Systematic literature review on collaborative sustainable transportation: overview, analysis and perspectives

Abstract

In the last few years, competitiveness, problems of globalization and concerns about sustainability require new approaches and models for the planning of transport networks. Horizontal logistics cooperation has been considered an emerging and innovative approach in the design and management of sustainable supply chains. This approach is based on the sharing of resources between actors at the same level in different supply chains. This study provides a Systematic Literature Review (SLR) about sustainability and collaboration in the freight transport sector. It aims to analyze the existing literature in order to reveal the studies already conducted and to identify gaps and opportunities for future research. A total of 89 articles have been published between 2010 and 2020 which have been examined. The results show that the integration of these three dimensions of sustainable development in the field of collaborative network optimization, especially the social considerations have been little studied. In addition, the analysis shows that most of the authors have focused their research on transport optimization at the operational level, with few works on the problem of designing and managing the integrated supply chain.

Full text

Systematic literature review on collaborative sustainable transportation:
overview, analysis and perspectives
Aymen Aloui
a,
⇑
, Nadia Hamani
a
, Ridha Derrouiche
b
, Laurent Delahoche
a
a
University of Picardie Jules Verne, Laboratory of Innovative Technology (LTI, EA 3899), 80025 Amiens, France
b
EM Strasbourg Business School, Université de Strasbourg, HuManiS (UR 7308), 67000 Strasbourg, France
ARTICLE INFO
Keywords:
Horizontal collaboration
Sustainability
Systematic literature review
Freight transport
Transport planning
Logistics collaboration
ABSTRACT
In the last few years, competitiveness, problems of globalization and concerns about sustainability require new
approaches and models for the planning of transport networks. Horizontal logistics cooperation has been con-
sidered an emerging and innovative approach in the design and management of sustainable supply chains. This
approach is based on the sharing of resources between actors at the same level in different supply chains. This
study provides a Systematic Literature Review (SLR) about sustainability and collaboration in the freight trans-
port sector. It aims to analyze the existing literature in order to reveal the studies already conducted and to
identify gaps and opportunities for future research. A total of 89 articles have been published between 2010
and 2020 which have been examined. The results show that the integration of these three dimensions of sus-
tainable development in the field of collaborative network optimization, especially the social considerations
have been little studied. In addition, the analysis shows that most of the authors have focused their research on
transport optimization at the operational level, with few works on the problem of designing and managing the
integrated supply chain.
Contents
1. Introduction ..................................................................................................... 2
2. Research methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.1. Question formulation and keywords definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.2. Inclusion/exclusion criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.3. Datasearch................................................................................................. 3
2.4. Selection of the most relevant papers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.5. Analysis, synthesis and results reporting: Classification methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Resultsandfindings................................................................................................ 3
3.1. Publications per year . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.2. Publications by journal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
4. Categorization analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
4.1. Decisionanalysis............................................................................................. 4
4.1.1. Strategic decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
4.1.2. Tactical decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4.1.3. Operational decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.2. Sustainability analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2.1. Economic measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2.2. Environmental measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2.3. Social measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.3. Researchmethods.................................... ........................................................ 11
https://doi.org/10.1016/j.trip.2020.100291
Received 2 November 2020; Revised 15 December 2020; Accepted 20 December 2020
Available online 5 January 2021
2590-1982/© 2020 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
⇑
Corresponding author.
E-mail address: aymen.aloui@u-picardie.fr (A. Aloui).
Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
Contents lists available at ScienceDirect
Transportation Research Interdisciplinary Perspectives
journal homepage: www.elsevier.com/locate/trip

5. Research trends and gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
6. Future research orientations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
7. Conclusion .......... .............................................................. .............................. 13
References ........ ............................................................. ................................. 13
1. Introduction
Today's business world is influenced by the globalization of mar-
kets, the rapidly changing requirements of customers, and concerns
about sustainability. Consequently, companies are constantly search-
ing for new strategies to improve their logistics performance and
ensure their competitiveness in today's market (Aloui et al., 2020).
Especially on their goods distribution network, which represents a
major component in all supply chains (Muñoz‐Villamizar et al.,
2019a). In this context, logistics collaboration is considered as one
of the most effective mechanisms for companies who wish to increase
their logistics efficiency and to achieve their goals of economic, envi-
ronmental and social sustainability (Ben Jouida et al., 2017;
Vanovermeire and Sörensen, 2014). This approach focuses principally
on the coordination and integration of processes between supply chain
actors (Stellingwerf et al. 2019). It can be horizontal between a group
of stakeholders from different supply chains acting at the same level,
vertical by considering hierarchical relationships in a supply chain
or integrated by combining horizontal and vertical synergies, known
as lateral collaboration (Soysal et al., 2018).
In recent years, researchers and practitioners have increasingly
focused on horizontal collaboration, through the multiple benefits that
it generates by sharing resources (warehouses, distribution centers,
vehicles, etc.) between actors in different logistics networks (Pan
et al., 2019). This strategy has been involved at different levels of plan-
ning of transportation networks. At the strategic level, the use of the
horizontal collaboration concepts are focused on designing common
logistics networks in order to consolidate flows and achieve economies
of scale (Ballot and Fontane, 2010; Nataraj et al., 2019; Pan et al.,
2013; Verdonck et al., 2016). At a tactical and operational level, route
optimization and organization are the most discussed issues in the lit-
erature (Buijs et Wortmann, 2014; Defryn et al., 2019; Montoya‐Torres
et al., 2016; Quintero‐Araujo et al., 2016; Wang et al., 2020; 2017a;
2018a). Most studies, from the strategic to the operational level, focus
on quantifying potential economic savings through horizontal collabo-
ration. However, as mentioned above, the logistics networks' effective-
ness must be evaluated with the three sustainability aspects.
The literature and industry practices in this topic increasingly empha-
size that horizontal collaboration is an effective means of balancing the
three dimensions of sustainability and increasing transport efficiency.
However, scientific research on horizontal collaboration and the integra-
tion of the Triple‐Bottom‐Line (3TBL) dimension is still at an undevel-
oped level, it is therefore necessary to conduct a detailed analysis of
the state of knowledge. This study has identified four literature reviews
that differ from the objectives of this research. In fact, Verdonck et al.
(2013) and Guajardo and Rönnqvist (2016) reviewed methods of sharing
the gains generated by horizontal collaboration in freight transportation.
At the operational level, Gansterer and Hartl (2018) examined optimiza-
tion models for collaborative transport planning. Pan et al. (2019) pro-
vided a comprehensive review of existing horizontal collaboration
solutions and implementation issues in freight transportation. To the best
of our knowledge, no study has been conducted on horizontal collabora-
tion and sustainability in the freight transportation sector. To fill this gap,
this article addresses the horizontal collaboration and sustainability in
freight distribution. In particular, this research aims to answer the follow-
ing two questions: Firstly, what is the existing work and what are the cur-
rent research trends on horizontal collaboration and sustainability in
freight transportation? Secondly, what are the existing research gaps
and what are the potential contributions for future work?
The rest of this paper is organized as followed. The second section
presents the methodology of the review. A descriptive analysis of the
selected articles is described in the third section. The fourth section
categorizes and discusses in detail the existing work according to cat-
egorization criteria. Research trends and gaps are discussed in the fifth
section. The sixth section aims to identify some directions for future
research. Finally, the seventh section is the conclusion.
2. Research methodology
To answer the research questions identified in the previous section,
an SLR is conducted in order to provide a detailed and comprehensive
analysis of the state of the art. In this study, the guidelines proposed by
Durach et al. (2017) to conduct an SLR in supply chain management
have been used. The methodology of this study consists of six steps:
(i) Formulation of the search question and choice of keywords,
(ii) Definition of inclusion and extrusion criteria,
(iii) Search in databases,
(iv) Papers selection, discussion and analysis of the results, and
(v) Reporting of the results.
Fig. 1 summarizes the methodology used in this study.
2.1. Question formulation and keywords definition
The first step in an SLR is to clearly define one or more research
questions. In this sense, the main topic of our research is to examine
the existing state of the art dealing with horizontal collaboration in
freight transport and to study how aspects of sustainability are
included in strategic, tactical and operational decisions. Also, based
on the research purpose, it is necessary to define a list of keywords
in order to localize and limit this study. Accordingly, two categories
of keywords are proposed. The main keywords in the first category
are: “collaborative transport”,“cooperative transport”,“and ”coalition
transport“. In the second category, we have identified the following
words: ”sustainable development“,”sustainability“,”social“,”ecological“,
”green“”economic“and ”environmental“.
Fig. 1. Six-step Research Methodology.
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
2

2.2. Inclusion/exclusion criteria
In addition to the keywords defined previously in the first step, a
list of inclusion and exclusion criteria was established to limit the lit-
erature search and to select the articles that we would focus on. The set
of criteria developed in this search is presented in Table 1.
2.3. Data search
In this step, we have defined three sub‐steps: The first concerns the
selection of databases. Consequently, we have selected the following
source databases: Google Scholar,Web of Science,Scopus,Taylors &
Francis,Springer,Science Direct and Wiley Online Library. We then com-
menced a data search of the various databases identified by combining
the keywords defined in the first step, for example, “horizontal collab-
oration”in combination with “sustainability”. In the literature search
phase, we identified a first set of articles based on the relevance of
the title according to the context of the study. A total of 156 articles
were identified in this phase.
2.4. Selection of the most relevant papers
To reduce the size of the database constructed in the previous step
and to review a reasonable number of studies, we applied a filter using
the inclusion/exclusion criteria. This step aims to identify the most rele-
vant studies to focus on and eliminate irrelevant studies from our study.
During the selection phase, we read the full text of each article and
reviewed the list of references for each. In addition, we identified the
main authors contributing to freight transportation optimization in order
to perform a second search based on their names. After that, we added
the papers that were not initially found in our database. Finally, a set
of 89 papers were selected for review and analysis as relevant studies.
2.5. Analysis, synthesis and results reporting: Classification methodology
Once the relevant papers have been identified, steps (5) and (6) aim
to synthesize the literature and to report the results. Firstly, a descriptive
analysis of the identified literature was conducted according to the distri-
bution of the work in the different journals and over time. Secondly,
based on the research questions presented in the first section, the papers
have been classified according to the decision problem addressed (net-
work design, profit‐sharing, inventory management, distribution organi-
zation, vehicle routing) and sustainability dimensions evaluated
(economic, environmental, social). Besides, the studies reviewed have
been categorized according to the research methodology used. Papers
thatusemathematicalmodelssuchasoptimization, simulation and game
theory are classified in the experimental category. The second sub‐
category contains exploratory research that focuses on the definition of
new concepts, case studies or interviews with practitioners to identify
the challenges of real‐world horizontal collaboration, and empirical stud-
ies. The last sub‐category contains literature reviews or surveys on hori-
zontal collaboration in freight transportation in general.
In the following sections, we have concluded an in‐depth analysis
of the literature identified according to the above categorization crite-
ria. This categorization allows us to determine the current trend of hor-
izontal collaboration and sustainability in freight transportation and to
identify research gaps in this field.
3. Results and findings
3.1. Publications per year
In this section, distribution analysis of the papers identified has
been done to study the current research trends between 2010 and
2019, we only have data for the first half of 2020.
Fig. 2 shows the annual distribution of the selected studies. We can
see that the number of publications has increased in recent years, 53%
of the papers have been published since 2015. This analysis reflects the
Fig. 2. Distribution of reference papers by publication date.
Table 1
Inclusion and exclusion criteria used to select papers.
Criteria Justification
Inclusion Papers published between
2010 and the first semester of
2020
To focus on the most recent
publication
Publications in peer-reviewed
journals, conference papers
and chapters
To concentrate on high-quality
articles and other documents for a
broader and more comprehensive
literature search
Papers focused on horizontal
collaboration and freight
transportation
The emphasis on work related to
horizontal collaboration in
transportation
Empirical and experimental
studies and review articles
To include different research
approaches
Exclusion Papers focusing only on
vertical collaboration
The purpose of the research is to
review the existing literature on
horizontal collaboration in freight
transportation
Studies in a language other
than English and French
The researchers involved in this
project can read these languages
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
3

