Next Article in Journal
Sustainability, Corporate Social Responsibility, and Performance in the Spanish Wine Sector
Previous Article in Journal
Service Design Management and Organizational Innovation Performance
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

The Adoption of Green Vehicles in Last Mile Logistics: A Systematic Review

Faculty of Economics, Universitas Mercatorum, Piazza Mattei, 10, 00186 Rome, Italy
Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy
Department of Political Science, Roma Tre University, Via Gabriello Chiabrera 199, 00145 Rome, Italy
Department of Logistics, Molde University College, P.O. Box 2110, 6402 Molde, Norway
Author to whom correspondence should be addressed.
Sustainability 2021, 13(1), 6;
Received: 4 December 2020 / Revised: 17 December 2020 / Accepted: 18 December 2020 / Published: 22 December 2020
(This article belongs to the Section Sustainable Transportation)


Widespread adoption of green vehicles in urban logistics may contribute to the alleviation of problems such as environmental pollution, global warming, and oil dependency. However, the current adoption of green vehicles in the last mile logistics is relatively low despite many actions taken by public authorities to overcome the negative externalities of distributing goods in cities. This paper presents a comprehensive literature review on studies investigating the adoption of green vehicles in urban freight transportation, paying specific attention to e-commerce. To shed light on the adoption of green vehicles in city logistics, the paper conducts a systematic review of the empirical literature on the topic. The 159 articles reviewed were classified into the following: (a) Optimization and scheduling (67 papers); (b) policy (55 papers); (c) sustainability (37 papers). Among the 159 articles, a further selection of 17 papers dealing with e-commerce, i.e., studies that highlight the most relevant aspects related to the integration of green vehicles in e-commerce urban logistics, was performed. Our findings indicate that green vehicles are competitive in urban deliveries characterized by frequent stop-and-go movements and low consolidation levels while incentives are still necessary for their adoption. The use of autonomous vehicles results the most promising and challenging solution for last-mile logistics.

1. Introduction

The demand for urban freight transportation has increased considerably owing to urbanization and demographic growth, along with the increased diffusion of e-commerce, new management principles (e.g., just-in-time), and the introduction of new pervasive technologies [1].
The ever-increasing trade volumes of e-commerce, which still showed a worldwide growth rate of 23.3% in 2018 and have drastically increased during the COVID-19 pandemic, have led to huge parcel volumes that need to be delivered each day especially in large urban areas. More and more delivery vehicles are required to bridge the last mile towards the customers [2].
Due to the increasing number of goods vehicle movements in urban areas, modern cities are facing congestion, lack of public space, air pollution, noise, etc., which are reducing life quality. In fact, as mentioned by Janjevic et al. [3], urban logistics operations have a considerable impact on three different aspects of sustainability: Economic (e.g., efficiency and costs of deliveries), environmental (e.g., CO2 emissions), and social (e.g., congestion).
To cope with this situation, city municipal administrations are implementing several sustainability initiatives such as urban consolidation centers, intelligent fleet management systems, use of green vehicles, and putting in place various freight regulations such as vehicle sizing, access timing restrictions, and congestion pricing [4,5,6]. The efforts made by public authorities are also in line with the goals that the EU is pursuing with respect to this sector as indicated in the white paper 2011 on transport [7] where it is stated that a reduction of at least 60% of GHGs by 2050 with respect to 1990 is required from the transport sector. Moreover, in the second point of the white paper 2011 it is affirmed that, in order to reach the energy and environmental target set, technological innovations able to raise vehicle energy efficiency are required along with the implementation of new technologies and engines for clean road transport. Among these concepts, this paper focuses on green vehicles adoption due to its relevance in a context where the increase in the e-commerce market and the rise of on-demand logistics imply poor efficiency and might cause additional negative environmental consequences.
The innovations introduced have been disruptive, especially with respect to new engine technologies and driverless vehicles [8,9]. The European Commission considers green vehicles (a ‘green’ vehicle is defined as one with emissions intensity that does not exceed 120 g of CO2 emissions per km [10]) relevant for decarbonizing transport, reducing air pollution, and increasing system efficiency. Green vehicles such as electric vehicles (EVs), autonomous vehicles (AVs), and drones will be evaluated in terms of their current integration in city logistics.
The paper aims at providing a literature review concerning the introduction of green vehicles in the last mile distribution to identify the main issues and challenges. Moreover, this study proposes some research directions and approaches for the advancement of sustainable freight transportation in urban logistics.
As observed by Hu et al. [11], achieving green logistics goals relies on two dominant strategies: (1) To promote the introduction of green vehicles; (2) to develop and implement policy measures and regulations to reduce the negative effect of city logistics. The research presented in this paper attempts to perform a systematic review on the first of these aspects, distinguishing from previous similar works for this specific focus. The state of the art and trends of the growing research on the integration of green vehicles in urban freight distribution are explored.
A recent review reporting a comprehensive analysis from a broad perspective of the research on green logistics has been proposed by Ren et al. [12]. They found that the number of papers published in this field has been rapidly growing in the past two years as well as the multitudinous research directions.
An integrated view of the literature published on the research area of last mile logistics, has been also proposed by Olsson et al. (2019) [13]. They report four previous systematic reviews focusing on specific elements within the area of last mile logistics [14,15,16,17]. Among these studies, only the review proposed by de Oliveira et al. [17] focused on vehicles alternatives for last mile distribution in urban freight.
In this light, the purpose of this study is to update and consolidate the current understanding with respect to the adoption of EVs, AVs, and drones in urban logistics, shedding light on the e-commerce sector.
The reminder of the paper is organized as follows. Section 2 outlines the methodology used to perform the literature review, with results presented in Section 3. Section 4 provides an overview of the most relevant aspects related to the integration of green vehicles in e-commerce urban logistics. Section 5 concludes, summarizes the paper, and provides some suggested future research paths.

2. Methodology

The literature selection procedure is shown in Figure 1. The keywords selected in the initial thematic search were: ‘autonomous vehicles’, ‘electric vehicles’, ‘green vehicles’, ‘last mile logistics’, ‘urban logistics’, ‘city logistics’, and ‘urban freight’. The query string used for database searches was: ((«autonomous vehicles» OR «electric vehicles» OR «green vehicles» OR «drones») AND («last mile logistics» OR «urban logistics» OR «city logistics» OR «urban freight»)).
To ensure homogeneous quality levels of the contribution investigated, we have only included papers published in peer-reviewed journals in English. Consequently, the literature review excludes sources falling outside the perimeter of peer-reviewed articles available online (e.g., conference proceedings, book chapters, and white papers) or not written in English, since this represents the dominant language in last mile logistics research (box C in Figure 1). Although some papers were selected since they matched the keywords used in the search process, after reading the abstracts we excluded 48 since the key topic was not aligned with the research focus of our paper (box D in Figure 1). As mentioned in the Introduction, the paper aims at improving the understanding of the underlying motivations with respect to green vehicles adoption in last mile logistics.
A final selection of 159 papers was included in this review. Among these articles, a further selection of 17 papers which provide significant insights into the introduction of green vehicles in urban freight distribution, are discussed in Section 4.

3. Results

Urban logistics is a research area that encompasses different fields ranging from engineering to transportation policy and human studies. This work proposes a systematic literature review of articles belonging to different research areas. A classification of the papers reviewed is introduced to better evaluate and discuss their added value and their potential integration in the future research directions about the diffusion of green vehicles in e-commerce and urban logistics. Three main categories were identified: Optimization and scheduling (O), policy (P), and sustainability (S). A similar categorization was also found in other literature review papers [18,19]. The following criteria were employed for the classification of the papers.
  • Optimization and scheduling: The works investigating operation research problems were included in this category. The methodologies that are generally applied are based on mixed integer mathematical problems formulated to study vehicle routing problems and solved using algorithms aiming at minimizing delivery time, total costs of the activities, or the generated emissions. Neural networks, mathematical optimization techniques, and stochastic modeling are further characteristic applications regarding this category.
  • Policy: The works classified in this category focus on governance, planning, regulations, and incentives for innovative technologies. The topics are centered on:
    Evaluation of policy interventions effectiveness/acceptability before and/or after their deployment.
    Innovative incentivization schemes in order to facilitate the spread of ‘green vehicles’.
    Coordination among stakeholders having contrasting interests.
    Recommendations for policy or decision makers, both public and private, aiming at improving urban logistics.
The methods that are commonly applied are based on econometric or statistical analyses of empirical data acquired via questionnaires or stakeholders’ interviews.
  • Sustainability: Environmental, economic, and social sustainability considerations characterize this group. The papers included usually evaluate some logistics configurations or future scenarios comparing their economic and environmental performances. Social sustainability focuses on quality of life and conflicts concerning the use of space in urban cities. The methodologies that are often applied in this section make use of life cycle analysis, energy/fuel consumption modeling, economic analysis, or performance indicators.
Some review articles [18,19,20,21], employing a multidisciplinary approach, investigated aspects belonging to different categories (e.g., policy and sustainability). In these few cases the papers were classified following the topic that was considered characteristic and prevailing. Finally, the works about e-commerce were simply retrieved verifying that the topic was adequately dealt with in the text.
Table A1, Table A2 and Table A3 in the Appendix A summarize the classification of the main contributions of the relevant papers included in this review sorted by the number of citations; a similar approach has been already proposed by Hilmola [22]. Figure 2 plots the publication year of the works selected. It can be noted that most of the works are quite recent and belong to the 2010–2020 decade. Figure 3 shows the distribution of the works per category: 42% belongs to the optimization and scheduling group (O), 34% is related to policy (P), and 24% is sustainability centered (S). The total percentage of papers containing references to e-commerce is 11% with 5.7% belonging to the policy group (see Figure 3). Figure 4 shows a word map colored for the number of authors per country: As can be expected, the most of them are affiliated with research institutions settled in developed countries (e.g., Europe and North America). Table 1 shows the most active authors with more than three papers in the sample analyzed.

3.1. Optimization and Scheduling Group

As reported in Figure 2, the academic interests in urban freight distribution from the operation research point of view is significantly increased through time. Most of the literature focus on green vehicles routing and scheduling problem (GVRSP). This aims to minimize green-house gas (GHG) emissions in logistics systems through better planning of deliveries/pickups made by a mixed fleet of vehicles (green and traditional). The optimization techniques can be applied to several aspects capable of reducing externalities in smart logistics [14]. Cattaruzza et al. [23] provide a general overview of the most relevant studies of the GVRSP for city logistics.
In the mathematical formulations of the GVRSP the charging time is considered a downtime and the insufficiency of charging infrastructure as well as the battery limitations are considered a barrier for the integration of green vehicles in urban logistics.
Many studies propose models and applications for the optimal location of charging stations for electric freight vehicles [24,25,26]. A recent article by Cortés-Murcia et al. [27] propose a novel variant of the electric vehicle routing problem (EVRP) where the customer visits are allowed by an alternative means of transport while the EV is at a recharging station.
Recent studies have proposed models for the GVRSP considering a mixed fleet of traditional manually operated vehicles and autonomous vehicles (AVs). This represents a substantial technological advance in the field of transportation [28] and is expected to play an important role in supporting the last mile logistics within intelligent transportation systems [29]. Bucsky [30] highlights that autonomous vehicles can navigate less efficiently than human driven trucks and special drop-off places should be created since parking for a longer period and lack of human communication with other drivers could generate traffic congestion in mixed fleet scenarios.
To reduce excessive road traffic from last mile deliveries, Boysen et al. [31] propose the use of unmanned aerial vehicles (also known as drones) launched from trucks. The authors provided an optimization model where the truck serves both as a mobile depot for the goods and as a mobile launching platform for one or multiple drones based on the top of the truck. The en-route launch of the drone, i.e., the drone is launched from the truck and later re-joins the truck, and its relative optimization model has been also proposed by [32,33,34].
Since the standard GVRSP does not allow multiple trips to the depot, Dorling et al. [35] suggests solving drone delivery problems with a multitrip vehicle routing problem to overcome the difficulties caused by the short operating ranges of drones. Other studies [36,37] are aimed at finding the best spatial distribution of facilities of launch and recharge stations for a drone delivery system.
It is worth mentioning that recent and comprehensive review on GVRSP from various perspectives can be found in Moghdani et al. [38] and Ferreira et al. [39].

