indicating that scholars pay attention to the
research areas of these clusters. The eigenvector is calculated in order to specify the lead nodes in each
cluster [46]. Table S3 shows the top 10 articles in each cluster based on the eigenvector centrality.
The articles with higher eigenvector centrality have greater influence in the cluster and represent
the core research area. In general, the results show a strong association between the articles in all the
clusters especially in Cluster 2 because its articles share a higher eigenvector centrality compared to
the articles in the other clusters which means that the articles in Cluster 2 have a strong association
with the other clusters.
3.7. Keyword Co-Occurrence Analysis
The keyword co-occurrence analysis was constructed using BibExcel for the keywords from
each cluster separately. A generated .net file format was then imported into Gephi to obtain the
keyword co-occurrence network for each cluster separately as shown in Figures 36respectively.
The Force Atlas 2 layout algorithm has been used to position the keywords that are linked in close
proximity. The eigenvector centrality was measured to identify the core keywords in each cluster.
Table 3represents the top 20 keywords based on the eigenvector centrality in each cluster and helps
to identify the keywords for a particular research area. Put simply, the major research themes in
each cluster were identified from the influential keywords in each cluster. Consequently, the results
retrieved 45, 35, 25, and 26 keywords from Clusters 1, 2, 3, and 4 respectively.
A higher eigenvector centrality for a keyword, represented by a node, indicates that this
keyword has many neighbours or important neighbours [
46
]. From Table 3, five keywords
(Cross-Docking, Vehicle Routing, Linear and Integer Programming, Simulated Annealing, and
Performance Management) are found in all four clusters with high eigenvector centrality, indicating
a strong association between these clusters. Moreover, there are 5 keywords (Scheduling, Heuristic,
Tabu Search, Docking, and Variable Neighbourhood Search) found only in Clusters 1, 2, and 4; two
keywords (Supply Chain Management and Transportation) are found in Clusters 1, 3, and 4; and
two keywords (Production and Distribution) are found in Clusters 2, 3, and 4. Further, there are
three keywords (Location, Stochastic Programming, and Warehousing) common to Clusters 1 and 4,
three keywords (Logistics, Graph Theory, and Mathematical Models) found in Clusters 2 and 3, two
keywords (Genetic Algorithm and Inventory Management) found in Clusters 1 and 3, one keyword
(Hybrid Metaheuristics) in Clusters 1 and 2, and one keyword (Manufacturing) in clusters 3 and 4.
Those keywords appearing in more than one cluster are termed as bridging keywords. They
highlight the relationship between the clusters. The more common the keywords between two clusters,
Sustainability 2020,12, 4789 10 of 19
the greater is the association between them [
46
]. Clearly, the association between all 4 clusters is strong
as they are connected through five bridging keywords.
It is worth noting that the cross-docking bridge keyword has a high eigenvector centrality in
all clusters indicating its importance in all of the 4 clusters. Vehicle routing is critical in supporting
cross-docking execution which includes warehouse management and distribution management [
50
]
and helps in reducing operating costs.
Therefore, many studies integrate both cross-docking and distribution strategies in order to
extend the integrated optimal solution such as minimizing the transportation costs and increasing
the eciency of deliveries and pickups [
51
]. Scholars have solved vehicle routing problems by
applying various models and techniques such as Tabu search (TS) [
30
], iterated local search heuristics
(ILS) [
52
], mixed-integer programming [
39
,
53
], simulated annealing (HAS) [
51
], and hybrid fuzzy
possibilistic-stochastic programming [
54
]. Integer linear programming models are also considered in
cross-docking studies because these models help to solve many related problems such as reducing
transportation costs, increasing shipment rates, improving product management, designing the supply
chain network, and providing scheduling and routing solutions [
55
]. Simulated annealing is a widely
used meta-heuristic applied to cross-docking to tackle the combinatorial nature of cross-docking
including location, assignment, and movement scheduling [
56
]. Performance management is
also related to cross-docking as improving performance increases satisfaction, loyalty, and market
opportunities [57].