emerging trends and increased attention paid to research into horizon-
tal collaboration in freight transportation. In addition, 4 papers were
identified in the first half of 2020 (5%), which allow us to predict
another year of growth for research areas, especially in the area of
research into freight transport sustainability due to increasing external
pressures from authorities on sustainable development.
3.2. Publications by journal
The selected papers were distributed in a total of 44 international
journals and communicated at 5 international conferences in the field
of logistics and transport. As shown in Fig. 3, 16 journals contain more
than 50% of the papers, while the rest are found in 32 different jour-
nals and at 5 different conferences. The journals of The European Jour-
nal of Operational Research,Transportation Research Part E: Logistics and
Transportation Review,Computers & Operations Research and Journal of
Cleaner Production represents ¼of our sample. The European Journal
of Operational Research,Transportation Research Part E and Journal of
Cleaner Production occupy first, second and third rank, with 10, 5
and 5 publications, respectively.
4. Categorization analysis
Based on the categorization criteria defined above, our discussion
is mainly based on the summary results of Table 2 and Fig. 4.Table 2
presents the classification of the literature. Fig. 4 shows the number of
papers by classification criteria.
4.1. Decision analysis
4.1.1. Strategic decisions
Although strategic decisions are the most crucial in supply chain man-
agement, these decisions are less studied in the literature. In the literature,
these decisions have been integrated with other planning decisions whose
aim is to solve simultaneously or independently the various associated
problems. Usually, strategic decisions in logistics and transport concern
the design or co‐design of distribution networks in order to create a com-
petitive advantage and achieve the desired objectives.
Much of the literature used strategies to consolidate flows and
achieve economies of scale. In this context, Ballot and Fontane
(2010) were the first contributors to consider logistics pooling as a
form of horizontal collaboration that makes it possible to consolidate
flows and reduce greenhouse gas emissions. Then, Gonzalez‐Feliu
(2011) assessed the economic and environmental benefits of logistics
pooling in urban freight transport. To do this, the author generated
scenarios representing realistic situations within the city of Lyon. To
simulate and evaluate the various scenarios, Gonzalez‐Feliu (2011)
proposed an integrated simulation framework combining a demand
generator and a routing optimization model. A structural and organi-
zational framework is developed by Leitner et al. (2011) to optimize
transport efficiency between shippers in horizontal cooperation. The
framework is based on the definition and design of new structures
and processes to ensure the sustainable success of the cooperation.
Logistics models as well as organizational models have been demon-
strated using a case study from Romania. The results obtained showed
that collaboration between the partners allowed them to realize bene-
fits from an economic and an environmental perspective. Likewise,
Gonzalez‐Feliu et al. (2013) addressed the design problem and evalu-
ation of collaborative scenarios in urban freight transport based on a
hierarchical clustering technique and a dominance analysis method
to identify the best collaborative scenario. Furthermore, Pan et al.
(2013) independently explored the environmental and economic
impact of the pooling of warehouses and distribution centers between
shippers. Their findings indicated that pooling is a more effective
approach to reducing greenhouse gas emissions and logistics costs.
Another study conducted by Moutaoukil et al. (2013) which aims to
propose and simulate several scenarios to help small and medium
agri‐food enterprises design a distribution network by optimizing the
three sustainability dimensions. Other scenarios concerning logistical
organization have also been proposed by Pan et al. (2014). The
researchers treated the network distribution design problem by inde-
pendently optimizing the economic and environmental objectives.
To solve this problem, linear programming models were proposed to
determine the best number and location of hubs among a set of hubs
and to define transport plans in each network.
All the work mentioned above assumes the choice of the location
was resolved beforehand, and the main purpose of their contributions
was to define new strategies and organizations for consolidating
flows.Tang et al. (2016) contributed to this topic by studying the
problem of location of regional distribution centers in collaborative
distribution networks for horticultural products in France, while min-
imizing transportation and facility opening costs. Verdonck et al.
(2016) also focused on the problem of locating distribution centers
Fig. 3. Distribution of papers reviewed by journal who have published two or more papers.
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
4

Table 2
Classification of the literature based on the problem addressed, the sustainability assessed and the research methodology adapted.
Decision problem Context Decision-making Sustainability Methodology
Article Network design Profit-
sharing
Inventory Distribution
organization
Routing Deterministic Uncertain Independently Simultaneously Economic Environmental Social Experimental Exploratory Review
Abbad and Salaun (2019) √ √
Ackermann et al. (2011) √√ √ √
Adenso-Díaz et al. (2014a) √√ √ √
Adenso-Díaz et al. (2014b) √√ √ √
Allaoui et al. (2019) √√√√√√ √√√√
Ankersmit et al. (2014) √√ √ √
Audy et al. (2011) √√ √√
Audy et al. (2012) √√ √√
Bailey et al. (2011) √√ √ √
Ballot and Fontane (2010) √√√
Ben Jouida et al. (2017) √√ √√
Berger and Bierwirth (2010) √√ √ √
Björnfot and Torjussen (2012) √ √
Buijs et Wortmann (2014) √√ √ √
Buijs et al. (2016) √√ √ √
Chabot et al., 2018 √√ √ √ √
Chai et al. (2013) √ √
Chen (2016) √√ √ √
Cruijssen et al. (2010) √√ √√
Dahl et Derigs (2011) √√ √ √
Dai et Chen (2012) √√ √√
Defryn et al. (2019) √√√ √ √
Fernández et al. (2016) √√ √ √
Fernández et al. (2018) √√ √ √
Fernández et Sgalambro (2020) √√√√
Frisk et al. (2010) √√ √√
Gansterer et Hartl (2018) √ √
Gonzalez-Feliu (2011) √√√√√√√
Gonzalez-Feliu et al. (2013) √√√ √√
Gonzalez-Feliu et Morana
(2011) √ √
Guajardo et al. (2018) √√ √√
Guajardo et Rönnqvist (2016) √ √
Habibi et al. (2018) √√ √√ √ √
Hacardiaux and Tancrez (2020) √√ √ √√√√
Hernández et al. (2011) √√ √ √
Hernández et al. (2012) √√√√
Hernández et Peeta (2011) √√ √ √
Hernández et Peeta (2014) √√ √ √
Kuyzu (2017) √√ √ √
Leitner et al. (2011) √√√
Li et al. (2016) √√ √√
Liu et al. (2010a) √√ √ √
Liu et al. (2010b) √√ √ √
Lozano et al. (2013) √√ √√
(continued on next page)
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
5

Table 2 (continued)
Decision problem Context Decision-making Sustainability Methodology
Article Network design Profit-
sharing
Inventory Distribution
organization
Routing Deterministic Uncertain Independently Simultaneously Economic Environmental Social Experimental Exploratory Review
Makhloufiet al. (2015) √√ √√√
Martin et Tanguy (2019) √ √
Molenbruch et al. (2017) √√ √ √
Montoya-Torres et al. (2016) √√√ √ √√ √
Moutaoukil et al. (2013) √√ √ √√√√
Moutaoukil et al. (2015) √√ √ √ √
Muñoz-Villamizar et al.
(2019a) √√ √ √√ √
Muñoz-Villamizar et al.
(2019b) √√ √ √ √
Muñoz-Villamizar et al. (2020) √√ √ √ √
Nadarajah and Bookbinder
(2013) √√ √ √
Nataraj et al. (2019) √√√√ √√√√
Ouhader and El Kyal (2017) √√ √√√ √√√√√
Ouhader et kyal (2020) √√ √√√ √√√ √
Özener et al. (2011) √√ √ √
Özener et al. (2013) √√√ √ √
Pan et al. (2013) √√√√√
Pan et al. (2014) √√√√√√√
Pan et al. (2019) √√ √ √
Pérez-Bernabeu et al. (2015) √√ √ √ √
Quintero-Araujo et al. (2016) √√ √ √
Quintero-Araujo et al. (2019) √√√√ √√√√
Ruel (2019) √ √
Sanchez et al. (2016) √√ √ √√ √
Soysal et al. (2018) √√√ √√√√
Sprenger et Mönch (2014) √√ √ √
Stellingwerf et al. (2018) √√√ √√√√
Stellingwerf et al. (2019) √√√ √√√
Tang et al. (2016) √√√√
Vanovermeire et Sörensen
(2014) √√ √√
Vaziri et al. (2019) √√ √ √
Verdonck et al. (2013) √ √
Verdonck et al. (2016) √√ √ √ √ √
Wang et al. (2014) √√ √ √
Wang et al. (2017a) √√√√ √√ √
Wang et al. (2017b) √√√√ √√ √
Wang et al. (2018a) √√√√ √√ √
Wang et al. (2018b) √√√√ √√ √
Wang et al. (2018c) √√√√ √√√ √
Wang et al. (2020) √√√√ √√ √
Wang et Kopfer (2014) √√ √ √
Wang et Kopfer (2015) √√ √ √
Weng et Xu (2014) √√ √ √
Wick et al. (2011) √√ √√
Xu et al. (2012) √√ √√√
Yilmaz et Savasaneril (2012) √√√√
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
6

