MSCRA-04-2019-0011

A systematic literature review

on uncertainties in

cross-docking operations

Allahyar (Arsalan) Ardakani and Jiangang Fei

National Center for Ports and Shipping, Australian Maritime College,

University of Tasmania, Launceston, Australia

Abstract

Purpose – The technique of cross-docking is attractive to organisations because of the lower warehousing and

transportation (consolidated shipments) costs. This concept is based on the fast movement of products.

Accordingly, cross-docking operations should be monitored carefully and accurately. Several factors in

cross-docking operations can be impacted by uncertain sources that can lead to inaccuracy and inefficiency of

this process. Although many papers have been published on different aspects of cross-docking, there is a need

for a comprehensive review to investigate the sources of uncertainties in cross-docking. Therefore, the

purpose of this paper is to analyse and categorise sources of uncertainty in cross-docking operations.

A systematic review has been undertaken to analyse methods and techniques used in cross-docking research.

Design/methodology/approach – A systematic review has been undertaken to analyse methods and

techniques used in cross-docking research.

Findings – The findings show that existing research has limitations on the applicability of the models

developed to solve problems due to unrealistic or impractical assumption. Further research directions have

been discussed to fill the gaps identified in the literature review.

Originality/value – There has been an increasing number of papers about cross-docking since 2010, among

which three are literature reviews on cross-docking from 2013 to 2016. There is an absence of study in the

current literature to critically review and identify the sources of uncertainty related to cross-docking

operations. Without the proper identification and discussion of these uncertainties, the optimisation models

developed to improve cross-docking operations may be inherently impractical and unrealistic.

Keywords Warehousing, Supply chain management, Uncertainties, Cross-docking, Distribution centres,

Systematic literature review

Paper type Literature review

1. Introduction

Over recent years, competition between companies forced them to cut costs to remain in the

market. Cross-docking, which refers to direct shipment of receiving products from inbound

trucks to the outbound trucks, is a just-in-time and lean system of distribution, which

makes an essential contribution to the rapid movements of goods (Nassief et al., 2016).

This approach of distributing products helps reduce costs and leads to better service to the

customers. Distribution of products in an efficient way along supply chain is a complex task

that needs a careful attention to address a large number of challenges such as uncertainties,

just-in-time and cost-effective distribution (Dulebenets, 2019). Consequently, many

businesses try to address these challenges by using cross-docking, but cross-docking

operations are influenced by the dynamic nature of the business.

Cross-docking operations consist of receiving of inbound trucks and ass igning them

to the doors of cross-docking centre and the same fo r shipping trucks and doors.

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2,1

Applications. Published by Emerald Publishing Limited. This article is published under the Creative

Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create

derivative works of this article (for both commercial and non-commercial purposes), subject to full

attribution to the original publication and authors. The full terms of this licence may be seen at http://

creativecommons.org/licences/by/4.0/legalcode

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/2631-3871.htm

Received 10 April 2019

Revised 23 October 2019

Accepted 29 October 2019

Modern Supply Chain Research

and Applications

Vol. 2 No. 1, 2020

pp. 2-22

Emerald Publishing Limited

2631-3871

DOI 10.1108/MSCRA-04-2019-0011

The operations include the process of unloading receiving trucks, consolidating products

insid e of the cross-docking centre and according to the available resources and available

shipping trucks, transferring the products to the temporary storage, and l oading the

products to the shipping trucks according to their destination . Variations in the volume of

work, available resources and possib le disruptions in the process are uncertainties that can

impact the cross-docking operations. Cross-docking centres have to be flexible to overcome

challenges, such as short lead times, real-time responses and the supply of a wide var iety of

products (Ardakani et al.,2020). As a result, distribution centr es need a system that can

minimise the negative impac t of uncertainties in the whole proces s.

Uncertainties in the supply chain can be from environmental or systemic sources ( Ho,

1989). The performance of different members of a supply chain, such as suppliers and

manufacturers, can bring environmental uncertainties, and some activities in a supply chain,

such as production and distribution, may bring systemic uncertainties (Ho, 1989). Gong and

de Koster (2011), however, classified uncertainties according to their locations of occurrence,

for example, uncertainties inside or outside the supply chain, inside or outside the warehouse,

and uncertainties between warehouse control system.

Over recent years, distribution centre managers have used various innovative approaches

to develop robust operations and plans against uncertainties. These attempts although solved

part of problems, many issues still remain causing disruptions in the process of cross-docking

(Gong and de Koster, 2011). On the other hand, researchers have tried advanced optimisation

methods to reduce the negative impact of uncertainties on supply chain and cross-docking

operations (Kenne et al., 2012; Lee et al., 2010). During the last decades, many papers focussed

on deterministic models to address problems in a stable environment considering various

factors influencing cross-docking operations. In addition to supply uncertainties resulted from

the suppliers or manufacturers or demand uncertainties from end users and retailers, there are

other sources of uncertainty that can affect cross-docking operations. Delay in arrival time of

trucks, changes in the contents of a truck, truck breakdown, unloading incoming trucks, a

breakdown in handling facilities, the absence of workers, loading, shipping, and delay in the

departure time of vehicles can all be considered operations that are prone to uncertainty.

Several literature reviews on cross-docking have been published. Van Belle et al. (2012)

carried out a review on cross-docking which considered all aspects of cross-docking problems

from operational to physical characteristics. They covered a broader range of definitions and

categories to complement the studies of Boysen and Fliedner (2010) and Agustina et al. (2010).

There has been an increasing number of papers about cross-docking since 2010, among which

three are literature reviews on cross-docking from 2013 to 2016 (Buijs et al.,2014; Ladier and

Alpan, 2016a; Walha et al., 2014). However, there is an absence of study in the current literature

to critically review and identify the sources of uncertainty related to cross-docking operations.

Without the proper identification and discussion of these uncertainties, the optimisation models

developed to improve cross-docking operations may be inherently impractical and unrealistic.

The remainder of this paper is organised as follows. Section 2 describes the research

method used to explore the relevant literature. In Section 3, the identified studies are analysed

using thematic statistics to identify and classify the uncertainty components. The limitations

of existing literature are discussed in Section 4 with future research directions being proposed.

2. Method for literature review

The objectives of this literature review are to examine the studies in cross-docking under

uncertainty so that all possible sources of uncertainty can be identified and the limitations of

existing studies can be discussed. To achieve this objective, a systematic literature review

(SLR) was conducted. To carry out a literature review, a wide range of research should be

studied. However, it is impossible to consider all studies unless it is a new field (Seuring and

€

uller, 2008). To define the area of research, selection criteria and research steps to produce

Uncertainties

in cross-

docking

operations

a better review of literature, SLR guidelines are adopted. A SLR can be divided into four

stages (Denyer and Tranfield, 2009; Tranfield et al., 2003) including planning, conducting a

review, analysis and presenting the findings.