Thus, we identified each cluster’s research theme through the eigenvector centrality of the
keywords for each cluster. Cluster 1 is Vehicle Routing/Inventory Management, Cluster 2 is Scheduling,
Cluster 3 is Logistics, and Cluster 4 is Warehousing and Distribution respectively.
Figure 3. Keyword co-occurrence analysis of Cluster 1: Vehicle Routing/Inventory Management.
Sustainability 2020,12, 4789 11 of 19
Figure 4. Keyword co-occurrence analysis of Cluster 2: Scheduling.
Figure 5. Keyword co-occurrence analysis of Cluster 3: Logistics.
Figure 6. Keyword co-occurrence analysis of Cluster 4: Warehousing and Distribution.
Sustainability 2020,12, 4789 12 of 19
Table 3. Top 20 keywords based on eigenvector centrality in each cluster.
Rank Cluster 1: Vehicle
Routing/Inventory Management Cluster 2: Scheduling Cluster 3: Logistics Cluster 4: Warehousing and Distribution
Keywords EVC* Keywords EVC Keywords EVC Keywords EVC
1 Cross-Docking 1 Scheduling 1 Logistics 1 Distribution 1
2 Supply Chain Management 0.76299 Cross-Docking 0.9729 Cross-Docking 0.734327 Cross-Docking 0.833576
3 Vehicle Routing 0.743678 Logistics 0.788799 Distribution 0.69745 Linear and Integer Programming 0.782785
4 Linear and Integer Programming 0.699076 Hybrid Metaheuristics 0.682326 Supply Chain
Management 0.630789 Vehicle Routing 0.713216
5 Transportation 0.540495 Docking 0.649973 Vehicle Routing 0.578903 Warehousing 0.67861
6 Hybrid Metaheuristics 0.538099 Simulated Annealing 0.600426 Linear and Integer
Programming 0.548282 Location 0.665832
7 Genetic Algorithm 0.534187 Linear and Integer
Programming 0.532457 Networks Planning 0.498123 Supply Chain Management 0.654276
8 Scheduling 0.51701 Tabu Search 0.491198 Transportation 0.483642 Transportation 0.565639
9 Heuristic 0.467514 Ant Colony 0.443876 Manufacturing 0.441717 Production 0.516442
10 Tabu Search 0.446385 Performance
Management 0.422078 Graph Theory 0.43476 Heuristic 0.516039
11 Particle Swarm Optimization 0.42075 Graph Theory 0.418574 Genetic Algorithm 0.379535 Stochastic Programming 0.502261
12 Inventory Management 0.412083 Mathematical Models 0.413196 Inventory Management 0.347127 Scheduling 0.454689
13 Docking 0.39131 Distribution 0.407254 Storage 0.337752 Tabu Search 0.4485
14 Synchronisation 0.347521 Heuristic 0.39393 Harmony search 0.310071 Performance Management 0.414748
15 Location 0.319009 Variable Neighbourhood
Search 0.380262 Simulated Annealing 0.310071 Docking 0.381244
16 Simulated Annealing 0.318885 Production 0.370989
Discrete Event Simulation
0.307689 Fuzzy Logic 0.363862
17 Variable Neighbourhood Search 0.28169 Vehicle Routing 0.350027 Performance
Management 0.256504 Simulated Annealing 0.327904
18 Stochastic Programming 0.271833 Dynamic Programming 0.346912 Mathematical Models 0.233168 Variable Neighbourhood Search 0.289903
19 Performance Management 0.253003 Multi-objective
Optimization 0.323562 Production 0.233168 Manufacturing 0.243781
20 Warehousing 0.242288 Response Surface
Methodology 0.323562 Clustering 0.218689 Non-stationary 0.192331
* EVC—Eigenvector centrality.