to quantify the economic benefits of horizontal collaboration in two‐
echelon distribution networks. Similarly, Hernández et al. (2012)
addressed the problem of locating centralized and hybrid hubs. The
proposed model is hybrid because it authorizes the direct shipment
of goods, but it is centralized in the sense that the activities of all car-
riers are grouped in a hub‐and‐spoke system to consolidate flows.
Fernández and Sgalambro (2020) have proposed and analyzed sev-
eral collaborative policies to solve the location problem of the non‐
centralized and non‐hybrid hub. A decision‐making tool for collabo-
rative planning in sustainable supply chains has been proposed more
recently by Allaoui et al. (2019). The proposed tool allows us to gen-
erate optimized scenarios in order to design a supply chain according
to the weight and the sustainability criteria preferred. Naturally,
strategic decisions have a considerable impact on logistics perfor-
mance in the long‐term. Therefore, uncertainties regarding costs
and data need to be considered in order to guarantee the efficiency
future of logistics networks. Only one paper has been presented in
this context (Habibi et al., 2018). This study considers the uncer-
tainty of the costs in the hub location problem of two collaborative
distribution networks.
Although the work mentioned above deals with levels of planning
other than strategic decision making, the different decisions are trea-
ted separately. In recent years, a few contributions have been made
in the literature that propose integrated decision models for collabo-
rative transportation planning. In this context, Ouhader and El Kyal
(2017) have studied the design problem of collaborative distribution
network using a two‐echelon Location‐Routing‐Problem (LRP) model.
The main objective is to design a collaborative logistics network and
to optimize future routes jointly while optimizing the three dimen-
sions of sustainability, namely logistics costs, CO
2
emissions and
job opportunities created. To quantify the impact of horizontal col-
laboration in freight transport, the authors compared the results
obtained through collaboration with those obtained in the
non‐collaborative scenario. The results confirmed that horizontal
collaboration can contribute to a reduction in CO
2
emissions and
transportation costs, but it has a negative impact on the social aspect
evaluated, i.e., the number of jobs. Most recently, an extension of this
study was published in (Ouhader and kyal, 2020). In this contribu-
tion, the researchers were interested in ways of balancing economic
and environmental concerns in a collaborative coalition by using a
multi‐objective approach. Similarly, Quintero‐Araujo et al. (2019)
and Nataraj et al. (2019) studied the urban distribution network
design based on the LRP. The researchers examined the use of hori-
zontal collaboration concepts in integrated routing and location deci-
sions for a single‐echelon distribution network. Several scenarios
with different levels of collaboration were proposed and resolved
using meta‐heuristics with a single objective, namely, to optimize dis-
tribution costs. Hacardiaux and Tancrez (2020) studied the Location‐
Inventory‐Problem (LIP) to assess the environmental benefits of hor-
izontal collaboration in a two‐echelon distribution network. In this
study, the proposed model is principally aimed to optimize the eco-
nomic dimension, while the environmental footprint assessment has
been assessed after the economic resolution.
In addition to the quantitative studies discussed above, there are
also a few empirical studies that examine the strategic effects of logis-
tics collaboration and their barriers in industrial sectors. Björnfot and
Torjussen (2012) and Abbad and Salaun (2019) studied the advan-
tages of pooling logistics and the successful conditions for a collabora-
tive project in the freight distribution industry based on real case
studies. Concerning the obstacles to logistic pooling, Chai et al.
(2013), Martin and Tanguy (2019) and Ruel (2019) surveyed a few
industrialists to identify the reasons and barriers to the implementa-
tion of horizontal pooling. The results of these different surveys
showed that the main obstacle of implementing collaboration between
competitors is the need for information sharing, this has not yet been
studied in detail.
4.1.2. Tactical decisions
The tactical level in supply chain planning usually concerns
medium‐term decisions. It is at this level that decisions are made
regarding the size of the shipment, the gain sharing, the elaboration
of transport or distribution modes, the frequency of customer visits
and the inventory management.
4.1.2.1. Profit allocation. Because the main objective of horizontal
cooperation is to increase the participants' logistical efficiency and
since collaboration is often expressed in gains or additional savings,
many works which have addressed the tactical planning and horizon-
tal collaboration in freight distribution focus on how to share the ben-
efits obtained equitably among the different stakeholders. The
literature can be divided into two sub‐categories. The first regroups
the work that aims to propose only methods for gains and savings allo-
cation, while the second regroups the work that treats other levels of
decision making with the consideration of the benefit‐sharing where
existing allocation methods are used.
A significant part of research treats the first sub‐category
by focusing on collaboration between shippers/carriers and cost‐
sharing. As such, Yilmaz and Savasaneril (2012) studied the problem
of coalition formation among small shippers in an uncertain environ-
ment. The objective of their study is to determine the optimal policy
of coalition under uncertainty and to propose allocation mechanisms
based on game theory while guaranteeing a budget balance between
shippers. Cruijssen et al. (2010) discussed the outsourcing problem
of transport activities for shippers using a case study with four Dutch
grocers. In this study, the authors discussed how logistics service pro-
viders are the initiators of the outsourcing of logistics activities from
shippers. To achieve this, they proposed a selection procedure that
chose shippers with a high potential of synergy. To allocate the savings
obtained by outsourcing transportation activities between shippers in
the same coalition, an allocation method based on the Shapley value
is proposed. In (Frisk et al., 2010), some sharing mechanisms based
on cooperative game theory were studied and a new method has also
been proposed to allocate gains among participants in collaborative
lumber transport. The mechanism designed by Frisk et al. (2010) aims
to find a stable allocation so that the maximum difference between the
relative savings of two transporters will be minimal. As part of a col-
laboration between carriers, a case study conducted by Audy et al.
(2011) with four Canadian furniture manufacturers, to develop a cost
allocation system, based on the Frisk et al. (2010) mechanism, and to
provide a sensitivity analysis of the minimum savings amount that
could convince the manufacturers to join such a coalition. Based on
the latest work above, Dai and Chen (2012) improved the proposed
mechanisms by examining the contribution of each carrier to both
requests for offers and service requests. The proposed mechanisms
have been applied to examine the allocation of the costs between sev-
eral Less Than Truckload (LTL) carriers in a centralized collaboration.
A Mixed Integer Linear Program (MILP) is proposed in (Lozano et al.,
2013) to estimate the cost savings that companies can achieve by
accepting to participate in a cooperative program and to identify the
most profitable opportunities for collaboration. In addition, Lozano
et al. (2013) examined the problem of joint cost savings allocation
using different methods of cooperative game theory.
Frequently, companies are not only looking for efficiency
but also flexibility in their logistics organization. In this context,
Vanovermeire and Sörensen (2014) introduced the flexibility of deliv-
ery times in the operational transport planning optimization and the
profit allocation. To achieve their goal, Vanovermeire and Sörensen
(2014) proposed an approach to measure and recompense the value
of flexibility in the gain repartition between two partners. A method-
ology based on the Shapley value has recently been proposed by
Stellingwerf et al. (2019) to allocate the monetary gains in a way that
considers the contributions of partners to cost and emissions savings in
Joint‐Inventory‐Routing Problem (JIRP). All the work mentioned
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
7

below shows that horizontal collaboration generates economic gains of
up to 30% compared with the non‐collaborative case. A literature
review on existing methods for allocating collaborative transport costs
is published in (Guajardo and Rönnqvist, 2016). The authors con-
cluded that many of the proposed methods are combinations of alloca-
tion models defined in the literature and usually come from
cooperative game theory.
In addition to cost savings, there are also environmental
savings that are usually quantified in terms of CO
2
emissions. Since
environmental preoccupations have pushed companies to declare their
CO
2
emissions. As a result, the environmental benefits obtained
through horizontal collaboration must be shared equitably among
stakeholders. Abbad et al. (2011), Salaun et al. (2011) and Wick
et al. (2011) proposed the first solution on how to allocate GHG emis-
sion savings using the Shapley value concept of cooperative game the-
ory. Likewise, Xu et al. (2012) proposed a method of allocation that
was equitable by adapting the Shapley value concept. These savings
are measured in terms of transport cost and a carbon tax to consider
the environmental impact.
Additionally, some studies have used existing methods to allocate
the gains generated by such a strategy of horizontal collaboration or
by solving such a decision problem, these are the papers of the second
sub‐category. For example, Li et al. (2016) used a cooperative game
theory approach to solve the cost allocation problem in the context
of collaboration between LTL retailers of perishable products, in par-
ticular, in the decision on allocation of transport means. A comparison
of existing allocation methods has been done in (Ouhader and El Kyal,
2017; 2020) to help coalition members to share the economic, envi-
ronmental and social benefits generated by combining planning deci-
sions. Also, Verdonck et al. (2016) examined the different gain‐
sharing mechanisms to equitably share the savings obtained by solving
the facility location problem. In (Sanchez et al., 2016; Wang et al.,
2020; 2018ba, 2018bb, 2018cc, 2018ad, 2017be, 2017af), the
researchers used benefit allocation mechanisms based on game theory
to allocate the benefits of cooperative vehicle routing. The existing
mechanisms for allocating benefits were also used by Guajardo
(2018) to allocate emissions among the different actors involved in
joint transport activities. Özener et al. (2013) compared different cost
allocation methods for cooperation in the joint optimization of inven-
tory and transport decisions.
Since the mechanisms of benefit sharing are important to ensure
the stability of such a coalition, the gain sharing problem can be
extended to the coalition stability problem. Most of the literature cited
above only considers the coalition involving all actors, in other words,
a large coalition is formed. However, coalition actors can leave the alli-
ance at any time (Audy et al., 2012), and some actors may be more
interested in joining sub‐coalitions that are not a large coalition but
offer better benefits for all coalition members (Cruijssen et al.,
2010). Consequently, the question that now arises is how to find the
most stable and profitable sub‐coalitions, where they exist? This prob-
lem corresponds to what the literature calls coalition formation or con-
figuration. Coalition configuration has received less attention in the
literature. Only a few studies have examined coalition formation in
collaborative transport. Audy et al. (2012) were interested in issues
Fig. 4. Number of articles according to the classification criteria.
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
8