2.1 The planning process in SLR

To develop a coherent flow, the gaps in the literature need to be identified and discussed. To

present a comprehensive literature review of cross-docking under uncertainty, the following

questions are framed to guide the literature review:



Which decision levels are considered?



What uncertainties are considered?



What performance measures are discussed?



What methodology is used?



What are the limitations?

2.1.1 The searching and screening process in SLR. Boolean logic was used to define the

keywords for the search. The following keywords were selected: “cross-dock*” AND

“uncertainty” AND “supply chain”. After determining the keywords, eight databases were

identified and selected including Scopus, web of science sciencedirect, Emerald, Wiley Online,

Springer Online, Taylor & Francis and ProQuest. Google Scholar was used as a separate

database. The period for the data search was set from 1980. According to Krajewski et al. (1999)

and Apte and Viswanathan (2000), the cross-docking approach started from the 1930s. However,

it only became popular from the 1980s after the successful experience of Walmart. In addition,

we excluded the strategic level because these studies tend to focus on infrastructure and

facilities development prior to the construction of cross-docking centres. Other inclusion criteria

were that the research was written in English and the document was either a published paper, a

thesis, a book or a chapter. After applying these rules, 1,351 items were found. The list was then

checked for duplication which resulted in 234 items being excluded. In the screening process, the

authors read the title, abstract and conclusion of the remaining studies and excluded studies

that did not have uncertainty in abstract and conclusion. This process resulted in 1,079 being

removed and 38 remained. In addition to the database search, a snowball approach was used to

avoid the possibility of missing relevant papers. The searching and screening process resulted

in 46 papers which have been included in this literature review.

2.1.2 The analysing process in SLR. In evaluating the selected studies, the approach

suggested by Tranfield et al. (2003) was used. Each study was evaluated using descriptive

and thematic analysis (Table I).

Category Information

Descriptive analysis Year Year of publication

Country Authors affiliation

Type of document Journal, conference, thesis

Thematic analysis Solution method Review, simulation, exact method, heuristics,

meta-heuristics

Research area Research is related to which area in

cross-docking problems?

Uncertainty component Which uncertainty factor is considered?

Decision level The problem belongs to which decision level?

Performance measurement Which performance measure(s) was considered?

Table I.

Classifications used in

categorising and

analysing data in SLR

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2.1.3 Presenting the findings in SLR. Descriptive statistics findings through SLR. While the

search criteria were set from 1980, a majority of studies on uncertainties in cross-docking

started from 2008 with the first one appeared in 2004 (Figure 1). There has been an increase

number of studies from 2012. In terms of the research context of these studies, a majority of

studies were from developed economies with the USA having the greatest number (Figure 2).

Among these published studies, a third were published in journals, about a quarter were

thesis and over 40 per cent were conference papers (Figure 3).

3. Thematic findings: uncertainty components in cross-docking

centres operations

In this step, all research items were reviewed according to the components of uncertainties.

Following the discussion below, tables are presented to summarise the essential features of

each study. The papers were categorised based on the sources of uncertainty as shown in

000

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Number of Research works

Time slot

222

NUMBER OF FREQUENCY

China

France

Iran

Korea

Malaysia

The Netherlands

Singapore

Taiwan

Tunisia

USA

Figure 1.

Distribution of

documents between

time slots

Figure 2.

Countries of published

papers

Uncertainties

in cross-

docking

operations

Table II, and information on the performance measures used in these studies is provided

in Table III. Table IV summarises the solution methods. Table IV is presented. Based on an

analysis of the reviewed studies, a framework is developed to illustrate the composition of

uncertainty components in cross-docking operations (Figure 4).

3.1 External uncertainty components

In this part, each component of research is analysed in detail according to the external

uncertainty component.

3.1.1 Demand. Demand is one of the main factors of uncertainty in supply chain

environment. Most businesses are faced with the challenge of accurately predicting

customer needs in terms of product type, quantity and timing of delivery. The inability or

inaccuracy in predicting demand has a flow-on effect on cross-docking operations. Existing

literature on cross-docking only considered the impact of demand uncertainty on network

leaving the effect of cross-docking operations unaddressed.

According to Yan and Tang (2009), demand uncertainty can have a negative impact on

system performance in terms of total expected cost. The impact can be decreased by

employing pre- or post-distribution strategies. According to the results, pre-distribution is

preferred when demand is stable. However, in a situation where the demand is uncertain,

post-distribution is preferred. Pre-distribution has less impact on cross-docking

operations because suppliers have done all necessary preparation, while in post-

distribution the process of preparing happens inside the cross-docking centre leading to

high operation costs. A weakness of Yan and Tang (2009) is that the pre- and post-

distribution strategies were evaluated in isolation from other problems such as scheduling

and dock-door assignment in DC which may affect the outcomes of the distribution

strategies. Using a robust optimisation model, Spangler (2013) addressed the demand

uncertainty from a strategic level through location selection for the cross-docking centre

to ensure that the centre can handle changes in demand caused by seasonal fluctuation

and adverse weather conditions. The outcomes of Spangler’s (2013) research may be

helpful for the initial planning of a cross-docking centre but less relevant to the operation

of the centre.

Inability in prediction of demand can lead to a delay of trucks at cross-docking centres

and more gas and carbon emissions (Arnaout et al., 2010; Rodriguez-Velasquez et al., 2010).

Arnaout et al. (2010) considered demand, lead-time and service time as stochastic

parameters, which improved the results by reducing the use of unrealistic constraints in

their models. The results indicate that truck utilisation can be decreased by using

cross-docking centres and larger trucks when demand is uncertain. However,

41%

33%

26%

Conference Journal Thesis

Figure 3.

Type of published

research works

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2,1

Type of

uncertainties Component of uncertainties

Name of authors External Internal

Truck

arrival time

Availability

of trucks

Truck

departure

time

Processing

time Demand

Available

resources Supply

Type of cross-dock

problem

M.K. Acar (2004) UU U U Truck-to-door scheduling

Wang and Regan (2008) UUTruck-to-door scheduling

Yu et al. (2008) UU U U UTruck-to-door assignment

McWilliams (2009) UUTruck-to-door sequencing

Yan and Tang (2009) UUDistribution strategy

Alpan (2010) UU UTruck sequencing

Arnaout et al. (2010) UUCross-docking operation

Rodriguez-Velasquez et al.