Sustainability 2020,12, 4789 13 of 19
3.8. Discussion
Cross-docking is a valuable supply chain strategy given that it oers several benefits, for example,
reduction in inventory holding costs, reduction in transportation costs, and on-time deliveries [
58
60
].
By looking at the end-to-end supply chain process, cross-docking involves product flow from the
manufacturing plant directly to the customers with minimal or no warehousing in-between. In addition,
implementing cross-docking is aligned to the lean thinking approach, which leads to cost reduction.
For instance, implementing retail cross-docking at supermarkets that deliver multiple orders from
suppliers utilizes less workforce due to the products no longer requiring picking and putting away in
the supermarket’s warehouse facility.
3.8.1. Techniques Used for Cross-Docking Optimization
All the clusters include techniques and models on optimization and simulation. Mathematical
optimization is used to help in making complex decisions and finding optimal solutions
such as minimizing trac during loading/unloading dock scheduling, improving cross-docking
assignment [61], and selecting best product sizes for cross-docking [62].
Simulation is used to mimic real-world operations [
61
], such as developing an event simulator to
evaluate package flows and sortation times under various cross-docking output station assignments [
61
]
and simulating various warehouse floor area configurations related to optimal cross-dock capacity and
movement eciency [
63
]. The simulation models that have been applied include the more traditional
approaches of hybrid metaheuristics, genetic algorithm, Tabu search, particle swarm optimization,
simulated annealing, and variable neighbourhood search. For instance, hybrid metaheuristics,
combining genetic algorithm, and modified variable neighbourhood search are well-used for vehicle
routing in cross-docking. Simulated annealing with Tabu search is used in network design and in
location planning for cross-docking and flow centre design. A hybrid of genetic algorithm and particle
swarm optimization is used to minimize the transportation and fixed costs of multiple cross-docked
vehicle routing with pickup, delivery, and time windows. The more frequently applied optimization
techniques used in this cluster are linear and integer programming. Our review results suggest
that there is a lack of multi-attribute decision making techniques used in cross-docking, and newer
simulation approaches such as agent-based modelling.
So far, our findings identify that both optimization and simulation are used extensively in
cross-docking. Scheduling (Cluster 2) has adopted the use of optimization and simulation more than
the other clusters (5 for simulation and 6 for optimization) followed by vehicle routing/inventory
management (Cluster 1) (6 for simulation and 3 for optimization). Both logistics (cluster 3) and
warehousing and distribution (Cluster 4) adopted 6 techniques (4 for simulation and 2 for optimization,
and 3 each for simulation and optimization, respectively). For vehicle routing/inventory management
(Cluster 1) and logistics (Cluster 3), simulation is twice more likely to be used for the optimization.
This outcome is attributed to the non-polynomial nature of the problem at hand. While in the areas
of scheduling (Cluster 2) and warehousing and distribution (Cluster 4), scholars have used both
techniques almost equally.
Based on the eigenvector values, linear and integer programming is the most prevalent technique
used in the optimization suite to solve vehicle routing/inventory management (Cluster 1), logistics
(Cluster 3), and warehousing and distribution (Cluster 4). Integer linear programming models are
applied in cross-docking because of their eciency in providing optimal solutions [
55
], such as
minimizing vehicle and transportation costs [
51
], scheduling truck fleets [
64
], and optimizing internal
operations in a less-than-truckload cross-dock [
65
]. In the domain of scheduling (cluster 2), hybrid
metaheuristics is the most important technique.
Hybrid metaheuristics is also the second important approach applied to vehicle routing/inventory
management (Cluster 1). Metaheuristics models are used to provide good solutions, such as finding the
best vehicle routing in a short time [
66
] and providing vehicle routing under stochastic demand [
67
].
Simulated annealing is the second most commonly applied technique in scheduling (Cluster 2), due to
Sustainability 2020,12, 4789 14 of 19
its ecacy in solving combinatorial problems such as truck scheduling in multiple door cross-docking
systems and truck scheduling for fixed outbound schedules [
56
]. In logistics (Cluster 3), the second
most commonly used technique is graph theory, which helps to visualize systems as graphs to facilitate
decision making [
68
]. In warehousing and distribution (Cluster 4), heuristics are the second commonly
used technique for problems, such as scheduling cross-dock operations [65].