3.2. Policy

One of the main issues regarding policy applied to urban freight logistics is to develop methods to support decision makers for the ex-ante evaluation of possible alternative solutions taking into account the interests and preferences of all different stakeholders involved [40,41]. The integration and coordination of the different stakeholders (public and private) is a valuable solution to improve urban freight logistics reducing costs and negative environmental externalities [42,43]. The creation of urban consolidation centers (UCCs), for example, can result in a great commitment for the stakeholders involved and the interplay among them is often characterized by conflicting objectives [44]. As observed by Cleophas et al. [45], the key challenge to developing sustainable urban logistics is collaboration between businesses, logistics service providers, citizens, and the public sector.
The horizontal and vertical collaboration among courier companies in load consolidation can provide consistent benefits for all the subjects involved [46,47] also reducing urban congestion and pollution problems. Subcontracting and partnership should be preferred over creating new on-demand dedicated services [48] that would imply a huge investment on warehouse facilities and large delivery fleets. The use of sub-contractors is particularly desirable in urban contexts with strong regulatory constraints, high flexibility, and seasonal variations of flows. The spread of cross borders parcel distribution is boosting the cooperation of different distribution actors in the use of a common information technology platform and in the establishment of cross border settlements. The awareness of political authorities about the potential of e-commerce is growing and new regulations are developing aiming at improving the convenience of the service for customers [48].
The usage of small electric vehicles for urban deliveries is incentivized by public authorities because of their capacity for local pollution reduction [49]. For this reason, electric vehicles are often free from restrictions of access to inner city centers [5]; moreover, their ease of movement through the narrow streets of historic centers facilitates the delivery processes.

3.3. Sustainability

The claim for the reduction of negative externalities deriving from urban freight logistics is growing and the attention is focused on economic and environmental sustainability. This is strongly linked to the reduction of greenhouse gas emissions and other air pollutants in urban areas. The strategies that are often considered in order to improve the logistics system from both the environmental and costs perspectives are the reduction of the vehicles size and their electrification [14,17,50], conversion of a homogeneous fleet to a mixed one [1,51], creation of UCCs that work in combination with a mixed fleet for deliveries [14,52,53,54].
A recent solution refers to the use of crowdshipping especially for last-mile deliveries, however, its impacts mostly depend on the mode employed, length of detour, parking behavior, and daily traffic variations [55]. Gatta et al. [56] investigate a green-type of crowdshipping based on public transportation, estimating its environmental and economic impacts.
The use of light electric vehicles, drones, unmanned aerial vehicles, or cargo bikes constitute a solution that can reduce the operational energy requirements in most of the cases analyzed [52,57] even if the mileage toured is higher than that characterizing traditional vans. The capacity of new delivery vehicle types is lower, and a higher number of tours are necessary to serve the same number of customers. The spread of renewable electricity production is able to further reduce operational well-to-wheel energy consumption increasing the sustainability level of the electrification of the fleets employed for last mile deliveries [58]. The reduction of the operational energy is always coupled with a reduction of some environmental impacts, such as the amount of greenhouse gas emissions.
Life cycle assessment (LCA) applications are performed by different authors [59,60,61] to evaluate the overall benefits when the reduction of the operational energy consumption of urban freight vehicles is followed by an increase of the use of energy storage technologies or the necessity of new urban facilities. The importance of a life cycle approach is also underlined by Taefi et al. and Patella et al. [62,63]. This article, analyzing the economic sustainability of the application of electric vehicles in urban logistics, stresses the importance of long mileages and long battery warranties as the precondition for their real competitiveness in substitution with traditional internal combustion engines. Moreover, the high cost of the battery is still a strong limit for the diffusion of electric vehicles and incentives are fundamental to guarantee their economic competitiveness [37,64]. Furthermore, some literature works [65,66,67] showed a negative correlation between the reduction of greenhouse gas emissions and the life cycle costs of the delivery system: only in some specific cases it is possible to obtain a win-win scenario.
The presence of battery charging restrictions and reduced vehicle speed cause the design of shorter routes, the increase of the total daily mileage of tours, and the multiplication of urban facility requirements (e.g., UCC, satellites, micro-consolidation centers, charging stations). Melo et al. [68] provided an example showing that cargo bikes, which have a strong potential in emission and environmental impacts reduction, need to establish an urban facility in city centers to operate adequately. The reduction of the operational energy use and of the related environmental impacts linked to the delivery vehicles causes an increase in the embodied components characterizing the supporting logistics facilities. Moreover, the increase in the number of warehouses causes an increment of the energy required for their operation. Stalaroff et al. [61] show that, in some cases, the proliferation of new urban depots and facilities can result in an increase of the overall life cycle environmental burdens so that their implementation should be carefully evaluated. The application of LCA is strongly recommended since the methodology can evaluate the right compromise between environmental impacts belonging to different life cycle phases avoiding burden shifting. Moreover, the localization of depots in the inner parts of the cities is becoming complicated and scarcely affordable because of increasing land use values [69]. The sprawl of freight facilities in the suburbs or in the external parts of the cities, where the price of land is lower, contributes to increasing the length distances of the journeys. The high competition for land use in the core of the cities also involves the reallocation of space for social activities, green mobility, public transport, and pedestrians. The increasing congestion on the streets is, in fact, reducing the space required for socialization due to the deterioration of the quality of livable areas. Furthermore, the absence of bike lanes and pedestrian safe paths hinders sustainable mobility and provokes road accidents [70]. Dedicated infrastructure for walking, cycling, and recreational activities are necessary in modern sustainable cities and sustainable lifestyles are demanding a reduction of the amount of space for traffic [71].

4. The Adoption of Green Vehicles on E-Commerce City Logistics

Different studies [61,72,73] showed that e-commerce is more sustainable than retail pick-up or traditional shopping. In a life cycle perspective, Weber et al. [74] showed that e-commerce delivery systems are characterized by 30% lower primary energy consumption and CO2 emissions when considering the warehouses energy demand, energy used in retail stores and headquarters, electricity used for home computer shopping activities and data centers, fuel necessary for transportation (from manufacturer to wholesale warehouse, from the wholesale warehouse to the retail stores or distribution centers, for last mile deliveries), and packaging. The last mile delivery represents, on average, the most important contribution with respect to total CO2 emissions associated with e-commerce (32%). The wholesale warehousing energy use represents the second contribution (31%) of the total emissions linked to the e-commerce delivery system, but a similar share is registered also for traditional retail (26%). The main difference between the two systems is linked to the increase of the packaging impacts in e-commerce and to the reduction of the energy necessary for customers’ transport to retail stores that, in traditional retail shopping, plays a preponderant role. The significant role played by last mile delivery in the definition of the global impacts linked to e-commerce activities explains why the topic attracts such attention by the scientific literature.
One of the main characteristics of e-commerce deliveries is their small size and light weight. The former property makes the consolidation of traditional diesel vans very difficult [75] and pushes towards the reduction of the size of the vehicles used for last mile activities in response to smaller, fragmented, and more frequent deliveries. Boosted by the spread of e-commerce, the number of light goods vehicles circulating in the inner city areas is increasing more than the number of heavy goods vehicles or cars [69]. The reduction of the capacity of fleet vehicles is, however, necessary for deliveries in historic centers characterized by narrow streets. Electrification is pursued since the efficiency of electric engines at low driving speeds (about 50 km/h [67]) is higher than that of fossil fuel-propelled engines due to regenerative braking. The diffusion of renewable energy generation can furthermore boost the competitiveness of green vehicles in last mile logistics both from an economic and environmental perspective. The drawback of the use of light weight green vehicles is the increase of the travelled distance as shown by different authors [47,49,76]. The higher mileages travelled by green vehicles can increase traffic congestion and its negative externalities; off-peak deliveries are suggested by different authors [49] to avoid worsening scenarios.
The possibility of choosing different delivery options is often given by online retailers with time windows that can be lower than 24 h (same-day delivery) [19]. Customers are demanding easy and fast deliveries and companies are experiencing a high pressure on delivery costs. The on-demand market is characterized by several independent players aiming at increasing the sales of their products and gaining market shares from their competitors also accepting to operate without covering the cost of last mile delivery operations. In order to increase online sales and meet customers’ expectations, retailers guarantee ‘free’ delivery or offer different delivery options with a wide range of time windows. The guaranty of narrow time windows as a delivery option can furthermore stress the system reducing the possibility of demand aggregation and lowering the consolidation level of freight vehicles. The lower aggregation of demand produces an increase of the number of vehicles in circulation, of the distance travelled and, consequently, of the operational energy requirement and related environmental burdens [69,76]. In these cases, green vehicle or drone deliveries, replacing vans that are poorly consolidated, can play an important role in optimizing the system from both an environmental and economic perspective. However, even in case of drone usage, Dorling et al. [35] showed an inverse exponential relationship between the time limit and the total cost of the deliveries.

5. Conclusions

This systematic literature review showed that academic interest in the adoption of green vehicles in urban logistics has significantly increased as documented by the increasing number of publications on this topic in recent years.
This study confirmed that city logistics is intertwined with many aspects belonging to different fields related to urban planning, especially when considering sustainability. Three main categories were identified with respect to the various perspectives of the papers dealing with urban logistics: Optimization and scheduling (O), policy (P), and sustainability (S).
Green vehicles routing problems (GVRP), which represent the most relevant contribution of operations research to green logistics, are included in the “optimization and scheduling” category. Our analysis confirmed findings from Moghdani et al. [38], showing that the number of research studies interested in GVRP has grown rapidly.
Policy actions aiming at increasing the sustainability level of the system and the collaboration between stakeholders are discussed in the second category. The collaboration has been identified by many authors (e.g., Gatta et al. [40], Cleophas et al. [45]) as the key factor to increase urban freight sustainability.
The third category includes studies about environmental (employing LCA methodologies), economic, and social sustainability. The category subdivision resumes three different approaches developed by the literature analyzed in the study of the adoption of green vehicles in last mile logistics.
The issues connected to the rise of e-commerce are addressed by papers classified in each category identified. E-commerce is more sustainable than traditional retail shopping [61,72,73,74]: last mile delivery and warehouse management represent the trickiest phases but an important reduction of the environmental impacts linked to customers travelling to retail stores, that in traditional shopping are the highest, can be reached. However, the effort in market shares acquisition by independent online sellers, the guaranty of narrow delivery time windows options, and the small size of the largest part of delivered items challenge the consolidation level of traditional diesel vehicles stressing the system and increasing its negative externalities [19,76]: The higher resulting number of vehicles in circulation and the higher distance travelled increase the energy use and the connected environmental burdens related to the delivery phase. In order to respond to this challenge, the adoption of green vehicles (lightweight electric vehicles, cargo bikes, unmanned aerial vehicles (UAVs), etc.) are being evaluated because of their potential to reduce the environmental burdens connected to last mile delivery, particularly in urban environments [52,57,58,68]. Their adoption is also incentivized by administration authorities to control local pollution [5]. The life cycle economic and environmental sustainability of the process is however still debated since the high impacts of batteries [62] and the necessity of new depots and facilities characterized by not negligible energy requirements [61,68,77] contribute to worsen this scenario.
The development of vehicle technologies such as electric vehicles, connected and automated vehicles, and drones, as well as the introduction of new business models for freight transportation, requires new strategies to control the urban transportation system (freight and travelers) effectively and globally. There is a need for a system-based vision, and therefore researchers are encouraged to chart new territory in the literature by exploring ways in which overall urban transport sustainability can be enhanced. Even more important, whenever innovative solutions or disruptive changes, such as those related to the introduction of green vehicles, are tested and implemented, accurate behavioral analyses based on stated preference methods are needed to investigate stakeholders’ acceptability and reactions.
As to the limitations of the research presented in this paper, this review is subject to the potential omission of relevant studies which have not been covered by the keywords. Moreover, the search excludes records which may potentially be relevant such as books, chapters, conference proceedings, editorials, reports, and articles not in English.