(2010)

UUCross-docking operation

Tang and Yan (2010) UUDistribution strategy

Larbi et al. (2011) UU UTruck sequencing

Sathasivan (2011) UUTruck scheduling problem

K. Acar et al. (2012) UU U U Truck-to-door scheduling

Li et al. (2012) UU U U U Truck scheduling

Shakeri et al. (2012) UUTruck-to-door scheduling

Soanpet (2012) U U Location and routing

Guignard, Hahn, and

Zhang (2013)

UU Truck-to-door scheduling

Konur and Golias (2013a) UU Truck-to-door scheduling

Konur and Golias (2013b) UU Truck-to-door scheduling

Shi et al. (2013) UU U U UUScheduling

Spangler (2013) UULocation of cross-dock

Zaerpour (2013) UU Cross-dock storage

operation

Cattani et al.

(2014) U U Flow of network

Ladier (2014) UU U U U U Truck scheduling

Ladier et al. (2014) UU U U U Truck scheduling

Walha et al. (2014) UU U U U U U Dock-door assignment

(continued )

Table II.

Uncertainty

components and types

of cross-docking

problems

Uncertainties

in cross-

docking

operations

Type of

uncertainties Component of uncertainties

Name of authors External Internal

Truck

arrival time

Availability

of trucks

Truck

departure

time

Processing

time Demand

Available

resources Supply

Type of cross-dock

problem

Heidari et al. (2018) UU Truck scheduling and

truck allocation

Ladier et al. (2015) UU U U Truck scheduling

Suh (2015) UUCross-docking operation

Yin et al. (2015) UUU U Collaborative planning

and scheduling

Zaerpour et al. (2015) UU Cross-docking storage

operation

Amini and Tavakkoli-

Moghaddam (2016)

U U Truck scheduling

Fatthi et al. (2016) UU U U UTruck-to-door assignment

and scheduling

Ladier and Alpan (2016b) UU U Truck scheduling

H. Zouhaier and

Ben Said (2016)

UU Dynamic scheduling

Motaghedi-Larijani and

Aminnayeri (2017)

UU Scheduling

Houda Zouhaier and Ben

Said (2017a)

UU UDynamic truck scheduling

Houda Zouhaier and Ben

Said (2017b)

UU UDynamic truck scheduling

Motaghedi-Larijani and

Aminnayeri (2018)

UU Scheduling

Table II.

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Performance measures

Name of authors Inventory

level/cost

Working

hours

Balanced

workload

Travel

distance

Congestion Total

product

stay time

Total

time

Total unloading

time

Truck processing

time or deviation

to the deadline

M.K. Acar (2004)

Wang and Regan (2008) UU

Yu et al. (2008) UU

McWilliams (2009)

Yan and Tang (2009) UUU

Alpan (2010) U

Rodriguez-Velasquez et al. (2010) U

Tang and Yan (2010) UUU

Larbi et al. (2011) U

Sathasivan (2011) U

K. Acar et al. (2012)

Li et al. (2012) U

Shakeri et al. (2012)

Soanpet (2012) U

Guignard et al. (2013) UU

Konur and Golias (2013a) UU

Konur and Golias (2013b) UU

Shi et al. (2013)

Spangler (2013)

Zaerpour (2013) U

Cattani et al. (2014) UUU

Ladier (2014) U U

Ladier et al. (2014) U U

Walha et al. (2014) ––– – – –– – –

Heidari et al. (2018)

Ladier et al. (2015) U U

Suh (2015) UU U

Yin et al. (2015)

Zaerpour et al. (2015) U

Amini and Tavakkoli-Moghaddam (2016) U

(continued )

Table III.

Performance measures

Uncertainties

in cross-

docking

operations

Fatthi et al. (2016)

Ladier and Alpan (2016b) U U

H. Zouhaier and Ben Said (2016) U

Motaghedi-Larijani and Aminnayeri (2017) U

Houda Zouhaier and Ben Said (2017a) U

Houda Zouhaier and Ben Said (2017b) U

Motaghedi-Larijani and Aminnayeri (2018) U

Performance measures

Name of authors Door

utilisation

Product

not

loaded

Schedule

length /

makespan

Preemption

costs

Travel

time

Truck

utilisation

Number

touches

Transportation

cost

Operation cost

M.K. Acar (2004) U

Wang and Regan (2008) UU

Yu et al. (2008)

McWilliams (2009) U

Yan and Tang (2009)

Alpan (2010) U

Rodriguez-Velasquez et al. (2010) U

Tang and Yan (2010)

Larbi et al. (2011) U

Sathasivan (2011) U

K. Acar et al. (2012) U

Li et al. (2012) U

Shakeri et al. (2012) U

Soanpet (2012) U

Guignard et al. (2013) U

Konur and Golias (2013a)

Konur and Golias (2013b)

Shi et al. (2013) U

Spangler (2013) U

Zaerpour (2013) U

Cattani et al. (2014) U U

Ladier (2014)

Ladier et al. (2014)

(continued )

Table III.

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Walha et al. (2014) ––– – – –– – –

Heidari et al. (2018) U

Ladier et al. (2015)

Suh (2015) UU

Yin et al. (2015) U

Zaerpour et al. (2015) U

Amini and Tavakkoli-Moghaddam (2016)

Fatthi et al. (2016) U

Ladier and Alpan (2016b)

H. Zouhaier and Ben Said (2016) U

Motaghedi-Larijani and Aminnayeri (2017)

Houda Zouhaier and Ben Said (2017a)

Houda Zouhaier and Ben Said (2017b)

Motaghedi-Larijani and Aminnayeri (2018)

Uncertainties

in cross-

docking

operations

Solution methods

Name of authors Type of mathematical model Exact method Heuristics Meta-heuristics Simulation

M.K. Acar (2004) MIQP Mathematical programming Other dedicated heuristics ––

Wang and Regan (2008) ––Scheduling heuristics U

Yu et al. (2008) ––Other dedicated heuristics Genetic algorithm/local

–

McWilliams (2009) ––Other dedicated heuristics ––

Yan and Tang (2009) Analytical models Mathematical programming ––

Alpan (2010) Polynomial algorithm – Other dedicated heuristics ––

Arnaout et al. (2010) ––Other dedicated heuristics – U

Rodriguez-Velasquez et al.