of gain sharing and coalition building. A model is proposed in (Audy
et al., 2012)tofind the most stable and credible coalition. In addition,
Ben Jouida et al. (2017) proposed a cooperative replenishment algo-
rithm to solve the problem of forming the most profitable coalitions.
Guajardo et al. (2018) studied the overlapping coalition‐building prob-
lem in freight transport. In this study, the authors proposed two
approaches to configuration. The first one is based on zones, which
assumes that the total territory is divided into zones, while the second
one consists of integrating the coalition configuration into the trans-
port problem. In the studies cited above, only common objectives
are considered in the distribution organization. Defryn et al. (2019)
have contributed to this topic by proposing an approach that considers
the common objectives of the coalition and those of the partners in
order to optimize the transport of freight.
4.1.2.2. Inventory decisions. The decisions concerning inventory man-
agement have been combined with other planning decisions to simul-
taneously optimize them. These decisions were included in the logistic
modelling of perishable products. Recently, the Inventory‐Routing‐
Problem (IRP) was studied by Stellingwerf et al. (2018) in the case
of temperature‐controlled food distribution. In this study, a linear pro-
gramming model that minimizes logistical costs, emissions or a linear
combination of two objectives is proposed to find the optimal route
and inventory plan. From a similar perspective, Soysal et al. (2018)
proposed a model to treat the green IRP with uncertainty in the case
of perishable products. Each product is assumed to have a fixed shelf
life, beyond which it will be wasted, resulting in a penalty cost. Finally,
a Mixed Integer Quadratic Program (MIQP) has been proposed by
Hacardiaux and Tancrez (2020) to jointly optimize location and inven-
tory decisions and to assess the environmental benefits of horizontal
collaboration while satisfying the uncertain demands.
4.1.2.3. Distribution organization. One of the key issues in transport
optimization is the freight distribution organization. The use of joint
routes and the consolidation of means is considered as the main source
of savings. Several existing studies proposed collaborative schemes to
optimize freight distribution. These schemes are essentially based on
the consolidation of means. Companies can share their means of trans-
port and share resources to serve a set of customers. For example, Pan
et al. (2014) compared four scenarios, including the case of joint route
distribution between suppliers, to identify the most efficient transport
plan in terms of cost and CO
2
emission reductions. As well, in
(Moutaoukil et al., 2013) various configurations have been examined
to determine the best distribution strategy that integrates the 3TBL.
Other studies begin with a non‐cooperative scenario and then analyze
the potential benefits that can be achieved by a collaborative practice.
For example, Ouhader and El Kyal (2017, 2020) compared two
decision‐making scenarios, a scenario in which there is no cooperation
between suppliers and a second scenario which is totally collaborative
in which decisions are taken jointly. Similarly, Quintero‐Araujo et al.
(2019); Nataraj et al. (2019); Gonzalez‐Feliu (2011); Gonzalez‐Feliu
et al. (2013); Sanchez et al. (2016); Wang and Kopfer (2015), Wang
et al (2017a); (2017b; 2018a; 2018b; 2018c; 2020;)) and Montoya‐
Torres et al. (2016) focused on last‐mile distribution to develop and
optimize a collaborative distribution network. Their main objective
is to assess the benefits obtained through collaboration and joint plan-
ning. Recently, Muñoz‐Villamizar et al. (2019a) considered a fleet of
electric vehicles as a strategy in collaborative routing in the urban
area. A case study from actual data has been conducted to quantify
and analyze the different advantages of using a mixed vehicles fleet
(electric and diesel). The results show that electric vehicles are more
profitable, economically and ecologically, for cooperation periods of
over three years.
4.1.3. Operational decisions
Most of the literature on horizontal collaboration in freight trans-
port focuses principally on operational planning, in approximately
60% of the studies reviewed. Operational planning consists of estab-
lishing optimal or efficient transportation plans by sharing resources
in order to improve the operational efficiency of carriers/shippers. In
fact, the researchers mainly concentrate on the operational problems
related to vehicle routing under several variants. Reviews of the litera-
ture provide a detailed overview of techniques and solutions of opera-
tional planning problems (Verdonck et al., 2013; Gansterer and Hartl,
2018). Regarding experimental studies, most work focuses on the quan-
tification of benefits and the roles of joint route planning (Adenso‐Díaz
et al., 2014a; Fernández et al., 2018; Molenbruch et al., 2017; Montoya‐
Torres et al., 2016; Pérez‐Bernabeu et al., 2015; Quintero‐Araujo et al.,
2016; Sanchez et al., 2016; Soysal et al., 2018; Sprenger and Mönch,
2014; Wang et al., 2018c; Yilmaz and Savasaneril, 2012, Adenso‐Díaz
et al., 2014b; Buijs et Wortmann, 2014; Dai and Chen, 2012; Morana
et al., 2014; Gonzalez‐Feliu, 2011). The literature indicates that joint
vehicle routing planning in freight transport can provide economic ben-
efits as well as environmental gains (Chabot et al., 2018; Muñoz‐
Villamizar et al., 2020; 2019a; 2019b;; Wang et al., 2018c), which
improves the profitability of companies through the inclusion of exter-
nal resources (Wang et al., 2014).
Several variants of Collaborative Vehicle Routing Problems (CVRP)
have been discussed in the literature. The Multi‐Depot Capacitated Arc
Routing Problem (MDCARP) with Full Truckloads (FTL) in carrier col-
laboration has been studied by Liu et al. (2010a) to minimize empty
movements. A two‐step heuristic has been developed to solve the pro-
posed model and large instance problems. In a similar context,
Fernández et al. (2016) developed an optimization model to solve
the non‐capacitive Arc Routing Problem (ARP). The Multi‐Depot Arc
Routing Problem (MDARP) has also been studied by Weng and Xu
(2014) to fuse flows and to optimize the hub routing of the merged
tasks. The authors note that the fusion of transport activities allows
companies to achieve important economies of scale. Based on the con-
cept of resource pooling, Nadarajah and Bookbinder (2013) proposed
a modelling framework and resolution approach to address the routing
problem between LTL carriers. Sprenger and Mönch (2014) developed
a decision support system tool for cooperative transport planning in
the food industry where several manufacturing companies share their
fleets to reduce transport costs. An approach based on decomposition
strategies has been proposed in (Buijs et al., 2016) to address the prob-
lem of collaborative road transport planning of two Dutch companies.
In addition to collaboration in road and rail transport, this strategy has
also been envisaged in air transport (Ankersmit et al., 2014). In the lat-
ter, the researchers proposed a conceptual simulation framework to
identify the potential for collaboration between several freight for-
warders at Schiphol‐Amsterdam Airport for short distances.
Frequently, shipment requests are not efficiently integrated into a
carrier's itinerary. In this case, a collaboration between the carriers
that consists of allocating or exchanging requests is necessary to
improve the overall efficiency of the distribution network and the
profit of each carrier. The different objectives of request exchanges
and collaborative planning have been discussed in multiple research
studies (Ackermann et al., 2011; Nadarajah and Bookbinderm, 2013;
Dai and Chen, 2012; etc.). Liu et al. (2010b) have combined the Vehi-
cle Routing Problem VRP with the demand selection problem where
carriers receive shipment orders that they must respond to either with
internal fleets or outsourced requests via a transport partner. The prin-
cipal objective is to route vehicles and to select requests by minimizing
the total cost when a carrier responds to requests. In the same context,
Berger and Bierwirth (2010) studied the problem of reallocation of
transport demand in a collaborative network to maximize the total
profit to carriers. In reality, to massify the flow and increase the fill
rate, companies make their shipments in FTL. In this context, Özener
et al. (2011) developed a lane exchange mechanism to treat the prob-
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
9

lem of exchange demand in decentralized collaboration. Likewise,
Kuyzu (2017) studied the fusion and exchange of lanes in collaborative
supply between LTL carriers. A decision support system for an express
courier collaborative network is developed by Dahl and Derigs (2011).
This tool is designed to promote real‐time exchange of requests
between carriers. The researchers demonstrated that with a real‐time
exchange system, the network can operate at a level like that obtained
by centralized planning. In this latter, planning decisions are taken by
a central authority and requests are exchanged via this authority,
which can be an online platform (Dai and Chen, 2012), in contrast
to decentralized planning.
Chen (2016) developed a combinatorial clock‐proxy exchange
mechanism for LTL carriers. Another type of planning discussed in
the literature is dynamic planning. An example of the work conducted
by Wang et Kopfer (2015) studies the dynamic version of the collabo-
rative planning problem with a rolling horizon in which carriers can
exchange customer requests. The reduction and optimization of travel
time, and particularly the empty returns of trucks. Bailey et al. (2011)
contributed to this topic by proposing an efficient method that makes
it possible to add delivery and pick‐up tasks during the return routing.
Two optimization models have been developed to elaborate the vehi-
cle itineraries and to select the requests to be shipped. The first one is
modelled as a Linear Program LP, while the second one is formulated
as a Mixed Integer Program (MIP). A heuristic and a taboo search are
used to solve these models. The results from the actual data show that
the percentage of cost reductions can be as high as 27%. Recently,
Vaziri et al. (2019) studied the Pickup and Delivery Problem PDP in
the context of a FTL carrier. The researchers proposed a MIP to allocate
profits equitably among carriers and minimize travel time. Another
model has been presented in (Wang et Kopfer, 2014). In this model,
the researchers assumed that LTL carriers can exchange all their
pick‐up and delivery requests in order to reduce transport costs. An
extension of the latter study that includes subcontracting is proposed
by Wang et al. (2014). In a similar contribution, Hernández and
Peeta (2014) addressed the single‐carrier collaboration problem in
which an LTL carrier seeks to collaborate with other partners by
acquiring the external capacity in order to meet the excess demand.
In practice, many constraints exist in collaborative VRP. These con-
straints lead to several variants of the problems. In fact, the collabora-
tive routing problem should consider the capacity constraints of the
carriers (Hernández et al., 2011; Hernández and Peeta, 2011).
Hernández and Peeta (2011) studied the centralized collaboration
problem between several carriers when capacity depends on time, in
other words, carriers can use or provide transport capacity during rout-
ing. The problem has been modelled as a Minimum Cost Flow Problem
(MCFP) and solved using a Branch‐and‐Cut (B&C) algorithm. The
authors addressed the deterministic and dynamic problem of single‐
carrier collaboration for LTL Small and Medium‐sized Enterprises
SMEs. The problem is deterministic because the demands are fixed
and the capacities depend on time and are known in advance, but it
is dynamic in the sense that the demands have time windows for
pickup or delivery. Another research stream attempted to integrate
time windows in collaborative transportation planning. For example,
Moutaoukil et al. (2015) solved the vehicle routing problem with time
windows to study the impact of last‐mile delivery on the three sustain-
ability dimensions. More recently, Wang et al. (2020) addressed col-
laborative planning in a multi‐depot logistics network with an
allocation of time windows to the delivery requests. Finally, a
simulation‐based decision support system that considers time con-
straints and stochastic demand for pick‐up and delivery has been pro-
posed by Makhloufiet al. (2015).
4.2. Sustainability analysis
In the last two decades, due to environmental issues and social
development, the sustainability aspects have become important mea-
sures in the organization and optimization of distribution networks.
Indeed, the sustainability considerations have been included progres-
sively in the collaborative transport optimization, namely in the strate-
gic decisions that have a significant long‐term impact on the global
performance of companies. The current state of the art in collaborative
transport consists essentially of integrating economic and environmen-
tal objectives, while the social aspect is less studied in the scientific lit-
erature, see Fig. 4. Please note that in classifying the literature
according to sustainability criteria, we focused on studies that address
one or more dimensions of sustainability, the remains of the studies
address the collaboration in a general perspective through surveys or
reviews
We can see that the economic dimension dominated the distribu-
tion network design and optimization problem. From the 79 articles
classified in Table 2, 52 deal only the economic objectives, 22 consider
the economic and environmental objectives and only three articles
address the distribution network optimization problem considering
the three sustainability aspects. Only two studies assess the environ-
mental dimension in the freight distribution organization. Finally, no
contributions are discussing collaborative transport problems from a
single social perspective and at the intersection between the social
and environmental or economic dimensions.
4.2.1. Economic measures
In a supply chain, the main objectives are to ensure the satisfaction
of customer demands and the functionality of the global network pro-
cesses. Literature on horizontal collaboration and transportation plan-
ning focuses mostly on the economic dimension to achieve these goals.
Like the traditional optimization models, much of the contribution
seeks to minimize costs or maximize benefits in transportation plan-
ning, in the strategic, tactical, operational level, or in the mix of these
different decisions. However, maximizing benefits to measure eco-
nomic performance in decision models has received much less atten-
tion. This can be expressed by the fact that manufacturers always
seek to obtain a competitive advantage by optimizing their logistics
network. In the studies reviewed, the most traditional measure for
assessing economic impact is the total cost of the network, which
can depend on the planning decisions addressed. For example, at the
strategic level, the cost of opening or choosing the location of a facility
is present in 9 studies on collaborative network design (Allaoui et al.,
2019; Habibi et al., 2018; Hacardiaux and Tancrez, 2020; Hernández
et al., 2012; Nataraj et al., 2019; Ouhader and El Kyal, 2017; 2020;;
Quintero‐Araujo et al., 2019; Tang et al., 2016). Usually, in transport
planning problems, the costs due to transport is the main component
used to assess economic performance. The latter is discussed in almost
all studies quantifying or optimizing the economic benefit of horizon-
tal collaboration in freight transport. This cost is generally calculated
based on the distance travelled between an origin and a given destina-
tion. In addition of these two economic components, other studies
include inventory costs (Allaoui et al., 2019; Hacardiaux and
Tancrez, 2020; Özener et al., 2013; Pan et al., 2013; Soysal et al.,
2018; Stellingwerf et al., 2019; 2018) and logistical costs related to
the handling operations and the use of transport (Allaoui et al.,
2019; Moutaoukil et al., 2013; Nataraj et al., 2019; Ouhader and El
Kyal, 2017; 2020; 2013;; Pan et al., 2014; Quintero‐Araujo et al.,
2019) in the economic criteria modelling. Additionally, Chabot et al.
(2018) and Moutaoukil et al. (2015) attempted to assess the service
level of horizontal collaboration in freight transport as another mea-
sure of economic performance.
4.2.2. Environmental measures
Besides economic objectives, environmental preoccupations have
received increasing attention in recent years. The questions relating
to the demand for environmental protection and the responsible con-
sumption of natural resources have prompted companies to integrate
environmental factors into their planning decisions, giving rise to
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
10