Author Contributions

Conceptualization, S.M.P. and V.G.; investigation, S.M.P. and G.G.; methodology, S.M.P. and G.G.; supervision, V.G., E.M. and S.C.; writing—original draft, S.M.P., G.G., V.G. and E.M.; writing—review and editing, S.M.P., G.G., V.G. and E.M. All authors have read and agreed to the published version of the manuscript.


This work was supported by the Roma Tre University (Research Development Plan: Action 4) research project: ShareLab—Sharing Economy in a Living Lab. Edoardo Marcucci and Valerio Gatta would like to acknowledge the funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 861598 for the LEAD project (

Conflicts of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.


EVsElectric vehicles
LCALife cycle analysis
UCCUrban freight consolidation center
UDCUrban distribution center
UAVUnmanned aerial vehicle
GVRSPGreen vehicles routing and scheduling problem

Appendix A

Table A1. Papers included in “optimization and scheduling” category by the proposed review (sorted by citations as of June 2020).
Table A1. Papers included in “optimization and scheduling” category by the proposed review (sorted by citations as of June 2020).
ReferencePub. YearCitationsCategoryMain Topic and Findings
Crainic et al. [78]2009556OTwo-echelon, synchronized, scheduled, multi-depot, multi-tour heterogeneous vehicle routing problem with time window: mathematical formulation.
Crainic et al. [79]2004483OModel for the optimization of freight transport in congested urban areas (e.g., Rome).
Murray and Chu [80]2015360OVehicle routing problem with a UAV working in parallel with a truck.
Ropke and Cordeau [81]2009351OPick-up and delivery problem with time window: mathematical formulation and solution methodology.
Dorling et al. [35]2017292OVehicle routing problem considering drones delivery.
Taniguchi and Van Der Heijden [82]2000258OVehicle routing and scheduling problem with time windows combined with a dynamic traffic simulation model to assess the performance of city logistics initiatives.
Boyer et al. [83]2009216OOptimization of the last mile delivery: effects of density and time-windows.
Cattaruzza et al. [23]2017148OReview about “Vehicle Routing Problem”.
Ha et al. [32]2018133OTravelling salesman problem with drones minimizing costs and delivery times.
Ćirović et al. [84]2014109OVehicle routing problem of a mixed fleet of green and traditional vehicles. The optimization is performed from an environmental and economic perspective.
Motraghi and Marinov [85]201289OLogistics optimization of freight train networks: a case study in Newcastle.
Ehmke et al. [86]201269OFloating car data to optimize tour times.
Juan et al. [87]201668OVehicle routing problem considering electric vehicle autonomy.
Fatnassi et al. [88]201561OTransportation problem of a dynamic personal rapid transit and freight rapid transit solution.
Devari et al. [89]201759OBenefits of crowdsourcing in last mile delivery.
Gentile and Vigo [90]201356ONew models to describe generation and distribution of freight movements in urban areas.
Zhou et al. [91]201853OMulti-depot two-echelon vehicle routing problem with delivery options (e.g., intermediate pickup facilities).
Shavarani et al. [36]201850OHierarchical facility location problem for a drone delivery system: the model proposed finds the optimal number and location of launch and recharge stations with the objective of minimizing the total costs of the system (cost of the facilities and of the operation and maintenance of drones).
McLeod and Cherrett [92]201150OLoading bay: advance booking and control system.
Ham [93]201849OParallel drone scheduling travelling salesman problem.
Muñoz-Villamizar et al. [66]201747OHorizontal collaboration between carriers and use of electric vehicles in urban freight transport: Vehicle routing problem optimizing delivery and environmental costs. A relationship between delivery and environmental costs is drawn.
Lebeau et al. [51]201546OVehicle routing problem with time window: sizing of a mixed vehicle fleet minimizing costs.
Deutsch and Golany [94]201844OLast mile delivery problem considering lockers.
Awasthi et al. [4]201642OSelection of collaborative partners: mathematical modeling.
Park et al. [95]201642OVehicle routing problem considering collaborative logistics in last mile delivery.
Boysen et al. [96]201838OTruck-based robot delivery scheduling problem: formulation and solution.
Tavana et al. [97]201738OOptimization of the logistics operations in cross-docks considering the use of drones or of traditional vehicles.
Schiffer and Walthera [98]201836OLocation routing problem with intra-route facilities solved using an adaptive large neighborhood search algorithm.
Yu and Lam [99] 201831OOptimization of routes and charging schedules of autonomous vehicles to maximize the use of renewable energy.
Boysen et al. [31]201830OVehicle routing problem with drones minimizing the delivery duration.
Pamučar et al. [100]201630OGreen-vehicles routing model implemented in a GIS platform that aims at optimizing the parameters of the environment (e.g., CO, NOx; noise, SO2, PM), health, use of space and logistics operating costs.
Wang and Sheu [34]201929OCapacitated vehicle routing problem considering drones.
Jie et al. [101]201926OTwo-echelon capacitated vehicle routing problem with battery swapping stations.
Behiri et al. [102]201825OFreight-rail-transport-scheduling-problem: a tool for the evaluation of the feasibility in Paris.
Marinelli et al. [33]201823OTravelling salesman problem with drones: the truck can deliver and pick a drone up not only at a node but also along a route arc.
Chiang et al. [103]201921OMixed integer routing problem with unmanned aerial vehicles (UAV).
Pelletier et al. [104]201821OComprehensive mathematical model for the cost optimization of a fleet of electric freight vehicles (EVs).
Battini et al. [105]201421OOptimization problem of the last mile delivery in Haiti.
Behnke and Kirschstein [106]201720OEmission minimizing routing problem.
Ahani et al. [107]201620OOptimization procedure for vehicles fleets used in last-mile delivery.
Mbiadou Saleu et al. [108]201819OParallel drone scheduling travelling salesman problem.
Baldi et al. [109]201916ONew packing problem with bin-dependent items profits.
Kin et al. [110]201816OCost optimization of alternative systems for last mile delivery.
Franceschetti et al. [111]201716OModel for the optimization of the total cost of a vehicles freight fleet considering restrictions in the access of the city.
Deflorio and Castello [25]201715OTraffic and energy modelling of dynamic charging-while-driving systems for freight delivery services using electric vehicles.
Sopha et al. [112]201613ODynamic vehicle routing problem.
F. Wang et al. [113]201912OIntelligent freight vehicles scheduling and management system based on crowd intelligence and ride-sharing.
Firdausiyah et al. [114]201912OModelling the behavior of freight carriers and of an urban consolidation center (UCC) operation.
Karak and Abdelghany [115]201911OHybrid drone-vehicle routing problem.
Pelletier et al. [116]20199OElectric vehicle routing problem with energy consumption uncertainty.
Troudi et al. [117]20189OCapacitated vehicle routing problem with time window for the sizing of a drone fleet.
Muñoz-Villamizar et al. [65]20197OHorizontal collaboration between carriers and use of electric vehicles in urban freight transport: Vehicle routing problem optimizing delivery and environmental costs in short and medium term. A relationship between delivery and environmental costs is deployed.
Rezgui et al. [118]20196OElectric vehicles routing problem with time window considering modular electric freight vehicles.
Scherr et al. [119]20196OService network design problem for a mixed fleet of motor and autonomous vehicles: the model proposed aims at optimizing the size and mix of the fleet as well as the routing of vehicles.
Shavarani et al. [37]20196OMathematical formulation of a facility location problem aiming at minimizing the total costs of a drone-based delivery system concerned with refuel stations, warehouses, drone procurement, and transportation.
Bergqvist and Monios [120]20166OIntermodal high capacity transport.
Y. Li et al. [121]20204OVehicle routing problem that minimizes the cost of an urban distribution system.
Moeini and Salewski [122]20202OSolution of the truck-drone-autonomous vehicles routing problem.
Cortés-Murcia et al. [27]20192OElectric vehicles routing problem with time window considering the possibility of costumers’ self-collection during the charging process.
H. Li et al. [123]2020-OThe two-echelon city logistics system with on-street satellites: mathematical formulation and solution.
Schwerdfeger and Boysen [2]2020-OMobile parcel lockers for last mile distribution: an optimization model minimizing the locker fleet when satisfying all customers is proposed.
Pinto et al. [26]2019-OOptimization model addressing the design problem of a network of
charging stations to support a meal delivery service using drones.
Yu [29]2019-OOptimization in the routing and charging schedules for an autonomous vehicles logistics system using distributed renewable energy generation.
Fikar et al. [124]2018-OModel optimizing the interaction between a traditional logistics operator and a group of freelancers using cargo bikes.
Chen [125]2017-OModel for the site selection of logistics center in an e-commerce network environment.
Cavadas et al. [24]2015-OMathematical model to locate charging stations for electric freight vehicles.
Ehmke and Mattfeld [126]2010-OTime dependent optimization: management of big data about traffic.
Table A2. Papers included in “sustainability” category by the proposed review (sorted by citations as of June 2020).
Table A2. Papers included in “sustainability” category by the proposed review (sorted by citations as of June 2020).
ReferencePub. YearCitationsCategoryMain Topic and Findings
Dekker et al. [127]2012734SOperational research emphasis on environmental problems.
Browne et al. [52]2011205SEvaluation of the impacts (travelled distances, emissions, costs) of the creation of a UCC in central London that employs cargo bikes and electric vehicles for the deliveries.
Behrends et al. [128]2008178SDefinition of a new indicator able to describe the level of sustainability in urban freight transport (SUFT).
Boerkamps et al. [54]2000143SModel for the evaluation of the logical and environmental performances of three types of urban distribution systems: the traditional system and two concepts using urban distribution centers (one using vans, the other using automated underground vehicles).
Goodchild and Toy [77]201896SComparison of the emissions reduction potential of two delivery modes: one using UAV and the other using trucks.
Stolaroff et al. [61]201882SLCA of an urban logistics system employing UAV: drones consume less energy per package-km than delivery trucks but the additional energy for warehouse requirements and the longer distances per package travelled greatly increase the life-cycle impacts. Small drones are more environmentally friendly.
Arvidsson [129]201369SSustainability paradox: access restrictions in city centers might increase social sustainability aspects while decreasing economic sustainability. The use of multiple key performance indicators in urban freight distribution is recommended.
Brown and Guiffrida [73]201468SComparison of the environmental impacts of traditional shopping and e-commerce activities.
Ranieri et al. [14]201866SEvaluation about how some negative externalities of urban freight transport can increase a cost function and how some last mile logistics innovations can reduce it.
Russo and Comi [130]201663SRegressive model for the evaluation of the reduction potential of the environmental impacts (CO, NOx, SOx, PM) resulting from the implementation of some sustainable measures in urban logistics.
Melo and Baptista [68]201747SThe use of electric cargo bikes is a sustainable option for last mile delivery: when replacing all the conventional vans, the well-to-wheel CO2 emission saving is 73%.
Aditjandra et al. [131]201638SEnvironmental impacts (CO2, PM, and NOx) of heavy and light freight vehicles operating in urban areas.
Lebeau et al. [132]201536STotal cost of ownership analysis of electric light vehicles for city logistics.
Schöder et al. [49]201633SImpact of e-commerce on urban logistics sustainability.
de Oliveira et al. [17]201732SReview about literature on sustainable vehicles for last mile delivery. A trend supporting smaller and lighter electric vehicles for last mile deliveries in urban areas is discovered.
Faccio and Gamberi [53]201532SEvaluation, based on field data, of the economic and environmental sustainability deriving from the creation of a centralised platform using electric vehicles to cover the last 50 miles of distribution.
Figliozzi [59]201732SLCA of a drone.