(2010)

––Other dedicated heuristics – U

Tang and Yan (2010) Analytical models Mathematical programming –– –

Larbi et al. (2011) Polynomial algorithm – Other dedicated heuristics ––

Sathasivan (2011) IP Mathematical programming – Genetic algorithm –

K. Acar et al. (2012) Mixed integer quadratic

programming (MIQP)

Mathematical programming Other dedicated heuristics ––

Li et al. (2012) ––Other dedicated heuristics ––

Shakeri et al. (2012) MIP Mathematical programming Other dedicated heuristics ––

Soanpet (2012) IP Mathematical programming

Guignard et al. (2013) –––Other meta-heuristic –

Konur and Golias (2013a) Bi-level optimisation Mathematical programming – Genetic algorithm –

Konur and Golias (2013b) Bi-objective and bi-level

optimisation

Mathematical programming Other dedicated heuristics Genetic algorithm

Shi et al. (2013) RSM Latin hypercube

sampling

––– U

Spangler (2013) MIP Mathematical programming –– –

Zaerpour (2013) MIP Mathematical programming Other dedicated heuristics ––

Cattani et al. (2014) Markov decision process Other dedicated heuristics ––

Ladier (2014) IP Mathematical programming –– U

Ladier et al. (2014) IP Mathematical programming –– U

Walha et al. (2014) –––– –

Heidari et al. (2018) Bi-objective bi-level

optimisation

Mathematical programming – Mode, NSGA-II, GASH

(continued )

Table IV.

Solution methods

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Solution methods

Name of authors Type of mathematical model Exact method Heuristics Meta-heuristics Simulation

Ladier et al. (2015) IP Mathematical programming –– U

Suh (2015) ––––U

Yin et al. (2015) MIP Mathematical programming Other dedicated heuristics ––

Zaerpour et al. (2015) MIP Mathematical programming Other dedicated heuristics ––

Amini and Tavakkoli-

Moghaddam (2016)

Bi-objective linear

programming

Mathematical programming – NSGA-II, MOSA, MODE –

Fatthi et al. (2016) MIP Mathematical programming –– –

Ladier and Alpan (2016b) Minmax Mathematical programming –– U

H. Zouhaier and Ben Said

(2016)

MIP Mathematical programming Other dedicated heuristics ––

Motaghedi-Larijani and

Aminnayeri (2017)

Queuing model – Other dedicated heuristics ––

Houda Zouhaier and Ben

Said (2017a)

Queuing Model – Other dedicated heuristics ANI –

Houda Zouhaier and Ben

Said (2017b)

IP Mathematical programming –– U

Motaghedi-Larijani and

Aminnayeri (2018)

Queuing model – Other dedicated heuristics ––

Table IV

Uncertainties

in cross-

docking

operations

Arnaout et al. (2010) assumed that cross-docking centres have infinite space, and loading

and unloading delays are negligible, which is unrealistic.

3.1.2 Supply. Uncertainty in supply is one of the disruption factors in operation of

distribution centres. In order for a distribution centre to deal with the negative impact

of supply uncertainty, large amount of inventory is required. This contradicts with the aim

of DCs and cross-dock centres. The other reason for uncertainty in supply is because

retailers tend to request for shorter delivery times increasing the pressure on both

manufacturers and distributors. The inability of cross-docking centres in distributing the

products to manufacturers or retailers on time is caused by the high volume of transactions

along the supply chain (Cattani et al., 2014; Shi et al., 2013). It is vital for distributors to have

proper access to accurate information derived from suppliers. This can help distribution

centres to develop proper plans to manage their resources. The literature in cross-docking

often assumes that the supply is always stable leaving the impact of supply uncertainty on

sequencing and scheduling in cross-dock centres unaddressed.

According to Cattani et al. (2014) , different customers request different products at various

times. Some of these are supplied by distribution centres and cross-docking centres, and

others are provided through direct shipments. Resupply of these orders is sometimes delayed.

Also, uncertainty in supply is one of the reasons for an increase in supply cost. Cattani et al.

(2014) aimed to help the online retailers to reduce the expenses of resupplying and short

delivery. The results show that a cross-docking strategy can help reduce the penalties for

delays in resupplying. This study only considered cross-docking from the demand and supply

viewpoint without considering scheduling and assignment of trucks.

Shi et al. (2013) indicate that in order to control disruptive events such as supply shortage,

three factors should be optimised. In storage space, dwelling time (staying time) of parts

together with the number of pieces stays exceeding the threshold time should be minimised. In

addition, along with the two previous factors, throughput should be maximised. A main

weakness of this study was they considered temporary storage as infinite (Shi et al., 2013).

3.1.3 Arrival time. The literature about uncertainty in cross-docking shows that

managers consider arrival time uncertainty as one of the most critical factors that can have

a negative impact on the planning and scheduling of cross-dock centres (Boysen and

Fliedner, 2010; Ladier and Alpan, 2016a). In cross-docking literature, most of the researchers

assumed that arrival time is constant and that all trucks are available at the time of zero,

which is not realistic. Receiving and shipping trucks in the real environment have a release

and due time which should be monitored carefully to reduce the overall cost associated with

earliness and tardiness. Boysen and Fliedner (2010) identified several factors such as traffic

and engine failures that can delay the arrival time of trucks.

Uncertainty components

in Cross-dock operations

External Components

Internal Components

Demand

Supply

Arrival time

Availability of

Trucks

Processing Time

Available Resources

Departure Time

Figure 4.

Uncertainty

components in cross-

docking operations

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Monitoring the arrival time of trucks and scheduling both receiving and shipping trucks

can improve the efficiency of transhipment. The operation of cross-docking centres should

be dynamic and practical. Although static environment can be a starting point to explore a

research area, in order to improve the cross-docking operation in functional form, dynamic

situations should be considered in research. One of the first studies in the cross-docking

dynamic was presented by Konur and Golias (2013a). The authors pointed out that arrival

time of trucks needs careful observation and using the prediction method is not a proper

way to reduce these uncertainties. Online scheduling or scheduling on a rolling planning

horizon can help practitioners obtain better information on the arrival time of trucks.

However, a large amount of data and uncertainty in cross-docking operations can make the

scheduling process more complicated (Boysen and Fliedner, 2010; Konur and Golias, 2013a;

Van Belle et al., 2012).

Konur and Golias (2013a) considered only the inbound side of a cross-dock centre to

minimise the total waiting time for trucks with consideration of risk averse. The model

provided four perspectives. The deterministic perspective disrespects the possible earliness

and tardiness while pessimistic perspective is a risk averse method and uses the worst

probability distribution function on arrival time. The optimistic perspective works on the best

possible distribution for arrival time and hybrid cases. Konur and Golias (2013b) also

conducted a study to minimise costs associated with the arrival time of trucks on the inbound

side of cross-docking centres. This method was compared with a first-come-first-served policy.

In this study, the probability distribution of the arrival time of trucks was not considered, and

temporary storage space was zero.

In continue of research provided by Konur and Golias (2013a), Heidari et al. (2018)

performed a bi-objective bi-level optimisation to schedule and allocate trucks. Different from

Konur and Golias’s (2013a) study, Heidari et al. (2018) considered the outbound side as well.

The arrival time of trucks was uncertain, but a time window was defined for truck arrival.

To improve usability, Ladier and Alpan (2016b) developed a model to address the frequent

disruptions in the scheduling of trucks in cross-docking centres. However, a weakness of

their study is that the limits of the temporary storage are not considered.