the optimization of logistics networks from a green perspective. Usu-
ally, one of the most important impacts caused by logistics and trans-
port activities comes from the over‐exploitation of resources (energy,
water, etc.). The use of energy causes some indirect pollution prob-
lems, called greenhouse gases, that are considered the major contribu-
tors to climate change and global warming by their impact on the
greenhouse effect (Pan, 2010). Moreover, according to the Intergov-
ernmental Panel on Climate Change (IPCC) report, the transport sector
generates around 25% of global emissions related to the quantity of
energy consumed, of which 80% comes from road transport. The com-
bustion of petroleum‐based fuels in internal combustion motors is the
main source of these emissions. Although emissions are composed of
other gases in addition to carbon dioxide (CO
2
), such as methane
(CH
4
) and nitrous oxide (NO
x
), their quantity is relatively small com-
pared to the quantity of CO
2
, which represents approximately 77%
of all greenhouse gases (SOes, 2010). For this reason, environmental
impact measures focus primarily on the reduction of CO
2
emissions
in the freight transport sector. However, the calculation of CO
2
emis-
sions due to transport requires more complex and sophisticated
methodologies, which can only be estimated because of the difficulty
to quantify various aspects such as meteorological conditions and traf-
fic congestion. For this reason, most research usually proposes and
uses simple estimation methodologies based on distance (see, for
example, Gonzalez‐Feliu, 2011; Leitner et al., 2011; Montoya‐Torres
et al, 2016; Makhloufiet al., 2015; Muñoz‐Villamizar et al., 2020)
and/or on energy consumption (see, for example, Chabot et al.,
2018; Muñoz‐Villamizar et al., 2020; Stellingwerf et al., 2019; 2018;;
Soysal et al., 2018; Wang et al., 2018c). Nataraj et al. (2019) and
Quintero‐Araujo et al. (2019) used the estimation model proposed
by Ubeda et al. (2011) to estimate the environmental impact of the dif-
ferent collaboration scenarios. This model is based on distance and the
load carried in each vehicle. Based on previous studies, Ballot and
Fontane (2010) and Pan et al. (2014, 2013) developed a model for esti-
mating CO
2
emissions that considers the load transported, the capacity
used, the average travel speed and the distance travelled. Additionally,
to improve the estimation model proposed in (Ballot and Fontane,
2010; Pan et al., 2014; 2013), Moutaoukil et al. (2015) integrated
vehicle manufacturing and amortization of emissions to better quan-
tify the environmental dimension. Likewise, Ouhader and El Kyal
(2017, 2020) used the model proposed in the work cited above, but
with the consideration of emissions from truck and vehicle returns.
As regards the environmental consideration in the optimization
model, all the research work considering both aspects of economic
and environmental sustainability, except for (Ouhader and El Kyal,
2017; 2020;; Wang et al., 2018c; Muñoz‐Villamizar et al., 2019b),
mainly optimizes the economic objectives and assesses the environ-
mental dimension a Posteriori. Only the work of Ouhader et El Kyal
(2017, 2020), Muñoz‐Villamizar et al. (2019b) and Wang et al.
(2018c) optimize both objectives, simultaneously using multi‐criteria
approaches. Consequently, environmental integration in multi‐
criteria optimization models seems to be a new research topic.
4.2.3. Social measures
Social sustainability has been considered much less than economic
and environmental sustainability, only 3 articles integrate social indi-
cators in their decision model. By nature, social sustainability in sup-
ply chains is about social welfare. The integration of Corporate
Social Responsibility into planning decisions allows us to better assess
the impact of a supply chain for its stakeholders: customers, employees
and local community. It also makes it possible to consider the negative
and negative effects of logistics and transport on human life. Guideli-
nes for the corporate social responsibility and the engagement of orga-
nizations in sustainable development are presented in ISO Guide
26000. This identifies seven issues central for corporate social respon-
sibility: governance of organization, human rights, working relations
and conditions, environment, fair operating, consumer issues and
Community and local development. In the subfield working relations
and conditions, employment is the main social indicator used in the
social performance evaluation, 2 of the 3 studies considering the social
aspect focus on the quantification of the number of jobs. Ouhader and
El Kyal (2017) assessed the social aspect by the calculation of the vari-
able job opportunities created dependent on the facilities’capacity.
The experiments' results showed that collaboration has a negative
impact on the social dimension because it reduces the number of jobs
created. Very recently, a decision support tool was proposed by Allaoui
et al. (2019) to assess the three aspects of sustainability according to
the indicator selected and the corresponding weighting for each. In
this tool, the social aspect is assessed by quantifying the total number
of jobs, the satisfaction of employees and other indicators related to
employees' health and safety, such as the accident rate. In
(Moutaoukil et al., 2013), the researchers focused on the transporter’s
safety and the resident’s comfort. Because of the difficulty in quantify-
ing these two aspects, Moutaoukil et al. (2013) used an estimation of
accident risk and a quantification of the vehicle numbers used to be
capable of assessing accident risk and congestion in the transport net-
work, respectively.
The integration of social criteria in sustainable optimization models
for collaborative transport networks creates difficulties in modelling
and evaluation. As noted above, the social welfare issues concern mul-
tiple stakeholders and are multi‐disciplinary. As a result, the social per-
formance is difficult to measure with quantitative indicators. In
addition, due to the qualitative nature of social aspects, the integration
of social objectives with other sustainability dimensions in decision‐
making models is often very complex and difficult to establish.
Multi‐criteria approaches should therefore be used to consider all sus-
tainability objectives simultaneously.
4.3. Research methods
It can be seen from Fig. 4 and Table 2 that most studies used opti-
mization concepts and experimental methods. These studies focused
on the resolution of planning problems using decision‐support tools
based on mathematical approaches and simulation techniques. Con-
cerning mathematical approaches, MILP or Mixed Integer Non‐Linear
Programming (MINLP), heuristics and meta‐heuristics are the most
used in the literature to treat the optimization problem of transport
networks. Since most work addresses only one dimension of sustain-
ability, single‐objective optimization is most frequently applied in ana-
lytical optimization models (Adenso‐Díaz et al., 2014b; Buijs et al.,
2016; Nataraj et al., 2019; Pan et al., 2014; Quintero‐Araujo et al.,
2019, 2016; Stellingwerf et al., 2018; Wang et Kopfer, 2014, etc.).
Multi‐objective optimization has also been used in some studies to
incorporate environmental and social considerations in economic deci-
sion models. In this context, Muñoz‐Villamizar et al. (2019b) used the
weighted sum technique to integrate environmental and economic
objectives in the planning and organization of last‐mile transport. Also,
Allaoui et al. (2019) aggregated the three goals of sustainability into a
single objective. Ouhader and El Kyal (2017, 2020) applied the ε‐
constraint method to simultaneously optimize the economic, environ-
mental and social dimension in the two‐echelon distribution network
design. In the gain sharing problem, cooperative game theory is the
main approach adopted to solve this problem (Ben Jouida et al.,
2017; Frisk et al., 2010; Lozano et al., 2013; Vanovermeire and
Sörensen, 2014; Verdonck et al., 2016; Yilmaz and Savasaneril,
2012). In addition, discrete event simulation (Makhloufiet al.,
2015) and multi‐agent systems have been used extensively for opera-
tional transport planning (Berger and Bierwirth, 2010; Buijs et al.,
2016; Sprenger and Mönch, 2014).
The second most used category of research methodology is explora-
tory studies. Nearly ten per cent of the literature examined in this
study focuses on defining and exploring new concepts of horizontal
collaboration using case studies (Ballot and Fontane, 2010;
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
11