Duarte et al. [57]201629SData analysis about the energy consumptions and environmental impacts of electric vehicles used for last mile logistics in Lisbon.
Giordano et al. [58]201821SComparison of diesel and battery electric delivery vans using emission and costs LCA. If electricity is clean and diesel vans are old, high savings can be reached in both fields from their substitution with electric ones (e.g., −93/98% of GHGs).
Park et al. [133]201819SComparison of the LCA impacts of a motorcycle delivery and of a drone delivery in urban and rural areas.
J. Wang et al. [67]201918SA negative correlation between the cost and carbon emissions under the shortest distribution routes is found. The optimized solution that minimizes the emissions of greenhouse gases and the overall operational cost is searched.
Durand et al. [134]201317SComparison of the environmental and cost performances of three scenarios for delivering to urban online shoppers.
Taefi et al. [62] 201714SA high mileage increases the cost-effectiveness of medium-duty electric vehicles. However, expensive battery replacements or quick charging can reduce the benefits significantly.
Aurambout et al. [135]201912SMarket potential and economic sustainability of using drones in last mile delivery within EU-28 countries.
de Mello Bandeira et al. [50]201910SSustainability assessment of electric light vehicles in the last mile of transportation and of postal deliveries in Rio de Janeiro.
Teoh et al. [136]201810SThe opportunity charging can significantly reduce the life cycle cost and emission of EVs used for urban freight transport operations.
Taefi [63]20168SPotential of double-shift usage in reducing the total costs of ownership of electric vehicles: EVs are competitive for high daily mileages.
Koiwanit [72]20187SLCA of drone delivery on an online shopping system.
Semanjski and Gautama [137]20196SMulti-criteria decision-making approach to achieve consensus among stakeholders on the best routing option from a sustainability perspective.
Digiesi et al. [138]20175SReview work about the strategies for the reduction of externalities in last mile logistics.
Figliozzi et al. [60]20205SLife cycle carbon emissions of an urban distribution centers.
Nenni et al. [20]20195SSustainability-based review of urban freight models: impacts related to economic (freight transport, infrastructure, employment), social (safety, security, noise) and environmental (land use, energy and emissions) fields are considered.
Bucsky [30]20182SReview of data about the economic and environmental sustainability concerning the use of autonomous vehicles for urban freight delivery.
Lebeau et al. [139]20191STotal cost of ownership of electric freight vehicles.
Marmiroli et al. [140]20201SComparative life cycle assessment of electric, compressed natural gas and diesel light-duty vehicles. Electric motors are more efficient in urban environments and the role of the batteries in embodied impacts is dominant.
Cárdenas et al. [141]2017-SAnalysis about the spatial distribution of e-commerce deliveries in Belgium and definition of an external cost index per parcel delivered that considers the costs of congestion, accidents, noise, air pollution and climate change. Average costs per parcel are higher for deliveries in rural areas.
Moore [142]2019-SInnovative scenarios for intra-city freight delivery are considered and compared with a baseline scenario (diesel based) from an energy consumption perspective.
Table A3. Papers included in “policy” category by the proposed review (sorted by citations as of June 2020).
Table A3. Papers included in “policy” category by the proposed review (sorted by citations as of June 2020).
ReferencePub. YearCitationsCategoryMain Topic and Findings
Fagnant and Kockelman [143]20151451PAutonomous vehicle in the USA: advantages barriers and policy recommendations.
Muñuzuri et al. [5]2005373PEvaluation of the solutions or initiatives that can be implemented by local administrations in order to improve freight deliveries in urban environments and expected effects.
Savelsbergh and Van Woensel [47]2016265PChallenges and opportunities in city logistics.
Cherrett et al. [144]2012196PReview article about data and information on urban freight transports. A central coordination of some activities, such as service provision, can reduce environmental impacts.
Lindholm and Behrends [145]2012191PIntegration between public and private actors to face the sustainability challenges raised by urban freight transport in Baltic countries.
Tadić et al. [146]2014170PA multi-criteria decision-making model is proposed for the selection of the city logistics concept which would be most appropriate for different participants, stakeholders, and attributes of the surroundings.
Schliwa et al. [147]2015158PThe local authorities play a key role
in creating the conditions that incentivize large logistics companies to integrate
cargo cycles into their supply chains.
Marcucci and Danielis [148]2008153PStated-preference study about the potential use of an urban freight consolidation center in Fano (Italy).
Ballantyne et al. [149]2013152PThe work aims at demonstrating
that urban freight transport planning can be improved only by involving a wider range of stakeholders.
Guerra [150]2016150PPolicy recommendations for planning the diffusion of autonomous vehicles in urban environments.
Muñuzuri et al. [151]2012144PTypical regulation schemes in Spain and reason of their obsolescence and lack of enforcement. Possible efforts towards improvement are discussed.
Lagorio et al. [18]2016104PSystematic literature review that addresses urban logistics from a logistics and management perspective. The focus is recently moving to stakeholder involvement in the decision-making process.
Gruber et al. [152]2014102PElectric cargo bikes: potential market, organization of the current market, how they are perceived by bike and car messengers, and what factors drive their willingness to use them.
Pelletier at al. [21]201695PReview article about goods distribution with electric vehicles: the technological and market background are addressed.
Russo and Comi [153]201189PModel that aims at supporting the ex-ante assessment of city logistics measures simulating the choices of each decision-maker involved in response to policy measures.
Ducret [48]201486PAnalysis of the changes in European courier, express and parcel sector: the rise of cross-border e-commerce.
Morganti and Gonzalez-Feliu [154]201579PThe case study of the urban distribution center of Parma (Food Hub): the role of public actors in defining adequate policy measures.
Taniguchi et al. [76]200374PMathematical computer-based models for the planning of city logistics schemes and predicting their effects.
Allen et al. [69]201866PChallenges of e-commerce delivery and initiatives that retailers, parcel carriers, and city authorities, can implement to reduce the costs associated with last mile logistics.
Ajanovic and Haas [155]201666PIdentify the major impact factors for a broader dissemination of EVs in urban areas. The incentives should depend on the decarbonization potential of electricity generation.
Lenz and Riehle [156]201361PEuropean experiences of cargo bike usage for delivery in cities. The spread is possible only if public authorities play an important role in the promotion (incentives, provision of space for depots in city centers, dedicated infrastructures).
Roumboutsos et al. [157]201459PDefinition of a methodology based on the Systems of Innovation approach to examine the process by which EVs may be introduced in city logistics.
Taefi et al. [158]201649PMulti-criteria analysis of policy measures in Germany that support the adoption of electric vehicles in urban road freight transport.
Anand et al. [159]201448PAn ontology model of city logistics is proposed which includes independent city logistics entities and the relationships between them in a structured form.
Nuzzolo et al. [160]201646PComparison of the characteristics of urban freight transport and of the logistics measures being implemented in Rome, Barcelona, and Santander.
Bjerkan et al. [161]201443PCollaborative approach in urban freight transport: stakeholder evaluations about the implementation of a mobile depot and night and evening deliveries.
Yuen et al. [162]201841PAnalysis on the customers’ intention to use self-collection as a last mile delivery method and how to increase it.
Lebeau et al. [163]201641PVehicle choice behavior of transport companies and expectations: authorities should encourage the use of battery electric vehicles.
Harrington et al. [164]201637PMulti-stakeholder conceptual framework: socio-economic common interests, trade-offs, and interdependences.
Jaller et al. [165]201531PLogistics initiatives to alleviate the externalities of the freight traffic produced and attracted by large freight traffic generators.
Morganti and Browne [166]201830PTechnical and operational obstacles to the adoption of electric vans in France and the UK. Some financial and non-financial
incentives to foster the adoption of electric vans.
Perboli and Rosano [1]201926PHarmonization of the business and operational models of traditional and green logistics (mainly cycled) actors.
Mirhedayatian and Yan [167]201826PFramework to evaluate policy options supporting EVs in urban freight transport.
Allen et al. [46]201724PA “freight traffic controller” is proposed to manage the repartition of work between parcel carriers collaborating horizontally to reduce urban congestion.
Kane and Whitehead [168]201723PPolicy actions to manage the on-going transport disruptions avoiding non-optimal outcomes.
Musolino et al. [169]201922PMethodology for the evaluation of an urban distribution center location in order to pursue sustainability goals.
Ville et al. [44]201322PThe case study of the UCC of Vicenza: limits of the public policy intervention in restricting vehicle access to the city center.
Dolati Neghabadi et al. [19]201920PSystematic literature review about policy, innovative solutions, stakeholders, and sustainability aspects in city logistics.
Arvidsson et al. [170]201615PThe integration of passenger and freight transport in urban areas is a promising approach to ease the last mile problem.
De Marco et al. [75]201814PEmpirical analysis of a dataset about implemented city logistics measures: pollution, diffusion of e-commerce and gross domestic product are important drivers of city logistics deployment.
Hopkins and McCarthy [171]201614PSignificant trends in urban freight delivery and policy implications: online shopping, new technologies, and changing expectations.
Hoffmann and Prause [172]201813PNecessity of a clear regulatory framework for the usage of autonomous vehicles in last mile delivery.
Cagliano et al. [173]201713PPublic intervention is necessary to speed the diffusion of EVs: definition of an incentive scheme and evaluation of their effects.
Lebeau et al. [174]201811PMulti-criteria analysis to support administrations in choosing, involving stakeholders, the priority policy to be implemented in order to improve the sustainability of city logistics.
Christensen et al. [175]201710PCommercial sectors suitable for a shift to electric mobility: construction, human health, and other service sectors.
Kellermann et al. [176]20206PReview about the use of drones for parcel and passenger transportation: technical and regulatory problems and barriers.
Buldeo Rai et al. [177]20195PLocal authorities can facilitate the adoption of emerging measures to optimize the activities of logistics by cooperating with the private sector and by developing a harmonized, long-term vision on freight transport policy across cities and regions.
Hu et al. [178]20204PIntegration of freight logistics into urban passengers’ rail transit network: stakeholders’ characterization, variables affecting the system operations and external impacts.
Matusiewicz [179]20193PIdentify the conditions for the implementation of deliveries in the limited accessibility zone in Gdansk and the guidelines for the implementation of a new sustainable transport policy.
Skiver [180]20173PCompetition between brick-and-mortar retailers and e-commerce in guaranteeing the same-day home deliveries.
Arroyo et al. [181]20192PThe effectiveness of carbon pricing policies for promoting urban freight electrification in Madrid is low in the short term.
Monios and Bergqvist [182]20192PDescription of the transport geography of electric and autonomous vehicles for road freight transport and identification of the many unresolved issues about to emerge.
Ehrler et al. [183]2019-PPerspectives, prerequisites, and challenges for a shift to electric vehicles in the last mile logistics of grocery e-commerce.
Ørving et al. [184]2019-PPublic-private partnership to facilitate the development of commercial cargo bike use in Oslo.
Cheng and Liu [185]2016-PGovernment policies aiming at the diffusion of EVs.