In order to reduce the long waiting times at the gates and yards, management of arrival

time is vital. H. Zouhaier and Ben Said (2016) explained that reducing the waiting times

caused by delays in arrival time of trucks can increase efficiency. To reduce the negative

impact of uncertainties, one of the practical measures is a truck appointment system.

This method can monitor the planning of arrival times by assigning an appointed slot to

each truck, which, in turn, minimise truck deviation time. Although H. Zouhaier and Ben

Said (2016) considered the limitation of resources and doors, the limitations of temporary

storage and yard space were not considered.

The above-discussed studies considered the uncertainties in arrival time of receiving

trucks. The arrival time of shipping trucks is equally important can impact cross-docking

operations. The first study about uncertainties in the arrival time of shipping trucks was

presented by Zaerpour (2013) and Zaerpour et al. (2015). The authors argued that when trucks

arrive outside the time window, the risk of reshuffling with shared storage will increase.

Reshuffling time in this system can be increased because of improper assignment. First come,

first serve (FCFS) can increase the possibility of reshuffling. Accordingly, uncertainties in

truck arrival times can decrease the accuracy of defined time windows which leads to

reshuffling and increase in cross-docking operations costs. Reducing the cost associated with

reshuffling, arrival time of trucks needs a proper time window for the arrival time of shipping

trucks. It is also interesting to consider the probability of facilities breakdown.

Queuing systems can help manage the waiting time of trucks in cross-docking centres.

To improve the system, Motaghedi-Larijani and Aminnayeri (2017) proposed a model to

Uncertainties

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docking

operations

examine the arrival time of single outbound trucks as random with uniform distribution.

A queuing model was developed based on a situation where the expected waiting time of

customers is considered. The aim of this paper was minimising the total admission and

waiting time cost. However, the research only used one door and one side of the arrival time,

which limited the applicability of the model.

By considering the arrival time of truck as a deterministic factor and a certain parameter,

literature about cross-docking is far from the reality in the industry. Arrival time of trucks

can be the starting source of uncertainty in cross-docking operations. Accordingly,

Motaghedi-Larijani and Aminnayeri (2018) considered arrival time of trucks following beta

probability distribution and applied queuing model in this problem. They calculated the

waiting times of customers based on the delay that happened in arrival time.

3.1.4 Availability of trucks. The availability of trucks which is related to the external

suppliers can impact planning and scheduling of resources. When proper resources are not

available it impacts all products scheduled for delivery to customers. This factor includes

both the inbound and outbound sides of the cross-docking centre operations. In addition,

trucks can fail during the delivery of products to cross-docking centres or retailers. If the

availability of trucks is disrupted, there is a need for reallocation of all orders and resources

to fulfil the scheduled delivery.

Amini and Tavakkoli-Moghaddam (2016) developed a model that considered truck

breakdown during service time. The breakdown of trucks followed a Poisson distribution. The

objective of this paper was minimising the total weighted completion time or tardiness of

outbound trucks. This paper only considered the outbound process. All of the trucks were

available at the time of zero, which is impractical, and the temporary storage capacity is infinite.

3.2 Internal uncertainty components

3.2.1 Processing time. Processing of inbound and outbound trucks is prone to uncertainty.

Delay in fright handling can prolong the distribution process in the whole system. There

are several factors that can impact the processing time of cross-dock centres. For instance,

loading and unloading of trucks can be impacted by skills of the workforce in terms

of the time that people need for doing the same job. This process can disrupt the flow of

products in cross-dock centres. The loading and unloading and transferring time for

different types of products is also different that can influence on planning. Accordingly,

Wang and Regan (2008) suggested that using real-time information to schedule the

unloading of receiving trucks can decrease the total freight transfer time. Therefore, they

focussed on the effect of new receiving trucks on overall transhipment time. One weakness

of this study is that it did not consider both inbound and outbound sides. It is important

for cross-dock operations from a practical viewpoint to focus on unloading, loading and

waiting time of trucks. McWilliams (2009) conducted a study into the processing time

inside cross-docking centres to minimise total transfer time. A dynamic load-balancing

algorithm was designed. The process of unloading trucks and assignment of trucks to

doors was updated after unloading each truck. The study assumed that all shipping

and receiving trucks were available at the time of zero, which is not realistic. In addition,

the priority of each truck was not considered.

According to Sathasivan (2011), unloading and loading of trucks can be overestimated or

underestimated. Both can impact the optimal solution. Therefore, it is pivotal to consider the

uncertainty in unloading time of trucks. As a result, stochastic and robust optimisation

approaches were implemented. Sathasivan (2011) minimised weighted completion time to

determine the optimal schedule for unloading receiving trucks. The study assumed that

trucks were available at the time of zero and that the cross-docking centre had only one

receiving and one shipping truck, which was far from a real environment.

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3.2.2 Available resources. Material handling is the core of operations and includes the

most expensive operations in cross-docking. Unloading, transferring, consolidating, splitting

of orders and loading during the operation of cross-docking rely on labours and available

resources. Therefore, this costly operation needs to be carefully monitored to reduce cost and

increase utilisation. Shakeri et al. (2012) developed a model to address the delays caused by

forklift breakdown inside the cross-dock centre. The model may be improved through

assessing the probability of forklift breakdowns. From a different perspective, Soanpet (2012)

studied the effects of capacity uncertainty on the location of cross-dock centres to minimise

the total routing cost. Capacity can impact on the number of products that can be handled

in the centre. However, their study did not consider limited temporary storage and truck

arrival time. Zouhaier and Ben Said (2017a) argued that increasing the available resources can

increase the performance of cross-dock centre and decrease the completion time at the same

time. They presented a multi-agent-based truck scheduling model to coordinate the arrival

and gate process and the availability of human resources inside the cross-docking centre.

They considered available human resources with different abilities, but did not consider

temporary storage inside the cross-docking centre.

3.2.3 Departure time. The departure time of trucks is one of the uncertainty

components that can be resulted from internal and external sources. It can absorb other

uncertainties such as arrival time and service time. This situation becomes more

challenging when the trucks on the inbound and outbound sides have a deadline.

Assignment of trucks to doors is one of the critical d ecisions in cross-docking operations.

With restricted truck departure time, M.K. Acar (2004) studied dock-door assignment

to minimise the distance travelled inside the cross-docking centre to deliver products to

shipping doors. The authors assumed that shippi ng trucks were always available at

shipping docks and temporary storage was not considered, which is not realistic

(Acar et al.,2012). Literature about departure uncertainty is limited and requires further

attention. Studies in the area of flight routing and schedulin g with departure uncertainties

in air traffic management may be a good starting point for developing solutions in

cross-dock ing operations.