3.8.2. Implications of Research in each Cluster
Inventory cost is one of the three most significant cost centres for supply chain management.
Cross-docking is a remedy to reduce the total cost of ownership by minimizing the inventory cost
without compromising the responsiveness of the supply chain. Firms are constantly striving to develop
ecient approaches to optimize the operations of cross-docks and minimize the total cost of the system.
Firms also try to optimize the scheduling of the inbound and outbound vehicles by minimizing the idle
time or empty travels of the vehicles. Given a large number of decision variables in scheduling, vehicle
routing, and inventory management for cross-docking operations, non-linear optimization models are
more suitable to handle them. Therefore, optimization techniques using metaheuristics and simulation
are commonly used for this purpose. Inventory management, vehicle routing, and scheduling are
highly interrelated cost centres as they are directly related to logistics and warehousing. In other
words, all these operations must be integrated through a highly ecient decision support system
(DSS). Such a DSS ensures that the operations of the cross-dock are performed seamlessly to satisfy
the requirements of the set of the supply chain partners. Cross-docking is a relatively new concept in
logistics and warehousing; however, it has provided organizations with many advantages such as
lower demand for inventory investment and storage space, speeding the deliveries, and improving
inventory turnover.
In the past five years, more than 40% of the food and beverage industry logistics executives
surveyed have increased their cross-docking practices. This increase is due to the businesses seeking
new approaches to fulfil demand and the need for just-in-time services. Besides, businesses would
like to improve service to customers [
69
]. For example, through cross-docking, perishable products
including food and beverages reach the marketplace quicker, fresher, and with preserved quality.
For instance, the large grocery chains can now keep fresher products on their shelves consistently.
The main role of cross-docking in this practice is related to logistics and transportation. Other
characteristics for cross-docking practices are shipping patterns and high-volume products with a
short time window. Also, cross-docking practices can work best for multiple stock-keeping units
(SKUs) that are consistently shipped to multiple locations. Some store chains like grocery chains, bring
the products into one location and re-split them to multiple locations at dierent stores. A study of
cross-docking in the retail sector [
70
] examined the costs/benefits of implementing the cross-docking
strategy in a major French retail supply chain, in particular, fast-moving consumer goods.
Previous studies show that the ingredient factors for logistics and distribution companies to
implement supply chain and logistics are collaboration with other supply chain trading partners or
logistics service providers, eective communication, and synchronization of inbound and outbound
shipments. Moreover, the Logistics Bureau has suggested six useful factors to practise cross-docking in
logistics and distribution strategies [71], i.e.,
use conveyors if possible,
housekeeping needs to be up-to-date,
use dock space sparingly,
locations require adequate yard space,
shipment-staging area needs to well-organized,
technology-based solutions are useful.
Warehouse Management Systems (WMS) are designed to facilitate cross-docking operations and to
address issues related to inventory and data visibility, traceability, supply chain agility, and operational
Sustainability 2020,12, 4789 15 of 19
adaptability. To implement WMS for a cross-docking facility, Automated Data Collection (ADC)
integration is required to capture data in real-time to reduce manual data entry and errors while
improving data accuracy within the facility.
3.9. Future Research Directions
Looking at the existing research in cross-docking, the following avenues are recommended to
future researchers.
Uncertainties in demand and supply reduce the eciency of statically optimized operations. In a
global supply chain, a fair amount of stochasticity and variability in time exist, the supply chain
stakeholders need to adapt to those perturbations. Research can study the dynamic optimization
of cross-docking operations when the supply and demand fluctuate with time.
Since cross-docking seeks to improve customer satisfaction by providing better services at a
relatively lower cost, future research can be devoted to evaluating the relationships among
customer satisfaction, operational eciency of the firm, and the operational eciency of the
cross-docking centres.