  1. Perboli, G.; Rosano, M. Parcel delivery in urban areas: Opportunities and threats for the mix of traditional and green business models. Transp. Res. Part C Emerg. Technol. 2019, 99, 19–36. [Google Scholar] [CrossRef]
  2. Schwerdfeger, S.; Boysen, N. Optimizing the changing locations of mobile parcel lockers in last-mile distribution. Eur. J. Oper. Res. 2020, 285, 1077–1094. [Google Scholar] [CrossRef]
  3. Janjevic, M.; Knoppen, D.; Winkenbach, M. Integrated decision-making framework for urban freight logistics policy-making. Transp. Res. Part D Transp. Environ. 2019, 72, 333–357. [Google Scholar] [CrossRef]
  4. Awasthi, A.; Adetiloye, T.; Crainic, T.G. Collaboration partner selection for city logistics planning under municipal freight regulations. Appl. Math. Model. 2016, 40, 510–525. [Google Scholar] [CrossRef]
  5. Muñuzuri, J.; Larrañeta, J.; Onieva, L.; Cortés, P. Solutions applicable by local administrations for urban logistics improvement. Cities 2005, 22, 15–28. [Google Scholar] [CrossRef]
  6. Center of Excellence for Sustainable Urban Freight Systems Improving Freight System Performance in Metropolitan Areas: Planning Guide. Available online: (accessed on 17 July 2020).
  7. Directorate-General for Mobility and Transport (European Commission). White Paper on Transport; European Union: Brussels, Belgium, 2011; ISBN 978-92-79-18270-9. [Google Scholar]
  8. Asghari, M.; Mirzapour Al-e-hashem, S.M.J. Green vehicle routing problem: A state-of-the-art review. Int. J. Prod. Econ. 2021, 231, 107899. [Google Scholar] [CrossRef]
  9. Macrina, G.; Di Puglia Pugliese, L.; Guerriero, F.; Laporte, G. Drone-aided routing: A literature review. Transp. Res. Part C Emerg. Technol. 2020, 120, 102762. [Google Scholar] [CrossRef]
  10. Australian Department of Industry Science Energy and Resources ‘Green’ Vehicles Definition. Available online: (accessed on 17 July 2020).
  11. Hu, W.; Dong, J.; Hwang, B.; Ren, R.; Chen, Z. A Scientometrics Review on City Logistics Literature: Research Trends, Advanced Theory and Practice. Sustainability 2019, 11, 2724. [Google Scholar] [CrossRef][Green Version]
  12. Ren, R.; Hu, W.; Dong, J.; Sun, B.; Chen, Y.; Chen, Z. A Systematic Literature Review of Green and Sustainable Logistics: Bibliometric Analysis, Research Trend and Knowledge Taxonomy. Int. J. Environ. Res. Public Health 2019, 17, 261. [Google Scholar] [CrossRef][Green Version]
  13. Olsson, J.; Hellström, D.; Pålsson, H. Framework of Last Mile Logistics Research: A Systematic Review of the Literature. Sustainability 2019, 11, 7131. [Google Scholar] [CrossRef][Green Version]
  14. Ranieri, L.; Digiesi, S.; Silvestri, B.; Roccotelli, M. A review of last mile logistics innovations in an externalities cost reduction vision. Sustainability 2018, 10, 782. [Google Scholar] [CrossRef][Green Version]
  15. Melacini, M.; Perotti, S.; Rasini, M.; Tappia, E. E-fulfilment and distribution in omni-channel retailing: A systematic literature review. Int. J. Phys. Distrib. Logist. Manag. 2018, 48, 391–414. [Google Scholar] [CrossRef]
  16. Lim, S.F.W.T.; Jin, X.; Srai, J.S. Consumer-driven e-commerce. Int. J. Phys. Distrib. Logist. Manag. 2018, 48, 308–332. [Google Scholar] [CrossRef][Green Version]
  17. de Oliveira, C.M.; De Mello Bandeira, R.A.; Goes, G.V.; Gonçalves, D.N.S.; De Almeida D’Agosto, M. Sustainable vehicles-based alternatives in last mile distribution of urban freight transport: A Systematic literature review. Sustainability 2017, 9, 324. [Google Scholar] [CrossRef][Green Version]
  18. Lagorio, A.; Pinto, R.; Golini, R. Research in urban logistics: A systematic literature review. Int. J. Phys. Distrib. Logist. Manag. 2016, 46, 908–931. [Google Scholar] [CrossRef]
  19. Dolati Neghabadi, P.; Evrard Samuel, K.; Espinouse, M.L. Systematic literature review on city logistics: Overview, classification and analysis. Int. J. Prod. Res. 2019, 57, 865–887. [Google Scholar] [CrossRef]
  20. Nenni, M.E.; Sforza, A.; Sterle, C. Sustainability-based review of urban freight models. Soft Comput. 2019, 23, 2899–2909. [Google Scholar] [CrossRef]
  21. Pelletier, S.; Jabali, O.; Laporte, G. 50th Anniversary Invited Article—Goods Distribution with Electric Vehicles: Review and Research Perspectives. Transp. Sci. 2016, 50, 3–22. [Google Scholar] [CrossRef]
  22. Hilmola, O.-P. Supply Chain Cases; Springer International Publishing: Cham, Switzerland, 2018; ISBN 978-3-319-71657-2. [Google Scholar]
  23. Cattaruzza, D.; Absi, N.; Feillet, D.; González-Feliu, J. Vehicle routing problems for city logistics. EURO J. Transp. Logist. 2017, 6, 51–79. [Google Scholar] [CrossRef]
  24. Cavadas, J.; de Almeida Correia, G.H.; Gouveia, J. A MIP model for locating slow-charging stations for electric vehicles in urban areas accounting for driver tours. Transp. Res. Part E Logist. Transp. Rev. 2015, 75, 188–201. [Google Scholar] [CrossRef]
  25. Deflorio, F.; Castello, L. Dynamic charging-while-driving systems for freight delivery services with electric vehicles: Traffic and energy modelling. Transp. Res. Part C Emerg. Technol. 2017, 81, 342–362. [Google Scholar] [CrossRef]
  26. Pinto, R.; Zambetti, M.; Lagorio, A.; Pirola, F. A network design model for a meal delivery service using drones. Int. J. Logist. Res. Appl. 2020, 23, 354–374. [Google Scholar] [CrossRef]
  27. Cortés-Murcia, D.L.; Prodhon, C.; Murat Afsar, H. The electric vehicle routing problem with time windows, partial recharges and satellite customers. Transp. Res. Part E Logist. Transp. Rev. 2019, 130, 184–206. [Google Scholar] [CrossRef]
  28. Patella, S.M.; Scrucca, F.; Asdrubali, F.; Carrese, S. Carbon Footprint of autonomous vehicles at the urban mobility system level: A traffic simulation-based approach. Transp. Res. Part D Transp. Environ. 2019, 74, 189–200. [Google Scholar] [CrossRef]
  29. Yu, J.J.Q. Two-stage request scheduling for autonomous vehicle logistic system. IEEE Trans. Intell. Transp. Syst. 2019, 20, 1917–1929. [Google Scholar] [CrossRef]
  30. Bucsky, P. Autonomous vehicles and freight traffic: Towards better efficiency of road, rail or urban logistics? Urban Dev. Issues 2018, 58, 41–52. [Google Scholar] [CrossRef][Green Version]
  31. Boysen, N.; Briskorn, D.; Fedtke, S.; Schwerdfeger, S. Drone delivery from trucks: Drone scheduling for given truck routes. Networks 2018, 72, 506–527. [Google Scholar] [CrossRef]
  32. Ha, Q.M.; Deville, Y.; Pham, Q.D.; Hà, M.H. On the min-cost Traveling Salesman Problem with Drone. Transp. Res. Part C Emerg. Technol. 2018, 86, 597–621. [Google Scholar] [CrossRef][Green Version]
  33. Marinelli, M.; Caggiani, L.; Ottomanelli, M.; Dell’Orco, M. En route truck-drone parcel delivery for optimal vehicle routing strategies. IET Intell. Transp. Syst. 2018, 12, 253–261. [Google Scholar] [CrossRef]
  34. Wang, Z.; Sheu, J.B. Vehicle routing problem with drones. Transp. Res. Part B Methodol. 2019, 122, 350–364. [Google Scholar] [CrossRef]
  35. Dorling, K.; Heinrichs, J.; Messier, G.G.; Magierowski, S. Vehicle Routing Problems for Drone Delivery. IEEE Trans. Syst. Man Cybern. Syst. 2017, 47, 70–85. [Google Scholar] [CrossRef][Green Version]
  36. Shavarani, S.M.; Nejad, M.G.; Rismanchian, F.; Izbirak, G. Application of hierarchical facility location problem for optimization of a drone delivery system: A case study of Amazon prime air in the city of San Francisco. Int. J. Adv. Manuf. Technol. 2018, 95, 3141–3153. [Google Scholar] [CrossRef]
  37. Shavarani, S.M.; Mosallaeipour, S.; Golabi, M.; İzbirak, G. A congested capacitated multi-level fuzzy facility location problem: An efficient drone delivery system. Comput. Oper. Res. 2019, 108, 57–68. [Google Scholar] [CrossRef]
  38. Moghdani, R.; Salimifard, K.; Demir, E.; Benyettou, A. The green vehicle routing problem: A systematic literature review. J. Clean. Prod. 2021, 279, 123691. [Google Scholar] [CrossRef]
  39. Ferreira, J.C.; Steiner, M.T.A.; Canciglieri Junior, O. Multi-objective optimization for the green vehicle routing problem: A systematic literature review and future directions. Cogent Eng. 2020, 7. [Google Scholar] [CrossRef]
  40. Gatta, V.; Marcucci, E.; Delle Site, P.; Le Pira, M.; Carrocci, C.S. Planning with stakeholders: Analysing alternative off-hour delivery solutions via an interactive multi-criteria approach. Res. Transp. Econ. 2019, 73, 53–62. [Google Scholar] [CrossRef]
  41. Le Pira, M.; Marcucci, E.; Gatta, V.; Inturri, G.; Ignaccolo, M.; Pluchino, A. Integrating discrete choice models and agent-based models for ex-ante evaluation of stakeholder policy acceptability in urban freight transport. Res. Transp. Econ. 2017, 64, 13–25. [Google Scholar] [CrossRef]
  42. Gatta, V.; Marcucci, E.; Le Pira, M. Smart urban freight planning process: Integrating desk, living lab and modelling approaches in decision-making. Eur. Transp. Res. Rev. 2017, 9, 32. [Google Scholar] [CrossRef][Green Version]
  43. Marcucci, E.; Gatta, V.; Marciani, M.; Cossu, P. Measuring the effects of an urban freight policy package defined via a collaborative governance model. Res. Transp. Econ. 2017, 65, 3–9. [Google Scholar] [CrossRef]
  44. Ville, S.; Gonzalez-Feliu, J.; Dablanc, L. The Limits of Public Policy Intervention in Urban Logistics: Lessons from Vicenza (Italy). Eur. Plan. Stud. 2013, 21, 1528–1541. [Google Scholar] [CrossRef]
  45. Cleophas, C.; Cottrill, C.; Ehmke, J.F.; Tierney, K. Collaborative urban transportation: Recent advances in theory and practice. Eur. J. Oper. Res. 2019, 273, 801–816. [Google Scholar] [CrossRef]
  46. Allen, J.; Bektaş, T.; Cherrett, T.; Friday, A.; McLeod, F.; Piecyk, M.; Piotrowska, M.; Zaltz Austwick, M. Enabling a freight traffic controller for collaborative multidrop urban logistics: Practical and theoretical challenges. Transp. Res. Rec. 2017, 2609, 77–84. [Google Scholar] [CrossRef]