3.3 Multiple uncertainty components

Multiple uncertainties can exist during cross-docking operations. For the purpose of

discussion, research that considered more than one uncertainty components is grouped into

this category. Inaccuracy in arrival time and content in trucks can lead to uncertainty in

processing time. Yu et al. (2008) presented an online method to solve dock-door assignment

problems. The authors considered uncertainties in arrival time and the content of trucks

and supply to minimise processing time using the FCFS policy. According to the results, this

method can improve resource planning by 20 per cent. Temporary storage and

unavailability of resources were not considered in this study.

Following the same concept, Alpan (2010) presented a problem for the scheduling of

cross-docking operations under uncertainties of inbound truck arrival time. The model

aimed to minimise the total cost by using the best sequence of shipping trucks. They

assigned the products to the shipping trucks following the first-in-first-out policy, which is

the same as FCFS. The model, however, only considered one receiving door and one

shipping door with infinite temporary storage space. The results illustrated that when no

information was available on the arrival time of trucks, the total cost exhibited a significant

increase (Larbi et al., 2011).

Manual rules used to manage cross-dock operations give sub-optimal result, which

according to Li et al. (2012) is inappropriate. Consequently, they developed an online

scheduling and planning tool which reached optimal solutions for planning inbound trucks,

Uncertainties

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docking

operations

the allocation of trucks to docks and the priority of jobs for forklifts to maximise the output.

Research attempts to optimise cross-docking operations in three layers: planning,

scheduling and coordination. The aim of the planning layer is minimising processing time,

which consists of sequencing and allocation of containers. Processing time is the first

uncertainty component, the late arrival time of trucks is the second uncertainty and the third

one is resource management in a dynamic environment. To integrate the three layers, an

event-based integrated optimisation model was developed by Ladier et al. (2014) with

discrete event simulation. They aimed to evaluate the robustness of the IP model. In their

study, arrival time, unloading time and processing time were uncertain. They used FlexSim

software to develop the simulation model. In order to model unloading and to transfer time,

they used triangular distribution and, for arrival time, exponential distribution. Temporary

storage space was infinite. Resources inside the cross-docking centre were limited. The

results showed that the model had reasonable robustness against uncertainties. To improve

the previous model, Ladier et al. (2015) conducted further research and they considered

uncertainties in available resources and tasks as well.

Collaborative computing using a poll of heuristics can be used to find solution. Yin et al.

(2015) researched collaborative vehicle routing and scheduling in cross-docking centres

under uncertainties to minimise the makespan of cross-docking centres along the horizon.

Three types of uncertainties were considered including vehicle failure, demand and arrival

time. In order to solve the problem, a hyper-heuristic method was used which included

collaborative computing and service rules. In this paper, the temporary storage and the

process inside the cross-docking centre were not considered. Two-thirds of the operations in

cross-dock centres are focussed on scheduling and assignment. Proper coordination of

inbound and outbound activities can facilitate the smooth operation inside of the cross-dock

centres. Fatthi et al. (2016) presented a study about the scheduling and assignment of trucks

in an inbound phase to minimise the completion time on the inbound side. This model was

based on real-time information with the number of receiving trucks, the content of trucks,

arrival time of trucks and unloading time of trucks were dynamic.

4. Conclusions and future research directions

This literature review focusses on cross-docking operations under uncertainty. The selected

studies addressed various issues in cross-docking at tactical and operational levels. Since

the focus is on optimising operations with existing infrastructure and facilities, studies on

strategic-level problems were excluded. The framework presented in Figure 4 illustrates the

composition of uncertainties in cross-docking operations. Based on the results derived from

reviewing the literature, several gaps have been identified.

First, according to Boysen and Fliedner (2010), truck arrival time is often uncertain.

The causes of this uncertainty include weather condition, traffic condition and truck failure.

While several authors considered truck arrival time as uncertain, all these studies are far

from applicable to the practical environment. A main limitation is yard management and the

effects of uncertain arrival time and limited yard storage on cross-docking operations when

there are deadlines for receiving and shipping trucks.

Second, the availability of resources significantly influences cross-docking operations.

Forklifts, conveyors and labour are the most common resources for unloading, transferring

and loading the products. In the literature, some studies considered limited resources.

However, the assumptions used in developing the model are unrealistic and cannot be used

for practical solutions (Amini and Tavakkoli-Moghaddam, 2016; Fatthi et al., 2016; Ladier,

2014; Li et al., 2012; Shi et al., 2013; Soanpet, 2012; Zouhaier and Ben Said, 2017a, b).

If temporary storage has unlimited capacity, the impact of resources limitation is not visible

as all the extra products have to be moved to the temporary storage. If the storage capacity

is not enough, the operations of the cross-docking centre will be disrupted. Therefore,

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models combining the limited temporary storage with limited resources capacity may

provide meaningful solutions to optimise cross-docking operations.

Finally, the departure time of trucks relies on arrival time, truck processing time and

availability of resources inside the cross-docking centres. Previously, literature is limited to

arrival time and due date for shipping trucks (Acar et al., 2012; Acar, 2004; Fatthi et al., 2016;

Ladier, 2014; Ladier and Alpan, 2016b; Ladier et al., 2014 ; Walha et al., 2014). Future research

can focus on developing integrated solutions through several steps. In the first phase,

the process of optimising departure time and all related activities should be considered in

the model. In the second phase, the impact of limited yard storage and temporary storage

should be addressed. Finally, the effects of deadline on the overall performance of cross-

docking centres and the capacity of trucks occupied by loaded products should be examined

because in some cases, with deadlines on shipping trucks, the capacity which can be used

may be less. Limited yard and temporary storage can increase the waiting time of shipping

trucks and therefore increasing carbon emission. This is another gap that should be

addressed in future research. The result of this review shows that the combination of

uncertain factors and the effect of physical characteristics of cross-docking centres is one of

the leading research areas which deserve more attention.

References

Acar, K., Yalcin, A. and Yankov, D. (2012), “Robust door assignment in less-than-truckload terminals”,

Computers & Industrial Engineering, Vol. 63 No. 4, pp. 729-738, available at: https://doi.org/10.

1016/j.cie.2012.04.008.

Acar, M.K. (2004), “Robust dock assignments at less-than-truckload terminals”, master’s thesis,

University of South Florida.

Agustina, D., Lee, C.K.M. and Piplani, R. (2010), “A review: mathematical modles for cross docking

planning”, International Journal of Engineering Business Management, Vol. 2, p. 13, doi: 10.5772/9717.

Alpan, G. (2010), “Modeling and analysis methods to improve industrial performance”, Institut

polytechnique de Grenoble, Grenoble.