Transportation is one of the top three industries significantly contributing to environmental
pollution. Future studies can develop multi-objective optimization models to minimize
environmental pollution along with the traditional objectives such as minimizing delivery
time, location, and resource allocation decisions.
Corporate social responsibility is a major concern for firms. Planning seaports and dry ports in
crowded and congested areas needs cross-docking transportation and delivery services to address
the social welfare of residents in those urban areas. Future studies can analyse the cost-benefit
trade-os of establishing cross-docks in urban areas and examine the vehicle routing optimization
for cross-docks under social sustainability and corporate social responsibility.
To enhance cross-docking operations and to be a smart and intelligent logistics system,
the adapted concepts and design solutions should be developed to provide systematic management
of the smart operation in cross-docking which are synchronized with other cross-docking
problems. Future research may focus on developing smarter and more advanced technologies to
support cross-docking.
Since the internal operations between the inbound and the outbound dock-doors of cross-docking
have not attracted more attention, future research on cross-docking can focus on the task scheduling
inside the cross-docking terminals considering resource capacity and constraints.
Given the supply chain risks and uncertainties (for instance, COVID-19; uncertainties in
demand and supply), future research of cross-docking operations can examine the sources of
uncertainty (identification and discussion of the risks and uncertainties) to develop distributionally
robust optimization models which can improve the practicality, accuracy, and eciency of the
cross-docking operations.
From the logistics, warehouse, and distribution performance point of view, there are several
recommendations to improve their operations through cross-docking practices; for example,
developing a suitable WMS for cross-docking operations, and the continuous improvement of
suppliers through strategic supplier partnerships with higher transparency.
4. Implications and Conclusions
This study reviewed the research on cross-docking between 1997 and 2018. BibExcel and the
Gephi network analysis tool were used as the bibliometric analysis tools to visualize the interrelations
among the papers and their characteristics such as keywords, authors, country of authors, and year
of publication. We identified four streams of research in cross-docking: vehicle routing/inventory
management, scheduling, logistics, warehousing, and distribution.
Sustainability 2020,12, 4789 16 of 19
Policy implications relate to the way in which policies may improve various elements of
cross-docking, including scheduling, warehousing, and logistics operations, and support the improved
distribution of goods. The implementation of an adaptive policy instead of a fixed stock policy, in line
with the findings of Khorasani et al. [
19
], is better placed to allow for cost savings in cross-docking
situations. Inventory ordering policy, a key opportunity to gain eciencies in cross-docking [
72
],
suggest that replenishment policies are key to gaining time and cost improvements with cross-docking.
Buijs et al. (2016) propose that dynamic allocation policies for assigning trucks to cross-docking doors is
an important policy for gaining cross-docking advantage. Policies that oer flexible and agile solutions
appear to have greater fit with cross-docking [60].
Reviewing the techniques used for cross-docking operations showed that linear and integer
programming, non-linear programming, and stochastic programming are the most frequently used
optimization approaches for cross-docking. Several meta-heuristics such as particle swarm optimization,
tabu search, simulated annealing, and genetic algorithm have been applied to simulate cross-docking
operations. From the systematic literature review on cross-docking, we have oered several directions
for future research. Although we reviewed many papers to perform the analysis, a limitation of
this research is that we did not include the book chapters and conference papers. Future studies
can be devoted to determining the main research streams through reviewing this set of publications.
Furthermore, we only worked on papers that have been published in English. Future researchers can
review the relevant documents published in the other languages.
Supplementary Materials:
The following are available online at http://www.mdpi.com/2071-1050/12/11/4789/s1:
Figure S1: Stages in RFPN analysis visualization using Gephi, Figure S2: RFPN visualization, Figure S3: Clustered
RFPN with node size proportional to eigenvector centrality, Table S1: Publication frequency, Table S2: Number of
papers published for the top 10 countries by year, Table S3: Top 10 articles in RFPN clusters based on eigenvector
centrality, Supplementary Materials S1: List of papers reviewed with the relevant ID.