  47. Savelsbergh, M.; Van Woensel, T. City logistics: Challenges and opportunities. Transp. Sci. 2016, 50, 579–590. [Google Scholar] [CrossRef]
  48. Ducret, R. Parcel deliveries and urban logistics: Changes and challenges in the courier express and parcel sector in Europe—The French case. Res. Transp. Bus. Manag. 2014, 11, 15–22. [Google Scholar] [CrossRef]
  49. Schöder, D.; Ding, F.; Campos, J.K. The Impact of E-Commerce Development on Urban Logistics Sustainability. Open J. Soc. Sci. 2016, 4, 1–6. [Google Scholar] [CrossRef][Green Version]
  50. de Mello Bandeira, R.A.; Goes, G.V.; Schmitz Gonçalves, D.N.; D’Agosto, M. de A.; Oliveira, C.M. de Electric vehicles in the last mile of urban freight transportation: A sustainability assessment of postal deliveries in Rio de Janeiro-Brazil. Transp. Res. Part D Transp. Environ. 2019, 67, 491–502. [Google Scholar] [CrossRef]
  51. Lebeau, P.; De Cauwer, C.; Van Mierlo, J.; Macharis, C.; Verbeke, W.; Coosemans, T. Conventional, Hybrid, or Electric Vehicles: Which Technology for an Urban Distribution Centre? Sci. World J. 2015, 2015. [Google Scholar] [CrossRef][Green Version]
  52. Browne, M.; Allen, J.; Leonardi, J. Evaluating the use of an urban consolidation centre and electric vehicles in central London. IATSS Res. 2011, 35, 1–6. [Google Scholar] [CrossRef][Green Version]
  53. Faccio, M.; Gamberi, M. New city logistics paradigm: From the “Last Mile” to the “Last 50 Miles” sustainable distribution. Sustainability 2015, 7, 14873–14894. [Google Scholar] [CrossRef][Green Version]
  54. Boerkamps, J.H.K.; Van Binsbergen, A.J.; Bovy, P.H.L. Modeling behavioral aspects of urban freight movement in supply chains. Transp. Res. Rec. 2000, 17–25. [Google Scholar] [CrossRef]
  55. Simoni, M.D.; Marcucci, E.; Gatta, V.; Claudel, C.G. Potential last-mile impacts of crowdshipping services: A simulation-based evaluation. Transportation 2020, 47, 1933–1954. [Google Scholar] [CrossRef]
  56. Gatta, V.; Marcucci, E.; Nigro, M.; Patella, S.; Serafini, S. Public Transport-Based Crowdshipping for Sustainable City Logistics: Assessing Economic and Environmental Impacts. Sustainability 2018, 11, 145. [Google Scholar] [CrossRef][Green Version]
  57. Duarte, G.; Rolim, C.; Baptista, P. How battery electric vehicles can contribute to sustainable urban logistics: A real-world application in Lisbon, Portugal. Sustain. Energy Technol. Assessments 2016, 15, 71–78. [Google Scholar] [CrossRef]
  58. Giordano, A.; Fischbeck, P.; Matthews, H.S. Environmental and economic comparison of diesel and battery electric delivery vans to inform city logistics fleet replacement strategies. Transp. Res. Part D Transp. Environ. 2018, 64, 216–229. [Google Scholar] [CrossRef]
  59. Figliozzi, M.A. Lifecycle modeling and assessment of unmanned aerial vehicles (Drones) CO2e emissions. Transp. Res. Part D Transp. Environ. 2017, 57, 251–261. [Google Scholar] [CrossRef]
  60. Figliozzi, M.; Saenz, J.; Faulin, J. Minimization of urban freight distribution lifecycle CO2e emissions: Results from an optimization model and a real-world case study. Transp. Policy 2020, 86, 60–68. [Google Scholar] [CrossRef]
  61. Stolaroff, J.K.; Samaras, C.; O’Neill, E.R.; Lubers, A.; Mitchell, A.S.; Ceperley, D. Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery. Nat. Commun. 2018, 9, 1–13. [Google Scholar] [CrossRef]
  62. Taefi, T.T.; Stütz, S.; Fink, A. Assessing the cost-optimal mileage of medium-duty electric vehicles with a numeric simulation approach. Transp. Res. Part D Transp. Environ. 2017, 56, 271–285. [Google Scholar] [CrossRef]
  63. Taefi, T.T. Viability of electric vehicles in combined day and night delivery: A total cost of ownership example in Germany. Eur. J. Transp. Infrastruct. Res. 2016, 16, 512–553. [Google Scholar] [CrossRef]
  64. Patella, S.M.; Scrucca, F.; Asdrubali, F.; Carrese, S. Traffic Simulation-Based Approach for A Cradle-to-Grave Greenhouse Gases Emission Model. Sustainability 2019, 11, 4328. [Google Scholar] [CrossRef][Green Version]
  65. Muñoz-Villamizar, A.; Quintero-Araújo, C.L.; Montoya-Torres, J.R.; Faulin, J. Short- and mid-term evaluation of the use of electric vehicles in urban freight transport collaborative networks: A case study. Int. J. Logist. Res. Appl. 2019, 22, 229–252. [Google Scholar] [CrossRef][Green Version]
  66. Muñoz-Villamizar, A.; Montoya-Torres, J.R.; Faulin, J. Impact of the use of electric vehicles in collaborative urban transport networks: A case study. Transp. Res. Part D Transp. Environ. 2017, 50, 40–54. [Google Scholar] [CrossRef]
  67. Wang, J.; Lim, M.K.; Tseng, M.L.; Yang, Y. Promoting low carbon agenda in the urban logistics network distribution system. J. Clean. Prod. 2019, 211, 146–160. [Google Scholar] [CrossRef]
  68. Melo, S.; Baptista, P. Evaluating the impacts of using cargo cycles on urban logistics: Integrating traffic, environmental and operational boundaries. Eur. Transp. Res. Rev. 2017, 9. [Google Scholar] [CrossRef][Green Version]
  69. Allen, J.; Piecyk, M.; Piotrowska, M.; McLeod, F.; Cherrett, T.; Ghali, K.; Nguyen, T.; Bektas, T.; Bates, O.; Friday, A.; et al. Understanding the impact of e-commerce on last-mile light goods vehicle activity in urban areas: The case of London. Transp. Res. Part D Transp. Environ. 2018, 61, 325–338. [Google Scholar] [CrossRef][Green Version]
  70. Browne, M.; Allen, J.; Nemoto, T.; Patier, D.; Visser, J. Reducing Social and Environmental Impacts of Urban Freight Transport: A Review of Some Major Cities. Procedia Soc. Behav. Sci. 2012, 39, 19–33. [Google Scholar] [CrossRef]
  71. Vleugel, J.M.; Bal, F. More space and improved living conditions in cities with autonomous vehicles. Int. J. Des. Nat. Ecodynamics 2018, 12, 505–515. [Google Scholar] [CrossRef]
  72. Koiwanit, J. Analysis of environmental impacts of drone delivery on an online shopping system. Adv. Clim. Chang. Res. 2018, 9, 201–207. [Google Scholar] [CrossRef]
  73. Brown, J.R.; Guiffrida, A.L. Carbon emissions comparison of last mile delivery versus customer pickup. Int. J. Logist. Res. Appl. 2014, 17, 503–521. [Google Scholar] [CrossRef]
  74. Weber, C.L.; Hendrickson, C.T.; Matthews, H.S.; Nagengast, A.; Nealer, R.; Jaramillo, P. Life cycle comparison of traditional retail and e-commerce logistics for electronic products: A case study of In Proceedings of the 2009 IEEE International Symposium on Sustainable Systems and Technology, Phoenix, AZ, USA, 18–20 May 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 1–6. [Google Scholar]
  75. De Marco, A.; Mangano, G.; Zenezini, G. Classification and benchmark of City Logistics measures: An empirical analysis. Int. J. Logist. Res. Appl. 2018, 21, 1–19. [Google Scholar] [CrossRef]
  76. Taniguchi, E.; Thompson, R.G.; Yamada, T. Predicting the effects of city logistics schemes. Transp. Rev. 2003, 23, 489–515. [Google Scholar] [CrossRef]
  77. Goodchild, A.; Toy, J. Delivery by drone: An evaluation of unmanned aerial vehicle technology in reducing CO2 emissions in the delivery service industry. Transp. Res. Part D Transp. Environ. 2018, 61, 58–67. [Google Scholar] [CrossRef]
  78. Crainic, T.G.; Ricciardi, N.; Storchi, G. Models for evaluating and planning city logistics systems. Transp. Sci. 2009, 43, 432–454. [Google Scholar] [CrossRef][Green Version]
  79. Crainic, T.G.; Ricciardi, N.; Storchi, G. Advanced freight transportation systems for congested urban areas. Transp. Res. Part C Emerg. Technol. 2004, 12, 119–137. [Google Scholar] [CrossRef]
  80. Murray, C.C.; Chu, A.G. The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transp. Res. Part C Emerg. Technol. 2015, 54, 86–109. [Google Scholar] [CrossRef]
  81. Ropke, S.; Cordeau, J.F. Branch and cut and price for the pickup and delivery problem with time windows. Transp. Sci. 2009, 43, 267–286. [Google Scholar] [CrossRef][Green Version]
  82. Taniguchi, E.; Van Der Heijden, R.E.C.M. An evaluation methodology for city logistics. Transp. Rev. 2000, 20, 65–90. [Google Scholar] [CrossRef]
  83. Boyer, K.K.; Prud’homme, A.M.; Chung, W. the Last Mile Challenge: Evaluating the Effects of Customer Density and Delivery Window Patterns. J. Bus. Logist. 2009, 30, 185–201. [Google Scholar] [CrossRef]
  84. Ćirović, G.; Pamučar, D.; Božanić, D. Green logistic vehicle routing problem: Routing light delivery vehicles in urban areas using a neuro-fuzzy model. Expert Syst. Appl. 2014, 41, 4245–4258. [Google Scholar] [CrossRef]
  85. Motraghi, A.; Marinov, M.V. Analysis of urban freight by rail using event based simulation. Simul. Model. Pract. Theory 2012, 25, 73–89. [Google Scholar] [CrossRef]
  86. Ehmke, J.F.; Meisel, S.; Mattfeld, D.C. Floating car based travel times for city logistics. Transp. Res. Part C Emerg. Technol. 2012, 21, 338–352. [Google Scholar] [CrossRef]
  87. Juan, A.A.; Mendez, C.A.; Faulin, J.; De Armas, J.; Grasman, S.E. Electric vehicles in logistics and transportation: A survey on emerging environmental, strategic, and operational challenges. Energies 2016, 9, 86. [Google Scholar] [CrossRef][Green Version]
  88. Fatnassi, E.; Chaouachi, J.; Klibi, W. Planning and operating a shared goods and passengers on-demand rapid transit system for sustainable city-logistics. Transp. Res. Part B Methodol. 2015, 81, 440–460. [Google Scholar] [CrossRef]
  89. Devari, A.; Nikolaev, A.G.; He, Q. Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers. Transp. Res. Part E Logist. Transp. Rev. 2017, 105, 105–122. [Google Scholar] [CrossRef]