Amini, A. and Tavakkoli-Moghaddam, R. (2016), “A bi-objective truck scheduling problem in a

cross-docking center with probability of breakdown for trucks”, Computers & Industrial

Engineering, Vol. 96, pp. 180-191, available at: http://doi.org/10.1016/j.cie.2016.03.023.

Apte, U.M. and Viswanathan, S. (2000), “Effective cross docking for improving distribution

efficiencies”, International Journal of Logistics, Vol. 3 No. 3, pp. 291-302.

Ardakani, A., Fei, J. and Beldar, P. (2020), “Truck-to-door sequencing in multi-door cross-docking

system with dock repeat truck holding pattern”, International Journal of Industrial Engineering

Computations, Vol. 11 No. 2, pp. 201-220.

Arnaout, G., Rodriguez-Velasquez, E., Rabadi, G. and Musa, R. (2010), “Modeling cross-docking

operations using discrete event simulation”, paper presented at the Proceedings of the 6th

International Workshop on Enterprise & Organizational Modeling and Simulation,

Hammamet, 7-8 June.

Boysen,N.andFliedner,M.(2010),“Cross dock scheduling: classification, literature review and research

agenda”, Omega, Vol. 38 No. 6, pp. 413-422, available at: https://doi.org/10.1016/j.omega.2009.10.008.

Buijs, P., Vis, I.F.A. and Carlo, H.J. (2014), “Synchronization in cross-docking networks: a research

classification and framework”, European Journal of Operational Research, Vol. 239 No. 3,

pp. 593-608, doi: 10.1016/j.ejor.2014.03.012.

Cattani, K.D., Souza, G.C. and Ye, S. (2014), “Shelf loathing: cross docking at an online retailer”,

Production and Operations Management, Vol. 23 No. 5, pp. 893-906, doi: 10.1111/poms.12077 .

Denyer, D. and Tranfield, D. (2009),

“Producing a systematic review”, in Buchanan, D.A. and Bryman, A.

(Eds),TheSageHandbookofOrganizational Research Methods, Sage Publications Ltd, pp. 671-689.

Uncertainties

in cross-

docking

operations

Dulebenets, M.A. (2019), “A delayed start parallel evolutionary algorithm for just-in-time truck

scheduling at a cross-docking facility”, International Journal of Production Economics, Vol. 212,

pp. 236-258, available at: https://doi.org/10.1016/j.ijpe.2019.02.017.

Fatthi, W., Shuib, A. and Dom, R.M. (2016), “A mixed integer programming model for solving real-time

truck-to-door assignment and scheduling problem at cross docking warehouse”, Journal

of Industrial and Managem ent Optimization, Vol. 12 No. 2, pp. 431-447, doi: 10.3934/

jimo.2016.12.431.

Gong, Y. and de Koster, R.B.M. (2011), “A review on stochastic models and analysis of warehouse

operations”, Logistics Research, Vol. 3 No. 4, pp. 191-205, doi: 10.1007/s12159-011-0057-6.

Guignard, M., Hahn, P.M. and Zhang, H. (2013), “Practical cross-docking optimization ”, TRISTAN

VIII, San Pedro de Atacama, pp. 4-7.

Heidari, F., Zegordi, S.H. and Tavakkoli-Moghaddam, R. (2018), “Modeling truck scheduling problem at

a cross-dock facility through a bi-objective bi-level optimization approach”, Journal of Intelligent

Manufacturing, Vol. 29, p. 1155, available at: https://doi.org/10.1007/s10845-015-1160-3.

Ho, C.-J. (1989), “Evaluating the impact of operating environments on MRP system nervousness”,

The International Journal of Production Research, Vol. 27 No. 7, pp. 1115-1135.

Kenne, J.-P., Dejax, P. and Gharbi, A. (2012), “Production planning of a hybrid manufacturing–

remanufacturing system under uncertainty within a closed-loop supply chain”, International

Journal of Production Economics, Vol. 135 No. 1, pp. 81-93.

Konur, D. and Golias, M.M. (2013a), “Analysis of different approaches to cross-dock truck scheduling

with truck arrival time uncertainty”, Computers & Industrial Engineering, Vol. 65 No. 4,

pp. 663-672, doi: 10.1016/j.cie.2013.05.009.

Konur, D. and Golias, M.M. (2013b), “Cost-stable truck scheduling at a cross-dock facility with

unknown truck arrivals: a meta-heuristic approach”, Transportation Research Part E-Logistics

and Transportation Review, Vol. 49 No. 1, pp. 71-91, doi: 10.1016/j.tre.2012.06.007.

Krajewski, L.J., Ritzman, L.P. and Malhotra, M.K. (1999), Operations Management, Vol. 36,

Addison-Wesley, Singapore.

Ladier, A.-L. (2014), “Scheduling cross-docking operations: integration of operational uncertainties and

resource capacities”, Universit



e Grenoble Alpes, Grenoble.

Ladier, A.-L. and Alpan, G. (2016a), “Cross-docking operations: current research versus industry

practice”, Omega, Vol. 62, pp. 145-162, available at: http://doi.org/10.1016/j.omega.2015.09.006.

Ladier, A.-L. and Alpan, G. (2016b), “Robust cross-dock scheduling with time windows”, Computers &

Industrial Engineering, Vol. 99, pp. 16-28, available at: http://doi.org/10.1016/j.cie.2016.07.003.

Ladier, A.-L., Alpan, G. and Greenwood, A. (2014), “Robustness evaluation of an IP-based

cross-docking schedule using discrete-event simulation”, Industrial and Systems Engineering

Research Conference, Montr



eal, June, p. I211.

Ladier, A.-L., Greenwood, A. and Alpan, G. (2015), “Modeling issues when using simulation to test the

performance of mathematical programming models under stochastic conditions”, 29th IEEE

European Simulation and Modelling Conference (ESM 2015), Leicester, October, pp. 117-121.

Larbi, R., Alpan, G., Baptiste, P. and Penz, B. (2011), “Scheduling cross docking operations under full,

partial and no information on inbound arrivals”, Computers & Operations Research, Vol. 38

No. 6, pp. 889-900, available at:, doi: https://doi.org/10.1016/j.cor.2010.10.003.

Lee, D.-H., Dong, M. and Bian, W. (2010), “The design of sustainable logistics network under

uncertainty”, International Journal of Production Economics , Vol. 128 No. 1, pp. 159-166.

Li, Z., Sim, C.H., He, W. and Chen, C.C. (2012), “A solution for cross-docking operations

planning, scheduling and coordination”, Journal of Service Science and Management, Vol. 5

No. 2, p. 111.

McWilliams, D.L. (2009), “A dynamic load-balancing scheme for the parcel hub-scheduling problem”,

Computers & Industrial Engineering, Vol. 57 No. 3, pp. 958-962, available at: https://doi.org/10.