Gonzalez‐Feliu, 2011; Leitner et al., 2011) or interviews with industri-
alists (Björnfot and Torjussen, 2012; Abbad and Salaun, 2019; Chai
et al., 2013; Martin and Tanguy, 2019; Ruel, 2019). Finally, only four
papers have been identified which review the state of the art of opti-
mization models (Guajardo and Rönnqvist, 2016; Verdonck et al.,
2013; Gansterer and Hartl, 2018) and solutions for horizontal collab-
oration in freight transport (Pan et al., 2019).
5. Research trends and gaps
The purpose of this section is to analyze trends and gaps in the
research on collaboration and sustainability in freight transportation,
in order to identify the future research opportunities.
The first notable observation from Fig. 4 is that different planning
decisions from the strategic to the operational level have been dis-
cussed in the literature on horizontal collaboration in freight distribu-
tion. This shows the importance and the development of horizontal
cooperation in logistics and transport. In addition, environmental pre-
occupations and social issues have gained importance in recent years
to extend the economic aspects and include other sustainable develop-
ment aspects. This also demonstrates the relevance and the importance
of integrating sustainable development aspects into the planning deci-
sions with a view to achieving efficient and effective logistics.
A second finding is that a lot of attention has been focused on the
development of decision‐making models, especially for the problem of
transportation routing and benefit‐sharing. In contrast, the strategic
planning, the distribution organization and the inventory management
received significantly less attention than the decisions mentioned
before.
Thirdly, the inclusion of planning decisions in a single optimization
model, to optimize these decisions simultaneously, has attracted the
attention of some researchers in the last three years. According to this
review, there are three types of problems: the LRP that involves com-
bining vehicle routing when designing a collaborative network, the
IRP, which integrates inventory decisions with vehicle routing, and
the LIP that combines inventory and location decisions. Further studies
would be necessary because of the advantages of considering various
decisions simultaneously.
Fourthly, there is very limited research on collaboration with com-
petitors and Logistics Service Providers (LSP). Most of the research
focuses on upstream collaboration, among suppliers. Therefore, it
would be interesting to study the collaboration with several internal
and external entities.
Fifthly, from the perspective of sustainability, the performance
measures of horizontal collaboration in freight transport have evolved,
passing from mainly economic aspects to also include the environmen-
tal dimension. However, as mentioned in section 4.2.3, very little
research has been conducted on the social dimension of sustainability
or the consideration of all three dimensions simultaneously, compared
to the quantity of research on the other two aspects. This conclusion
demonstrates the need to find and include social aspects to balance
all sustainability considerations.
Finally, in terms of research methodology, many studies have used
experimental methods based on mathematical models. This can be
explained by the interest of researchers in operational research meth-
ods and by the need to model a large variety of problems. Indeed, most
work focuses on the development of single‐objective and deterministic
optimization models. Very little of the research has used multi‐
objective approaches to consider two or more sustainability objectives.
In addition, empirical studies have received much less attention in the
literature despite their importance in the experimental modelling of
optimization problems. For example, to study collaboration between
competing actors, empirical studies are necessary to explore the oppor-
tunities and obstacles of implementing such a collaborative strategy. In
addition, social modelling will need qualitative research to better
understand human goals in societal terms and to integrate them into
optimization models.
6. Future research orientations
From the analysis results, we can suggest some opportunities for
future research to fill the gaps in the literature on collaborative plan-
ning and sustainability in freight transport.
The first opportunity would be to extend collaboration downstream
of the distribution system and eventually with external parties such as
customers and competitors. Indeed, the integration of the customers in
a collaborative approach could increase the overall performance of
companies, for example, cooperation with customers during the collec-
tion of products returned for recycling. This topic deserves further
study in future research on the implementation of a collaborative
approach and the development of sustainable practices.
A second research line is to integrate the various decisions related
to the strategic, tactical and operational levels into an exhaustive
model. In fact, the strategic decisions have a significant impact on
the performance of organizations in the long‐term, on the one hand,
and on tactical and operational decisions and constraints, on the other.
Similarly, tactical and operational decisions can have impacts on trans-
port efficiency, for example, the optimization of freight distribution,
the modification of delivery frequencies and last‐mile distribution
mode (electric vehicles, drones, etc.) can reduce logistics costs and
other environmental and social consequences, but also have a signifi-
cant impact on the fill rate of vehicles. According to this analysis, the
combination of different levels of planning has been virtually
neglected in the literature. As noted in the previous section, no studies
have considered inventory, routing and design decisions simultane-
ously for a single or multiple echelon of a distribution system. Further-
more, few studies have considered the uncertainties relating to
parameters (demand, travel time, etc.) and logistical constraints (ca-
pacity, time windows, etc.). Consequently, optimization models con-
sidering these aspects for integrated planning problems should be
developed in future work.
A third research perspective concerns evaluation criteria and opti-
mization approaches. Firstly, concerning performance measurement,
the most frequent criterion in the work reviewed was economic perfor-
mance, namely minimizing operating costs, including variable and
fixed costs, transport and logistics costs. Emission taxes could be inte-
grated into the economic assessment in order to encourage companies
to use cleaner technologies from a green perspective. In contrast, the
other two aspects of sustainability, and in particular, the social criteria,
are rarely considered when assessing the performance of a distribution
network. Indeed, the environmental dimension is usually limited to
the calculation of the CO
2
emissions from transport. Direct emissions
from facilities and from inventory activities, for food products, as well
as other environmental factors such as the quantity of waste and
energy consumption, which could be included in the environmental
assessment. As regards the social dimension, the latter is practically
absent because of its qualitative nature and is only limited to quantify-
ing the number of jobs created and the health and safety factor for
employers. Therefore, future research should be focused on the way
this aspect is quantified. In addition, other operational factors such
as flexibility, use of resources and quality of service can be considered
in assessing the overall performance of logistics networks. Secondly,
performance criteria must be balanced, therefore, modelling
approaches should be developed for effective decision making. A
research opportunity would be to combine classical optimization
methods with qualitative (multi‐criteria) approaches to consider for-
mal criteria.
A fourth research direction is related to the problem of sharing the
gains generated by horizontal collaboration between partners. Almost
all the research reviewed on this topic focused on how to share the
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
12

economic gains equitably in order to maintain the stability of the coali-
tion and to encourage partners to collaborate. The allocation of emis-
sions and social benefits has received much less attention in research.
Moreover, the majority of studies have concentrated on the compar-
ison and selection of the most efficient allocation methods. The way
of considering sustainable development perspectives in the sharing
mechanism seems to be an interesting research topic. In addition,
another important research question is how to maximize gains and
to form the best coalition from a sustainable perspective. Finally,
because of the dynamic nature of real problems, the formation of a
coalition may be modified over time. It is then necessary to study
how the uncertainties can be integrated in the gain sharing mecha-
nisms. It would then be necessary to study how the uncertainties could
be incorporated into the gain‐sharing mechanisms.
The final research opportunity concerns operational transport plan-
ning. Indeed, collaborative vehicle routing has been the subject of
much research due to the decentralized nature of logistics organiza-
tions. In this context, auction mechanisms are used as a powerful tool
for increasing the benefits of collaboration in economic terms. Envi-
ronmental and social issues are practically absent in the exchange
mechanisms proposed (Pan et al., 2019). Consequently, request
exchange mechanisms should take sustainable development consider-
ations into account, especially in last‐mile transport, for example,
design a mechanism for allocating requests while maximizing trans-
port turnover, but also reducing negative effects on the environment
and society (CO
2
emissions, noise, congestion, accidents, etc.).
Research on these topics should be developed in future literature.
7. Conclusion
In recent years, horizontal collaboration has become a very effec-
tive and emerging strategy in freight transport, particularly from a sus-
tainability perspective. In this paper, an SLR was conducted on
collaboration and sustainability in freight transport with a focus on
work published over the past decade. This review aimed to identify
and examine the existing state of the art. We classified and analyzed
the literature according to three categories, namely the decision level,
the sustainability dimension and the research methodology used.
Then, based on the results of the analysis, we identified some research
perspectives that merit investigation in future work to ensure the sus-
tainability of transport.
The results of this review have shown that operational transport
planning is the major decision addressed, with nearly 60% of the liter-
ature reviewed. Also, the collaboration between external parties such
as logistics service providers, competitors and customers has received
very little attention. Furthermore, from a performance measurement
point of view, more attention has been given to economic and environ-
mental criteria than to social criteria. These are neglected in the liter-
ature. Moreover, most research uses experimental methods based on
mathematical models, empirical research is not used well despite its
importance in modelling problems, for example in measuring social
performance.
Based on the results of the qualitative and quantitative analysis, we
suggested five promising directions for research that merit further
study. Firstly, it is about extending horizontal collaboration with other
external parties. Secondly, the different planning decisions can be
combined in a global model to improve the overall performance of
the logistics network. Thirdly, assessment criteria and modelling
approaches should be improved for better decision‐making on eco-
nomic, environmental and social sustainability. Fourthly, it is impor-
tant to study the stability of a coalition and the sharing of benefits
in a sustainable perspective and under uncertain circumstances.
Finally, the three aspects of sustainable development should be inte-
grated in request sharing mechanisms to ensure transport efficiency
and effectiveness, particularly in urban cities.
In this study, we have focused on studies that concentrate on prac-
tical solutions for horizontal collaboration in freight transportation.
Innovative solutions such as the physical internet that are not yet fully
implemented in industrial applications are excluded in this review.
This last point could be considered as a limitation of this study.
References
Ackermann, H., Ewe, H., Kopfer, H., & Küfer, K. (2011). Combinatorial auctions in
freight logistics. In J. Böse, H. Hu, C. Carlos, X. Shi, R. Stahlbock, & S. Voss (Eds.),
Computational logistics. In Lecture Notes in Computer Science: 6971 (pp. 1–17).
Berlin, Heidelberg: Springer.
Abbad, H., Salaun, V., 2019. Quelles contributions de la proximité à la réussite d’un
projet de mutualisation logistique ? Le cas du GIE chargeurs Pointe de Bretagne.
Logistique & Management 27 (2), 108–118. https://doi.org/10.1080/
12507970.2019.1596759.
Adenso-Díaz, B., Lozano, S., Garcia-Carbajal, S., Smith-Miles, K., 2014a. Assessing
partnership savings in horizontal cooperation by planning linked deliveries.
Transportation Research Part A: Policy and Practice 66, 268–279. https://doi.org/
10.1016/j.tra.2014.05.013.
Adenso-Díaz, B., Lozano, S., Moreno, P., 2014b. Analysis of the synergies of merging
multi-company transportation needs. Transportmetrica A: Transport Science 10 (6),
533–547. https://doi.org/10.1080/23249935.2013.797518.
Aloui, A., Derrouiche, R., Hamani, N., Delahoche, L., 2020. Collaboration horizontale
durable des reseaux de transport de marchandises : etat de l'art et
perspectives. 13ème Conférence Internationale de Modélisation, Optimisation et
Simulation (MOSIM'20) , Agadir (on line), Maroc
Allaoui, H., Guo, Y., Sarkis, J., 2019. Decision support for collaboration planning in
sustainable supply chains. J. Cleaner Prod. 229, 761–774. https://doi.org/10.1016/
j.jclepro.2019.04.367.
Ankersmit, S., Rezaei, J., Tavasszy, L., 2014. The potential of horizontal collaboration in
airport ground freight services. Journal of Air Transport Management 40, 169–181.
https://doi.org/10.1016/j.jairtraman.2014.07.005.
Audy, J.-F., D’Amours, S., Rönnqvist, M., 2012. An empirical study on coalition
formation and cost/savings allocation. Int. J. Prod. Econ. 136 (1), 13–27. https://
doi.org/10.1016/j.ijpe.2011.08.027.
Audy, J.-F., D’Amours, S., Rousseau, L.-M., 2011. Cost allocation in the establishment of
a collaborative transportation agreement—an application in the furniture industry.
Journal of the Operational Research Society 62 (6), 960–970. https://doi.org/
10.1057/jors.2010.53.
Bailey, E., Unnikrishnan, A., Lin, D.-Y., 2011. Models for Minimizing Backhaul Costs
through Freight Collaboration. Transp. Res. Rec. 2224 (1), 51–60. https://doi.org/
10.3141/2224-07.
Ballot, E., Fontane, F., 2010. Reducing transportation CO 2 emissions through pooling of
supply networks: perspectives from a case study in French retail chains. Production
Planning & Control 21 (6), 640–650. https://doi.org/10.1080/
09537287.2010.489276.
Ben Jouida, S., Krichen, S., Klibi, W., 2017. Coalition-formation problem for sourcing
contract design in supply networks. Eur. J. Oper. Res. 257 (2), 539–558. https://doi.
org/10.1016/j.ejor.2016.07.040.
Berger, S., Bierwirth, C., 2010. Solutions to the request reassignment problem in
collaborative carrier networks. Transportation Research Part E: Logistics and
Transportation Review 46 (5), 627–638. https://doi.org/10.1016/j.
tre.2009.12.006.
Björnfot, A., Torjussen, L., 2012. Extent and Effect of Horizontal Supply Chain
Collaboration among Construction SME. J. Eng. 2 (1), 47–55.
Buijs, P., Alvarez, J.A.L., Veenstra, M., Roodbergen, K.J., 2016. Improved Collaborative
Transport Planning at Dutch Logistics Service Provider Fritom. Interfaces 46 (2),
119–132. https://doi.org/10.1287/inte.2015.0838.
Buijs, P., (“Hans”) Wortmann, J.C., 2014. Joint operational decision-making in
collaborative transportation networks: the role of IT. Supp Chain Mnagmnt 19,
200–210. https://doi.org/10.1108/SCM-08-2013-0298
Chabot, T., Bouchard, F., Legault-Michaud, A., Renaud, J., Coelho, L.C., 2018. Service
level, cost and environmental optimization of collaborative transportation.
Transportation Research Part E: Logistics and Transportation Review 110, 1–14.
https://doi.org/10.1016/j.tre.2017.11.008.
Chen, H., 2016. Combinatorial clock-proxy exchange for carrier collaboration in less
than truck load transportation. Transportation Research Part E: Logistics and
Transportation Review 91, 152–172. https://doi.org/10.1016/j.tre.2016.04.008.
Cruijssen, F., Borm, P., Fleuren, H., Hamers, H., 2010. Supplier-initiated outsourcing: A
methodology to exploit synergy in transportation. Eur. J. Oper. Res. 207 (2),
763–774. https://doi.org/10.1016/j.ejor.2010.06.009.
Chai, Y., O. Chanut, V. Michon, et T. Rocques. 2013. « La mutualisation logistique pour
des Supply Chains durables. » Dans La logistique: Une approche innovante des
organisations, edited by N. Fabbe-Costes et G. Paché,187–194. Aix-Marseille
Université, Presse Universitaire de Provence.
Dahl, S., Derigs, U., 2011. Cooperative planning in express carrier networks —An
empirical study on the effectiveness of a real-time Decision Support System. Decis.
Support Syst. 51 (3), 620–626. https://doi.org/10.1016/j.dss.2011.02.018.
Dai, B., Chen, H., 2012. Profit allocation mechanisms for carrier collaboration in pickup
and delivery service. Comput. Ind. Eng. 62 (2), 633–643. https://doi.org/10.1016/
j.cie.2011.11.029.
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
13