  90. Gentile, G.; Vigo, D. Movement generation and trip distribution for freight demand modelling applied to city logistics. Eur. Transp. Trasp. Eur. 2013, 1–27. [Google Scholar]
  91. Zhou, L.; Baldacci, R.; Vigo, D.; Wang, X. A Multi-Depot Two-Echelon Vehicle Routing Problem with Delivery Options Arising in the Last Mile Distribution. Eur. J. Oper. Res. 2018, 265, 765–778. [Google Scholar] [CrossRef]
  92. McLeod, F.; Cherrett, T. Loading bay booking and control for urban freight. Int. J. Logist. Res. Appl. 2011, 14, 385–397. [Google Scholar] [CrossRef][Green Version]
  93. Ham, A.M. Integrated scheduling of m-truck, m-drone, and m-depot constrained by time-window, drop-pickup, and m-visit using constraint programming. Transp. Res. Part C Emerg. Technol. 2018, 91, 1–14. [Google Scholar] [CrossRef]
  94. Deutsch, Y.; Golany, B. A parcel locker network as a solution to the logistics last mile problem. Int. J. Prod. Res. 2018, 56, 251–261. [Google Scholar] [CrossRef]
  95. Park, H.; Park, D.; Jeong, I.J. An effects analysis of logistics collaboration in last-mile networks for CEP delivery services. Transp. Policy 2016, 50, 115–125. [Google Scholar] [CrossRef]
  96. Boysen, N.; Schwerdfeger, S.; Weidinger, F. Scheduling last-mile deliveries with truck-based autonomous robots. Eur. J. Oper. Res. 2018, 271, 1085–1099. [Google Scholar] [CrossRef]
  97. Tavana, M.; Khalili-Damghani, K.; Santos-Arteaga, F.J.; Zandi, M.H. Drone shipping versus truck delivery in a cross-docking system with multiple fleets and products. Expert Syst. Appl. 2017, 72, 93–107. [Google Scholar] [CrossRef]
  98. Schiffer, M.; Walthera, G. An adaptive large neighborhood search for the location-routing problem with intra-route facilities. Transp. Sci. 2018, 52, 331–352. [Google Scholar] [CrossRef]
  99. Yu, J.J.Q.; Lam, A.Y.S. Autonomous Vehicle Logistic System: Joint Routing and Charging Strategy. IEEE Trans. Intell. Transp. Syst. 2018, 19, 2175–2187. [Google Scholar] [CrossRef]
  100. Pamučar, D.; Gigović, L.; Ćirović, G.; Regodić, M. Transport spatial model for the definition of green routes for city logistics centers. Environ. Impact Assess. Rev. 2016, 56, 72–87. [Google Scholar] [CrossRef]
  101. Jie, W.; Yang, J.; Zhang, M.; Huang, Y. The two-echelon capacitated electric vehicle routing problem with battery swapping stations: Formulation and efficient methodology. Eur. J. Oper. Res. 2019, 272, 879–904. [Google Scholar] [CrossRef]
  102. Behiri, W.; Belmokhtar-Berraf, S.; Chu, C. Urban freight transport using passenger rail network: Scientific issues and quantitative analysis. Transp. Res. Part E Logist. Transp. Rev. 2018, 115, 227–245. [Google Scholar] [CrossRef]
  103. Chiang, W.C.; Li, Y.; Shang, J.; Urban, T.L. Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization. Appl. Energy 2019, 242, 1164–1175. [Google Scholar] [CrossRef]
  104. Pelletier, S.; Jabali, O.; Laporte, G. Charge scheduling for electric freight vehicles. Transp. Res. Part B Methodol. 2018, 115, 246–269. [Google Scholar] [CrossRef]
  105. Battini, D.; Peretti, U.; Persona, A.; Sgarbossa, F. Application of humanitarian last mile distribution model. J. Humanit. Logist. Supply Chain Manag. 2014, 4, 131–148. [Google Scholar] [CrossRef]
  106. Behnke, M.; Kirschstein, T. The impact of path selection on GHG emissions in city logistics. Transp. Res. Part E Logist. Transp. Rev. 2017, 106, 320–336. [Google Scholar] [CrossRef]
  107. Ahani, P.; Arantes, A.; Melo, S. A portfolio approach for optimal fleet replacement toward sustainable urban freight transportation. Transp. Res. Part D Transp. Environ. 2016, 48, 357–368. [Google Scholar] [CrossRef]
  108. Mbiadou Saleu, R.G.; Deroussi, L.; Feillet, D.; Grangeon, N.; Quilliot, A. An iterative two-step heuristic for the parallel drone scheduling traveling salesman problem. Networks 2018, 72, 459–474. [Google Scholar] [CrossRef]
  109. Baldi, M.M.; Manerba, D.; Perboli, G.; Tadei, R. A Generalized Bin Packing Problem for parcel delivery in last-mile logistics. Eur. J. Oper. Res. 2019, 274, 990–999. [Google Scholar] [CrossRef]
  110. Kin, B.; Spoor, J.; Verlinde, S.; Macharis, C.; Van Woensel, T. Modelling alternative distribution set-ups for fragmented last mile transport: Towards more efficient and sustainable urban freight transport. Case Stud. Transp. Policy 2018, 6, 125–132. [Google Scholar] [CrossRef]
  111. Franceschetti, A.; Honhon, D.; Laporte, G.; Van Woensel, T.; Fransoo, J.C. Strategic fleet planning for city logistics. Transp. Res. Part B Methodol. 2017, 95, 19–40. [Google Scholar] [CrossRef]
  112. Sopha, B.M.; Siagian, A.; Asih, A.M.S. Simulating Dynamic Vehicle Routing Problem using Agent-Based Modeling and Simulation. IEEE Int. Conf. Ind. Eng. Eng. Manag. 2016, 1335–1339. [Google Scholar] [CrossRef]
  113. Wang, F.; Wang, F.; Ma, X.; Liu, J. Demystifying the Crowd Intelligence in Last Mile Parcel Delivery for Smart Cities. IEEE Netw. 2019, 33, 23–29. [Google Scholar] [CrossRef]
  114. Firdausiyah, N.; Taniguchi, E.; Qureshi, A.G. Modeling city logistics using adaptive dynamic programming based multi-agent simulation. Transp. Res. Part E Logist. Transp. Rev. 2019, 125, 74–96. [Google Scholar] [CrossRef]
  115. Karak, A.; Abdelghany, K. The hybrid vehicle-drone routing problem for pick-up and delivery services. Transp. Res. Part C Emerg. Technol. 2019, 102, 427–449. [Google Scholar] [CrossRef]
  116. Pelletier, S.; Jabali, O.; Laporte, G. The electric vehicle routing problem with energy consumption uncertainty. Transp. Res. Part B Methodol. 2019, 126, 225–255. [Google Scholar] [CrossRef]
  117. Troudi, A.; Addouche, S.A.; Dellagi, S.; El Mhamedi, A. Sizing of the drone delivery fleet considering energy autonomy. Sustainability 2018, 10, 344. [Google Scholar] [CrossRef][Green Version]
  118. Rezgui, D.; Chaouachi Siala, J.; Aggoune-Mtalaa, W.; Bouziri, H. Application of a variable neighborhood search algorithm to a fleet size and mix vehicle routing problem with electric modular vehicles. Comput. Ind. Eng. 2019, 130, 537–550. [Google Scholar] [CrossRef]
  119. Scherr, Y.O.; Neumann Saavedra, B.A.; Hewitt, M.; Mattfeld, D.C. Service network design with mixed autonomous fleets. Transp. Res. Part E Logist. Transp. Rev. 2019, 124, 40–55. [Google Scholar] [CrossRef]
  120. Bergqvist, R.; Monios, J. The last mile, inbound logistics and intermodal high capacity transport—The case of Jula in Sweden. World Rev. Intermodal Transp. Res. 2016, 6, 74. [Google Scholar] [CrossRef]
  121. Li, Y.; Lim, M.K.; Tan, Y.; Lee, S.Y.; Tseng, M.L. Sharing economy to improve routing for urban logistics distribution using electric vehicles. Resour. Conserv. Recycl. 2020, 153, 104585. [Google Scholar] [CrossRef]
  122. Moeini, M.; Salewski, H. A Genetic Algorithm for Solving the Truck-Drone-ATV Routing Problem. In Optimization of Complex Systems: Theory, Models, Algorithms and Applications; Springer: Berlin/Heidelberg, Germany, 2020; pp. 1023–1032. [Google Scholar]
  123. Li, H.; Liu, Y.; Chen, K.; Lin, Q. The two-echelon city logistics system with on-street satellites. Comput. Ind. Eng. 2020, 139, 105577. [Google Scholar] [CrossRef]
  124. Fikar, C.; Hirsch, P.; Gronalt, M. A decision support system to investigate dynamic last-mile distribution facilitating cargo-bikes. Int. J. Logist. Res. Appl. 2018, 21, 300–317. [Google Scholar] [CrossRef]
  125. Chen, B. Location Selection of Logistics Center in e-Commerce Network Environments. Am. J. Neural Networks Appl. 2017, 3, 40. [Google Scholar] [CrossRef][Green Version]
  126. Ehmke, J.F.; Mattfeld, D.C. Data Allocation and Application for Time-Dependent Delivery in Urban Areas. In Proceedings of the 12th World Conference on Transport Research, Lisbon, Portugal, 11–15 July 2010; Volume 46, pp. 1–16. [Google Scholar]
  127. Dekker, R.; Bloemhof, J.; Mallidis, I. Operations Research for green logistics—An overview of aspects, issues, contributions and challenges. Eur. J. Oper. Res. 2012, 219, 671–679. [Google Scholar] [CrossRef][Green Version]
  128. Behrends, S.; Lindholm, M.; Woxenius, J. The impact of urban freight transport: A definition of sustainability from an actor’s perspective. Transp. Plan. Technol. 2008, 31, 693–713. [Google Scholar] [CrossRef][Green Version]
  129. Arvidsson, N. The milk run revisited: A load factor paradox with economic and environmental implications for urban freight transport. Transp. Res. Part A Policy Pract. 2013, 51, 56–62. [Google Scholar] [CrossRef][Green Version]
  130. Russo, F.; Comi, A. Urban freight transport planning towards green goals: Synthetic environmental evidence from tested results. Sustainability 2016, 8, 381. [Google Scholar] [CrossRef][Green Version]
  131. Aditjandra, P.T.; Galatioto, F.; Bell, M.C.; Zunder, T.H. Evaluating the impacts of urban freight traffic: Application of micro-simulation at a large establishment. Eur. J. Transp. Infrastruct. Res. 2016, 16, 4–22. [Google Scholar] [CrossRef]
  132. Lebeau, P.; Macharis, C.; Van Mierlo, J.; Lebeau, K. Electrifying light commercial vehicles for city logistics? A total cost of ownership analysis. Eur. J. Transp. Infrastruct. Res. 2015, 15, 551–569. [Google Scholar] [CrossRef]
  133. Park, J.; Kim, S.; Suh, K. A comparative analysis of the environmental benefits of drone-based delivery services in urban and rural areas. Sustainability 2018, 10, 888. [Google Scholar] [CrossRef][Green Version]
  134. Durand, B.; Mahjoub, S.; Senkel, M.P. Delivering to urban online shoppers: The gains from “last-mile” pooling. Supply Chain Forum 2013, 14, 22–31. [Google Scholar] [CrossRef][Green Version]
  135. Aurambout, J.P.; Gkoumas, K.; Ciuffo, B. Last mile delivery by drones: An estimation of viable market potential and access to citizens across European cities. Eur. Transp. Res. Rev. 2019, 11. [Google Scholar] [CrossRef][Green Version]