1016/j.cie.2009.03.013.

MSCRA

2,1

Motaghedi-Larijani, A. and Aminnayeri, M. (2017), “Optimizing the admission time of outbound trucks

entering a cross-dock with uniform arrival time by considering a queuing model”, Engineering

Optimization, Vol. 49 No. 3, pp. 466-480, doi: 10.1080/0305215X.2016.1206414.

Motaghedi-Larijani, A. and Aminnayeri, M. (2018), “Optimizing the number of outbound doors in the

crossdock based on a new queuing system with the assumption of beta arrival time”, Scientia

Iranica, Vol. 25 No. 4, pp. 2282-2296, doi: 10.24200/sci.2017.4452.

Nassief, W., Contreras, I. and As’ad, R. (2016), “A mixed-integer programming formulation and

Lagrangean relaxation for the cross-dock door assignment problem”, International Journal of

Production Research, Vol. 54 No. 2, pp. 494-508, doi: 10.1080/00207543.2014.1003664.

Rodriguez-Velasquez, E., Arnaout, G., Rabadi, G. and Musa, R. (2010), “Modeling cross-dock networks

with time constraints using simulation”, 2010 IEEE Systems and Information Engineering

Design Symposium, Charlottesville, VA, pp. 52-56, doi: 10.1109/SIEDS.2010.5469678.

Sathasivan, K. (2011), “Optimizing cross-dock operations under uncertainty”, PhD dissertation,

The University of Texas at Austin.

Seuring, S. and M

€

uller, M. (2008), “From a literature review to a conceptual framework for sustainable

supply chain management”, Journal of Cleaner Production, Vol. 16 No. 15, pp. 1699-1710.

Shakeri, M., Low, M.Y.H., Turner, S.J. and Lee, E.W. (2012), “A robust two-phase heuristic algorithm for

the truck scheduling problem in a resource-constrained crossdock”, Computers & Operations

Research, Vol. 39 No. 11, pp. 2564-2577, available at: https://doi.org/10.1016/j.cor.2012.01.002.

Shi, W., Liu, Z.X., Shang, J. and Cui, Y.J. (2013), “Multi-criteria robust design of a JIT-based cross-

docking distribution center for an auto parts supply chain”, European Journal of Operational

Research, Vol. 229 No. 3, pp. 695-706, doi: 10.1016/j.ejor.2013.03.013 .

Soanpet, A. (2012), “Optimization models for locating cross-docks under capacity uncertainty ”,

Graduate theses, dissertations, and Problem Reports, p. 582, available at: https://

researchrepository.wvu.edu/etd/582.

Spangler, S. (2013), “Robust cross-dock location model accounting for demand uncertainty”, Graduate theses,

dissertations, and Problem Reports, p. 398, available at: https://researchrepository.wvu.edu/etd/398.

Suh, E.S. (2015),

“Cross-docking assessment and optimization using multi-agent co-simulation: a case

study”, Flexible Services and Manufacturing Journal, Vol. 27 No. 1, pp. 115-133, doi: 10.1007/

s10696-014-9201-3.

Tang, S.-L. and Yan, H. (2010), “Pre-distribution vs post-distribution for cross-docking with transshipments”,

Omega,Vol.38Nos3–4, pp. 192-202, available at: http://doi.org/10.1016/j.omega.2009.09.001.

Tranfield, D., Denyer, D. and Smart, P. (2003), “Towards a methodology for developing

evidence-informed management knowledge by means of systematic review”, British Journal

of Management, Vol. 14 No. 3, pp. 207-222.

Van Belle, J., Valckenaers, P. and Cattrysse, D. (2012), “Cross-docking: State of the art”, Omega, Vol. 40

No. 6, pp. 827-846, available at: http://doi.org/10.1016/j.omega.2012.01.005.

Walha, F., Chaabane, S., Bekrar, A. and Loukil, T., IEEE (2014), “The cross docking under uncertainty:

state of the art”,2014 International Conference on Advanced Logistics & Transport, pp. 330-335.

Wang, J.-F. and Regan, A. (2008), “Real-time trailer scheduling for crossdock operations”,

Transportation Journal, Vol. 47 No. 2, pp. 5-20.

Yan, H. and Tang, S.-l. (2009), “Pre-distribution and post-distribution cross-docking operations”,

Transportation Research Part E: Logistics and Transportation Review, Vol. 45 No. 6, pp. 843-859,

available at: http://doi.org/10.1016/j.tre.2009.05.005.

Yin, P.Y., Chuang, Y.L., Lyu, S.R. and Chen, C.Y., IEEE (2015), “Collaborative vehicle routing and

scheduling with cross-docks under uncertainty”, 2015 IEEE Conference on Collaboration and

Internet Computing, pp. 106-112, doi: 10.1109/cic.2015.19.

Yu, V.F., Sharma, D. and Murty, K.G. (2008), “Door allocations to origins and destinations at less-than-

truckload trucking terminals”, Journal of Industrial and Systems Engineering, Vol. 2

No. 1, pp. 1-15.

Uncertainties

in cross-

docking

operations

Zaerpour, N. (2013), “Efficient management of compact storage systems” (No. EPS-2013-276-LIS),

ERIM PhD Series Research in Management, Erasmus Research Institute of Management,

22 February, available at: http://hdl.handle.net/1765/38766.

Zaerpour, N., Yu, Y. and de Koster, R.B.M. (2015), “Storing fresh produce for fast retrieval in an

automated compact cross-dock system”, Production and Operations Management, Vol. 24 No. 8,

pp. 1266-1284, doi: 10.1111/poms.12321.

Zouhaier, H. and Ben Said, L. (2016), “An application oriented multi-agent based approach to dynamic

truck scheduling at cross-dock”, paper presented at the 2016 17th International Conference on

Parallel and Distributed Computing, Applications and Technologies (PDCAT), Guangzhou,

16–18 December.

Zouhaier, H. and Ben Said, L. (2017a), “Multi-agent based truck scheduling using ant colony intelligence

in a cross-docking platform”, in Madureira, A.M., Abraham, A., Gamboa, D. and Novais, P. (Eds),

Intelligent Systems Design and Applications: 16th International Conference on Intelligent Systems

Design and Applications (ISDA 2016) held in Porto, Portugal, 16-18 December, 2016, Springer

International Publishing, Cham, pp. 457-466.

Zouhaier, H. and Said, L.B. (2017b), “Robust scheduling of truck arrivals at a cross-docking

platform”, paper presented at the Proceedings of the Australasian Computer Science Week

Multiconference, Geelong, Geelong, 30 January-3 February.

Corresponding author

Allahyar (Arsalan) Ardakani can be contacted at: [email protected]

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