Author Contributions:
Conceptualization, R.K.M. and M.G.; methodology, R.K.M., M.G., A.A.K; software,
A.A.K.; formal analysis, F.J., N.K.M., A.A.K.; writing—original draft preparation, A.A.K., R.K.M., F.J. and N.K.M.;
writing—review and editing, K.B. and S.B.; visualization, A.A.K.; supervision, R.K.M.; funding acquisition, R.K.M.
All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Edith Cowan University (ECU) Collaboration Enhancement
Scheme (CES).
Conflicts of Interest: The authors declare no conflict of interest.
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... Gudang crossdock atau crossdocking adalah suatu gudang atau fasilitas logistik yang berfungsi untuk mendistribusikan barang serta dirancang secara khusus untuk mengkonsolidasi keinginan pelanggan secara efisien tanpa mengurangi efisiensi pengiriman atau logistic cost (Mavi et al., 2020). Dalam proses rantai pasok, gudang cross-dock sebagai suatu tempat transit cepat barang sebelum masuk ke pelanggan selanjutnya dengan meminimalkan biaya simpan dengan cara memindahkan barang secara langsung dari kendaraan besar ke kendaraan yang lebih kecil secara cepat tanpa melalui penyimpanan jangka panjang (Aryes & Odegard 2017). ...
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... Gudang crossdock atau crossdocking adalah suatu gudang atau fasilitas logistik yang berfungsi untuk mendistribusikan barang serta dirancang secara khusus untuk mengkonsolidasi keinginan pelanggan secara efisien tanpa mengurangi efisiensi pengiriman atau logistic cost (Mavi et al., 2020). Dalam proses rantai pasok, gudang cross-dock sebagai suatu tempat transit cepat barang sebelum masuk ke pelanggan selanjutnya dengan meminimalkan biaya simpan dengan cara memindahkan barang secara langsung dari kendaraan besar ke kendaraan yang lebih kecil secara cepat tanpa melalui penyimpanan jangka panjang (Aryes & Odegard 2017). ...
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Efficiency within a company must be enhanced to maintain competitiveness and business sustainability, particularly in logistics firms. In logistics processes, minimizing costs is a critical indicator of operational efficiency. This study applies optimization through linear programming to a cross-dock company characterized by fluctuating demand, focusing on a fast-moving product. The analysis begins by calculating the minimum and maximum demand based on twice the standard deviation from the mean, followed by determining the optimal scenarios for each depot. The results indicate that, for the eight depots, the majority of the truck selection involved using single-axle trucks. Consequently, the total cost from the first to the seventh week was Rp. 1,785,000,000, while the LP simulation results indicated a total cost of Rp. 1,214,000,000, representing a potential cost saving of Rp. 554,000,000. Keywords: Cross-Dock, Linear Programming, Logistic Cost, Optimization
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... It is responsible for the smooth movement of goods from the production stage to the hands of the final consumer with optimal time efficiency and cost expenditure (Benrqya et al., 2020). Traditional distribution systems often involve multiple handling stages, storage facilities, and various modes of transport, which can result in increased transit times and operational costs (Kiani Mavi et al., 2020). ...
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... Structure based virtual screenings are widely used for identification of new lead compounds for specific targets that their experimental 3D structure is available [26]. In this case the compounds screened against JAK3 (5lwm) were cross docked with structures of JAK1 and JAK2 protein to draw conclusions on the binding affinity of the proteins with the selected compounds. ...
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... Considering the objectives of the studied problem, we can highlight, such as those mentioned in [17][18][19], some performance ensures can be detached: Inventory level, Total product stay time, Truck process time deviation, Total loading time, Total unloading time, Makespan and Balanced workload. These authors emphasize that the most popular objectives are based on time-based conditions, with the Makespan being the most popular. ...
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