Durach, C.F., Kembro, J., Wieland, A., 2017. A new paradigm for systematic literature
reviews in supply chain management. Journal of Supply Chain Management 53 (4),
67–85.
Defryn, C., Sörensen, K., Dullaert, W., 2019. Integrating partner objectives in horizontal
logistics optimisation models. Omega 82, 1–12. https://doi.org/10.1016/j.
omega.2017.11.008.
Fernández, E., Fontana, D., Speranza, M.G., 2016. On the Collaboration Uncapacitated
Arc Routing Problem. Comput. Oper. Res. 67, 120–131. https://doi.org/10.1016/
j.cor.2015.10.001.
Fernández, E., Roca-Riu, M., Speranza, M.G., 2018. The Shared Customer Collaboration
Vehicle Routing Problem. Eur. J. Oper. Res. 265 (3), 1078–1093. https://doi.org/
10.1016/j.ejor.2017.08.051.
Fernández, E., Sgalambro, A., 2020. On carriers collaboration in hub location problems.
Eur. J. Oper. Res. 283 (2), 476–490. https://doi.org/10.1016/j.ejor.2019.11.038.
Frisk, M., Göthe-Lundgren, M., Jörnsten, K., Rönnqvist, M., 2010. Cost allocation in
collaborative forest transportation. Eur. J. Oper. Res. 205 (2), 448–458. https://doi.
org/10.1016/j.ejor.2010.01.015.
Gansterer, M., Hartl, R.F., 2018. Collaborative vehicle routing: A survey. Eur. J. Oper.
Res. 268 (1), 1–12. https://doi.org/10.1016/j.ejor.2017.10.023.
Guajardo, M., Rönnqvist, M., 2016. A review on cost allocation methods in collaborative
transportation: M. Guajardo and M. Rönnqvist. Intl. Trans. in Op. Res. 23 (3),
371–392. https://doi.org/10.1111/itor.2016.23.issue-310.1111/itor.12205.
Guajardo, M., 2018. Environmental benefits of collaboration and allocation of emissions
in road freight transportation. In: Zeimpekis, V., Aktas, E., Bourlakis, M., Minis, I.
(Eds.), Sustainable Freight Transport, vol. 63. Springer, Cham, ISBN 978-3-319-
62917-9.
Guajardo, M., Rönnqvist, M., Flisberg, P., Frisk, M., 2018. Collaborative transportation
with overlapping coalitions. Eur. J. Oper. Res. 271 (1), 238–249. https://doi.org/
10.1016/j.ejor.2018.05.001.
Habibi, M.K.K., Allaoui, H., Goncalves, G., 2018. Collaborative hub location problem
under cost uncertainty. Comput. Ind. Eng. 124, 393–410. https://doi.org/10.1016/
j.cie.2018.07.028.
Hacardiaux, T., Tancrez, J.-Sébastien., 2020. Assessing the environmental benefits of
horizontal cooperation using a location-inventory model. Cent Eur J Oper Res 28
(4), 1363–1387. https://doi.org/10.1007/s10100-018-0599-7.
Hernández, S., Peeta, S., 2014. A carrier collaboration problem for less-than-truckload
carriers: characteristics and carrier collaboration model. Transportmetrica A:
Transport Science 10 (4), 327–349. https://doi.org/10.1080/
23249935.2013.766279.
Hernández, S., Peeta, S., 2011. Centralized Time-Dependent Multiple-Carrier
Collaboration Problem for Less-Than-Truckload Carriers. Transp. Res. Rec. 2263
(1), 26–34. https://doi.org/10.3141/2263-04.
Hernández, S., Peeta, S., Kalafatas, G., 2011. A less-than-truckload carrier collaboration
planning problem under dynamic capacities. Transportation Research Part E:
Logistics and Transportation Review 47 (6), 933–946. https://doi.org/10.1016/j.
tre.2011.03.001.
Hernández, S., Unnikrishnan, A., Awale, S.S., 2012. Centralized Carrier Collaboration
Multihub Location Problem for Less-Than-Truckload Industry: Hybrid Hub-and-
Spoke Network. Transp. Res. Rec. 2269 (1), 20–28. https://doi.org/10.3141/2269-
03.
Jesus Gonzalez-Feliu, Joëlle Morana, Josep-Maria Salanova Grau, Tai-Yu Ma. Design
and scenario assessment for collaborative logistics and freight transport systems.
International Journal of Transport Economics, 2013, pp.207-240. Ffhalshs-
00844705ff
Jesus Gonzalez-Feliu. Costs and benefits of logistics pooling for urban freight
distribution: scenario simulation and assessment for strategic decision support.
Seminario CREI, Nov 2011, Rome, Italy.
Kuyzu, G., 2017. Lane covering with partner bounds in collaborative truckload
transportation procurement. Comput. Oper. Res. 77, 32–43. https://doi.org/
10.1016/j.cor.2016.07.018.
Leitner, R., Meizer, F., Prochazka, M., Sihn, W., 2011. Structural concepts for horizontal
cooperation to increase efficiency in logistics. CIRP J. Manuf. Sci. Technol. 4 (3),
332–337. https://doi.org/10.1016/j.cirpj.2011.01.009.
Li, J., Cai, X., Zeng, Y., 2016. Cost allocation for less-than-truckload collaboration
among perishable product retailers. OR Spectrum 38 (1), 81–117. https://doi.org/
10.1007/s00291-015-0424-9.
Liu, R., Jiang, Z., Fung, R.Y.K., Chen, F., Liu, X., 2010a. Two-phase heuristic algorithms
for full truckloads multi-depot capacitated vehicle routing problem in carrier
collaboration. Comput. Oper. Res. 37 (5), 950–959. https://doi.org/10.1016/
j.cor.2009.08.002.
Liu, R., Jiang, Z., Liu, X., Chen, F., 2010b. Task selection and routing problems in
collaborative truckload transportation. Transportation Research Part E: Logistics
and Transportation Review 46 (6), 1071–1085. https://doi.org/10.1016/j.
tre.2010.05.003.
Lozano, S., Moreno, P., Adenso-Díaz, B., Algaba, E., 2013. Cooperative game theory
approach to allocating benefits of horizontal cooperation. Eur. J. Oper. Res. 229 (2),
444–452. https://doi.org/10.1016/j.ejor.2013.02.034.
Makhloufi, R., Cattaruzza, D., Meunier, F., Absi, N., Feillet, D., 2015. Simulation of
Mutualized Urban Logistics Systems with Real-time Management. Transp. Res.
Procedia 6, 365–376. https://doi.org/10.1016/j.trpro.2015.03.027.
Molenbruch, Y., Braekers, K., Caris, A., 2017. Benefits of horizontal cooperation in dial-
a-ride services. Transp. Res.Part E: Logistics and Transportation Review 107,
97–119. https://doi.org/10.1016/j.tre.2017.09.001.
Montoya-Torres, J.R., Muñoz-Villamizar, A., Vega-Mejía, C.A., 2016. On the impact of
collaborative strategies for goods delivery in city logistics. Production Planning &
Control 27 (6), 443–455. https://doi.org/10.1080/09537287.2016.1147092.
Morana, J., Gonzalez-Feliu, J., Semet, F., 2014. Urban Consolidation and Logistics
Pooling: Planning, Management and Scenario Assessment Issues, in: Gonzalez-Feliu,
J., Semet, F., Routhier, J.-L. (Eds.), Sustainable Urban Logistics: Concepts, Methods
and Information Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, pp.
187–210. https://doi.org/10.1007/978-3-642-31788-0_10
Moutaoukil, A., Neubert, G., Derrouiche, R., 2015. Urban Freight Distribution: The
impact of delivery time on sustainability. IFAC-PapersOnLine 48 (3), 2368–2373.
https://doi.org/10.1016/j.ifacol.2015.06.442.
Moutaoukil, A., Derrouiche, R., Neubert, G., & Saint-Etienne, E. S. C. (2013). Modeling a
pooling logistics strategy for agri-food SMEs Acknowledgments. In 14th IFIP WG 5.5
Work. Conf. Virtual Enterp. PRO-VE 2013 (pp. 2-3).
Muñoz-Villamizar, A., Quintero-Araújo, C.L., Montoya-Torres, J.R., Faulin, J., 2019a.
Short- and mid-term evaluation of the use of electric vehicles in urban freight
transport collaborative networks: a case study. International Journal of Logistics
Research and Applications 22 (3), 229–252. https://doi.org/10.1080/
13675567.2018.1513467.
Muñoz-Villamizar, A., Santos, J., Montoya-Torres, J.R., Velázquez-Martínez, Josué.C.,
2020. Measuring environmental performance of urban freight transport systems: A
case study. Sustainable Cities and Society 52, 101844. https://doi.org/10.1016/j.
scs.2019.101844.
Muñoz-Villamizar A., Solano-Charris E.L., Santos J., Montoya-Torres J.R.2019b.
Measuring Environmental Impact of Collaborative Urban Transport Networks: A
Case Study. In: Paternina-Arboleda C., Voß S. (eds) Computational Logistics. ICCL
2019. Lecture Notes in Computer Science, vol 11756
Martin, M., Tanguy, C., 2019. La mutualisation des activités logistiques: une application
àlafilière vitivinicole bourguignonne. Logistique & Management 27 (2), 72–87.
https://doi.org/10.1080/12507970.2019.1573649.
Nadarajah, S., Bookbinder, J.H., 2013. Less-Than-Truckload carrier collaboration
problem: modeling framework and solution approach. J Heuristics 19 (6),
917–942. https://doi.org/10.1007/s10732-013-9229-7.
Nataraj, S., Ferone, D., Quintero-Araujo, C., Juan, A.A., Festa, P., 2019. Consolidation
centers in city logistics: A cooperative approach based on the location routing
problem., 393–404. https://doi.org/10.5267/j.ijiec.2019.1.001.
Ouhader, H., El Kyal, M., 2017. Combining Facility Location and Routing Decisions in
Sustainable Urban Freight Distribution under Horizontal Collaboration: How Can
Shippers Be Benefited?. Mathematical Problems in Engineering 2017, 1–18. https://
doi.org/10.1155/2017/8687515.
Ouhader, H., kyal, M.E., 2020. Assessing the economic and environmental benefits of
horizontal cooperation in delivery: Performance and scenario analysis. 10.5267/j.
uscm 303–320. https://doi.org/10.5267/j.uscm.2019.12.001
Özener, O.Ö., Ergun, Ö., Savelsbergh, M., 2013. Allocating Cost of Service to Customers
in Inventory Routing. Oper. Res. 61 (1), 112–125. https://doi.org/10.1287/
opre.1120.1130.
Özener, O.Örsan., Ergun, Özlem, Savelsbergh, M., 2011. Lane-Exchange Mechanisms for
Truckload Carrier Collaboration. Transportation Science 45 (1), 1–17. https://doi.
org/10.1287/trsc.1100.0327.
Pan, S., Ballot, E., Fontane, F., 2013. The reduction of greenhouse gas emissions from
freight transport by pooling supply chains. Int. J. Prod. Econ. 143 (1), 86–94.
https://doi.org/10.1016/j.ijpe.2010.10.023.
Pan, S., Ballot, E., Fontane, F., Hakimi, D., 2014. Environmental and economic issues
arising from the pooling of SMEs’supply chains: case study of the food industry in
western France. Flex Serv Manuf J 26 (1-2), 92–118. https://doi.org/10.1007/
s10696-012-9162-3.
Pan, S., Trentesaux, D., Ballot, E., Huang, G.Q., 2019. Horizontal collaborative transport:
survey of solutions and practical implementation issues. Int. J. Prod. Res. 57 (15-
16), 5340–5361. https://doi.org/10.1080/00207543.2019.1574040.
Pérez-Bernabeu, E., Juan, A.A., Faulin, J., Barrios, B.B., 2015. Horizontal cooperation in
road transportation: a case illustrating savings in distances and greenhouse gas
emissions. Intl. Trans. in Op. Res. 22 (3), 585–606. https://doi.org/10.1111/
itor.12130.
Quintero-Araujo, C.L., Gruler, A., Juan, A.A., 2016. Quantifying Potential Benefits of
Horizontal Cooperation in Urban Transportation Under Uncertainty: A Simheuristic
Approach. In: Luaces, O., Gámez, J.A., Barrenechea, E., Troncoso, A., Galar, M.,
Quintián, H., Corchado, E. (Eds.), Advances in Artificial Intelligence. Springer
International Publishing, Cham, pp. 280–289. https://doi.org/10.1007/978-3-319-
44636-3_26.
Quintero-Araujo, C.L., Gruler, A., Juan, A.A., Faulin, J., 2019. Using horizontal
cooperation concepts in integrated routing and facility-location decisions. Intl.
Trans. in Op. Res. 26 (2), 551–576. https://doi.org/10.1111/itor.2019.26.issue-
210.1111/itor.12479.
Ruel, S., 2019. Freins à la mutualisation horizontale: enseignements d’une étude de cas
dans le secteur agroalimentaire. Logistique & Management 27 (2), 119–131.
https://doi.org/10.1080/12507970.2019.1597647.
Sanchez, M., Pradenas, L., Deschamps, J.-C., Parada, V., 2016. Reducing the carbon
footprint in a vehicle routing problem by pooling resources from different
companies. Netnomics 17 (1), 29–45. https://doi.org/10.1007/s11066-015-9099-
2.
Soysal, M., Bloemhof-Ruwaard, J.M., Haijema, R., van der Vorst, J.G.A.J., 2018.
Modeling a green inventory routing problem for perishable products with horizontal
collaboration. Comput. Oper. Res. 89, 168–182. https://doi.org/10.1016/
j.cor.2016.02.003.
SOeS, 2010. Chiffres clés du climat France et Monde-Edition 2010.Commissariat général
au développement durable-Service de l’observation et des statistiques. Paris
Sprenger, R., Mönch, L., 2014. A decision support system for cooperative transportation
planning: Design, implementation, and performance assessment. Expert Syst. Appl.
41 (11), 5125–5138. https://doi.org/10.1016/j.eswa.2014.02.032.
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
14

Stellingwerf, H.M., Kanellopoulos, A., Cruijssen, F.C.A.M., Bloemhof, J.M., 2019.
Fair gain allocation in eco-efficient vendor-managed inventory cooperation.
J. Cleaner Prod. 231, 746–755. https://doi.org/10.1016/j.
jclepro.2019.05.232.
Stellingwerf, H.M., Laporte, G., Cruijssen, F.C.A.M., Kanellopoulos, A., Bloemhof, J.M.,
2018. Quantifying the environmental and economic benefits of cooperation: A case
study in temperature-controlled food logistics. Transportation Research Part D:
Transport and Environment 65, 178–193. https://doi.org/10.1016/j.
trd.2018.08.010.
Tang, X., Lehuédé, F., Péton, O., 2016. Location of distribution centers in a multi-period
collaborative distribution network. Electronic Notes in Discrete Mathematics 52,
293–300. https://doi.org/10.1016/j.endm.2016.03.039.
Ubeda, S., Arcelus, F.J., Faulin, J., 2011. Green logistics at Eroski: a case study. Int. J.
Prod. Econ. 131 (1), 44–51.
Vanovermeire, C., Sörensen, K., 2014. Measuring and rewarding flexibility in
collaborative distribution, including two-partner coalitions. Eur. J. Oper. Res. 239
(1), 157–165. https://doi.org/10.1016/j.ejor.2014.04.015.
Vaziri, S., Etebari, F., Vahdani, B., 2019. Development and optimization of a horizontal
carrier collaboration vehicle routing model with multi-commodity request
allocation. J. Cleaner Prod. 224, 492–505. https://doi.org/10.1016/j.
jclepro.2019.02.043.
Verdonck, L., Beullens, P., Caris, A., Ramaekers, K., Janssens, G.K., 2016. Analysis of
collaborative savings and cost allocation techniques for the cooperative carrier
facility location problem. Journal of the Operational Research Society 67 (6),
853–871.
Verdonck, L., Caris, AN, Ramaekers, K., Janssens, G.K., 2013. Collaborative Logistics
from the Perspective of Road Transportation Companies. Transport Reviews 33 (6),
700–719. https://doi.org/10.1080/01441647.2013.853706.
Wang, X., Kopfer, H., 2015. Rolling horizon planning for a dynamic collaborative
routing problem with full-truckload pickup and delivery requests. Flex Serv Manuf J
27 (4), 509–533. https://doi.org/10.1007/s10696-015-9212-8.
Wang, X., Kopfer, H., 2014. Collaborative transportation planning of less-than-truckload
freight: A route-based request exchange mechanism. OR Spectrum 36 (2), 357–380.
https://doi.org/10.1007/s00291-013-0331-x.
Wang, X., Kopfer, H., Gendreau, M., 2014. Operational transportation planning of
freight forwarding companies in horizontal coalitions. Eur. J. Oper. Res. 237 (3),
1133–1141. https://doi.org/10.1016/j.ejor.2014.02.056.
Wang, Y., Zhang, S., Guan, X., Peng, S., Wang, H., Liu, Y., Xu, M., 2020. Collaborative
multi-depot logistics network design with time window assignment. Expert Syst.
Appl. 140, 112910. https://doi.org/10.1016/j.eswa.2019.112910.
Wang, Y., Ma, X., Li, Z., Liu, Y., Xu, M., Wang, Y., 2017af. Profit distribution in
collaborative multiple centers vehicle routing problem. J. Cleaner Prod. 144,
203–219. https://doi.org/10.1016/j.jclepro.2017.01.001.
Wang, Y., Ma, X., Liu, M., Gong, K., Liu, Y., Xu, M., Wang, Y., 2017be. Cooperation and
profit allocation in two-echelon logistics joint distribution network optimization.
Appl. Soft Comput. 56, 143–157. https://doi.org/10.1016/j.asoc.2017.02.025.
Wang, Y., Peng, S., Xu, C., Assogba, K., Wang, H., Xu, M., Wang, Y., 2018ad. Two-
echelon logistics delivery and pickup network optimization based on integrated
cooperation and transportation fleet sharing. Expert Syst. Appl. 113, 44–65. https://
doi.org/10.1016/j.eswa.2018.06.037.
Wang, Y., Zhang, J., Assogba, K., Liu, Y., Xu, M., Wang, Y., 2018bab. Collaboration and
transportation resource sharing in multiple centers vehicle routing optimization
with delivery and pickup. Knowl.-Based Syst. 160, 296–310. https://doi.org/
10.1016/j.knosys.2018.07.024.
Wang, Y., Zhang, S., Assogba, K., Fan, J., Xu, M., Wang, Y., 2018cc. Economic and
environmental evaluations in the two-echelon collaborative multiple centers vehicle
routing optimization. J. Cleaner Prod. 197, 443–461. https://doi.org/10.1016/j.
jclepro.2018.06.208.
Weng, K., Xu, Z., 2014. Flow Merging and Hub Route Optimization in Collaborative
Transportation. J. Appl. Math. 2014, 1–8. https://doi.org/10.1155/2014/621487.
Wick, C., Klumpp, M., Kandel, C., 2011. Shapley value simulation for allocating GHG
emission savings due to logistics pooling within ecr cooperations 6.
Xu, X., Pan, S., Ballot, E., 2012. Allocation of Transportation Cost & CO2 Emission in
Pooled Supply Chains Using Cooperative Game Theory. IFAC Proceedings Volumes
45, 547–553. https://doi.org/10.3182/20120523-3-RO-2023.00078.
Yilmaz, O., Savasaneril, S., 2012. Collaboration among small shippers in a
transportation market. Eur. J. Oper. Res. 218, 408–415. https://doi.org/10.1016/
j.ejor.2011.11.018.
A. Aloui et al. Transportation Research Interdisciplinary Perspectives 9 (2021) 100291
15