  136. Teoh, T.; Kunze, O.; Teo, C.C.; Wong, Y.D. Decarbonisation of urban freight transport using electric vehicles and opportunity charging. Sustainability 2018, 10, 3258. [Google Scholar] [CrossRef][Green Version]
  137. Semanjski, I.; Gautama, S. A collaborative stakeholder decision-making approach for sustainable urban logistics. Sustainability 2019, 11, 234. [Google Scholar] [CrossRef][Green Version]
  138. Digiesi, S.; Fanti, M.P.; Mummolo, G.; Silvestri, B. Externalities reduction strategies in last mile logistics: A review. In Proceedings of the 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), Bari, Italy, 18–20 September 2017; pp. 248–253. [Google Scholar] [CrossRef]
  139. Lebeau, P.; Macharis, C.; Mierlo, J. Van How to improve the total cost of ownership of electric vehicles: An analysis of the light commercial vehicle segment. World Electr. Veh. J. 2019, 10, 90. [Google Scholar] [CrossRef][Green Version]
  140. Marmiroli, B.; Venditti, M.; Dotelli, G.; Spessa, E. The transport of goods in the urban environment: A comparative life cycle assessment of electric, compressed natural gas and diesel light-duty vehicles. Appl. Energy 2020, 260, 114236. [Google Scholar] [CrossRef]
  141. Cárdenas, I.; Beckers, J.; Vanelslander, T. E-commerce last-mile in Belgium: Developing an external cost delivery index. Res. Transp. Bus. Manag. 2017, 24, 123–129. [Google Scholar] [CrossRef]
  142. Moore, A.M. Innovative scenarios for modeling intra-city freight delivery. Transp. Res. Interdiscip. Perspect. 2019, 3, 100024. [Google Scholar] [CrossRef]
  143. Fagnant, D.J.; Kockelman, K. Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 2015, 77, 167–181. [Google Scholar] [CrossRef]
  144. Cherrett, T.; Allen, J.; McLeod, F.; Maynard, S.; Hickford, A.; Browne, M. Understanding urban freight activity—Key issues for freight planning. J. Transp. Geogr. 2012, 24, 22–32. [Google Scholar] [CrossRef][Green Version]
  145. Lindholm, M.; Behrends, S. Challenges in urban freight transport planning—A review in the Baltic Sea Region. J. Transp. Geogr. 2012, 22, 129–136. [Google Scholar] [CrossRef]
  146. Tadić, S.; Zečević, S.; Krstić, M. A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection. Expert Syst. Appl. 2014, 41, 8112–8128. [Google Scholar] [CrossRef]
  147. Schliwa, G.; Armitage, R.; Aziz, S.; Evans, J.; Rhoades, J. Sustainable city logistics—Making cargo cycles viable for urban freight transport. Res. Transp. Bus. Manag. 2015, 15, 50–57. [Google Scholar] [CrossRef][Green Version]
  148. Marcucci, E.; Danielis, R. The potential demand for a urban freight consolidation centre. Transportation (Amst). 2008, 35, 269–284. [Google Scholar] [CrossRef]
  149. Ballantyne, E.E.F.; Lindholm, M.; Whiteing, A. A comparative study of urban freight transport planning: Addressing stakeholder needs. J. Transp. Geogr. 2013, 32, 93–101. [Google Scholar] [CrossRef]
  150. Guerra, E. Planning for Cars That Drive Themselves: Metropolitan Planning Organizations, Regional Transportation Plans, and Autonomous Vehicles. J. Plan. Educ. Res. 2016, 36, 210–224. [Google Scholar] [CrossRef]
  151. Muñuzuri, J.; Cortés, P.; Guadix, J.; Onieva, L. City logistics in Spain: Why it might never work. Cities 2012, 29, 133–141. [Google Scholar] [CrossRef]
  152. Gruber, J.; Kihm, A.; Lenz, B. A new vehicle for urban freight? An ex-ante evaluation of electric cargo bikes in courier services. Res. Transp. Bus. Manag. 2014, 11, 53–62. [Google Scholar] [CrossRef][Green Version]
  153. Russo, F.; Comi, A. A model system for the ex-ante assessment of city logistics measures. Res. Transp. Econ. 2011, 31, 81–87. [Google Scholar] [CrossRef]
  154. Morganti, E.; Gonzalez-Feliu, J. City logistics for perishable products. The case of the Parma’s Food Hub. Case Stud. Transp. Policy 2015, 3, 120–128. [Google Scholar] [CrossRef]
  155. Ajanovic, A.; Haas, R. Dissemination of electric vehicles in urban areas: Major factors for success. Energy 2016, 115, 1451–1458. [Google Scholar] [CrossRef]
  156. Lenz, B.; Riehle, E. Bikes for urban freight? Transp. Res. Rec. 2013, 39–45. [Google Scholar] [CrossRef]
  157. Roumboutsos, A.; Kapros, S.; Vanelslander, T. Green city logistics: Systems of Innovation to assess the potential of E-vehicles. Res. Transp. Bus. Manag. 2014, 11, 43–52. [Google Scholar] [CrossRef]
  158. Taefi, T.T.; Kreutzfeldt, J.; Held, T.; Fink, A. Supporting the adoption of electric vehicles in urban road freight transport—A multi-criteria analysis of policy measures in Germany. Transp. Res. Part A Policy Pract. 2016, 91, 61–79. [Google Scholar] [CrossRef]
  159. Anand, N.; van Duin, R.; Tavasszy, L. Ontology-based multi-agent system for urban freight transportation. Int. J. Urban Sci. 2014, 18, 133–153. [Google Scholar] [CrossRef]
  160. Nuzzolo, A.; Comi, A.; Ibeas, A.; Moura, J.L. Urban freight transport and city logistics policies: Indications from Rome, Barcelona, and Santander. Int. J. Sustain. Transp. 2016, 10, 552–566. [Google Scholar] [CrossRef]
  161. Bjerkan, K.Y.; Sund, A.B.; Nordtømme, M.E. Stakeholder responses to measures green and efficient urban freight. Res. Transp. Bus. Manag. 2014, 11, 32–42. [Google Scholar] [CrossRef]
  162. Yuen, K.F.; Wang, X.; Ng, L.T.W.; Wong, Y.D. An investigation of customers’ intention to use self-collection services for last-mile delivery. Transp. Policy 2018, 66, 1–8. [Google Scholar] [CrossRef]
  163. Lebeau, P.; Macharis, C.; Van Mierlo, J. Exploring the choice of battery electric vehicles in city logistics: A conjoint-based choice analysis. Transp. Res. Part E Logist. Transp. Rev. 2016, 91, 245–258. [Google Scholar] [CrossRef]
  164. Harrington, T.S.; Singhai, J.; Kumar, M.; Wohlrab, J. Identifying design criteria for urban system last-mile solutions -A multi-stakeholder perspective. Prod. Plan. Control 2016, 27, 456–476. [Google Scholar] [CrossRef][Green Version]
  165. Jaller, M.; Wang, X.C.; Holguín-Veras, J. Large urban freight traffic generators: Opportunities for city logistics initiatives. J. Transp. Land Use 2015, 8, 51–67. [Google Scholar] [CrossRef][Green Version]
  166. Morganti, E.; Browne, M. Technical and operational obstacles to the adoption of electric vans in France and the UK: An operator perspective. Transp. Policy 2018, 63, 90–97. [Google Scholar] [CrossRef][Green Version]
  167. Mirhedayatian, S.M.; Yan, S. A framework to evaluate policy options for supporting electric vehicles in urban freight transport. Transp. Res. Part D Transp. Environ. 2018, 58, 22–38. [Google Scholar] [CrossRef]
  168. Kane, M.; Whitehead, J. How to ride transport disruption –a sustainable framework for future urban mobility. Aust. Plan. 2017, 54, 177–185. [Google Scholar] [CrossRef]
  169. Musolino, G.; Rindone, C.; Polimeni, A.; Vitetta, A. Planning urban distribution center location with variable restocking demand scenarios: General methodology and testing in a medium-size town. Transp. Policy 2019, 80, 157–166. [Google Scholar] [CrossRef]
  170. Arvidsson, N.; Givoni, M.; Woxenius, J. Exploring last mile synergies in passenger and freight transport. Built Environ. 2016, 42, 523–538. [Google Scholar] [CrossRef]
  171. Hopkins, D.; McCarthy, A. Change trends in urban freight delivery: A qualitative inquiry. Geoforum 2016, 74, 158–170. [Google Scholar] [CrossRef][Green Version]
  172. Hoffmann, T.; Prause, G. On the regulatory framework for last-mile delivery robots. Machines 2018, 6, 33. [Google Scholar] [CrossRef][Green Version]
  173. Cagliano, A.C.; Carlin, A.; Mangano, G.; Rafele, C. Analyzing the diffusion of eco-friendly vans for urban freight distribution. Int. J. Logist. Manag. 2017, 28, 1218–1242. [Google Scholar] [CrossRef]
  174. Lebeau, P.; Macharis, C.; Van Mierlo, J.; Janjevic, M. Improving policy support in city logistics: The contributions of a multi-actor multi-criteria analysis. Case Stud. Transp. Policy 2018, 6, 554–563. [Google Scholar] [CrossRef]
  175. Christensen, L.; Klauenberg, J.; Kveiborg, O.; Rudolph, C. Suitability of commercial transport for a shift to electric mobility with Denmark and Germany as use cases. Res. Transp. Econ. 2017, 64, 48–60. [Google Scholar] [CrossRef][Green Version]
  176. Kellermann, R.; Biehle, T.; Fischer, L. Drones for parcel and passenger transportation: A literature review. Transp. Res. Interdiscip. Perspect. 2020, 4, 100088. [Google Scholar] [CrossRef]
  177. Buldeo Rai, H.; Verlinde, S.; Macharis, C. City logistics in an omnichannel environment. The case of Brussels. Case Stud. Transp. Policy 2019, 7, 310–317. [Google Scholar] [CrossRef]
  178. Hu, W.; Dong, J.; Hwang, B.G.; Ren, R.; Chen, Y.; Chen, Z. Using system dynamics to analyze the development of urban freight transportation system based on rail transit: A case study of Beijing. Sustain. Cities Soc. 2020, 53, 101923. [Google Scholar] [CrossRef]
  179. Matusiewicz, M. Towards sustainable urban logistics: Creating sustainable urban freight transport on the example of a Limited Accessibility Zone in Gdansk. Sustainability 2019, 11, 3879. [Google Scholar] [CrossRef][Green Version]
  180. Skiver, R.L. Crowdserving: A Last Mile Delivery Method for Brick-and-Mortar Retailers. Glob. J. Bus. Res. 2017, 11, 67–77. [Google Scholar]
  181. Arroyo, J.L.; Felipe, Á.; Ortuño, M.T.; Tirado, G. Effectiveness of carbon pricing policies for promoting urban freight electrification: Analysis of last mile delivery in Madrid. Cent. Eur. J. Oper. Res. 2019. [Google Scholar] [CrossRef]
  182. Monios, J.; Bergqvist, R. The transport geography of electric and autonomous vehicles in road freight networks. J. Transp. Geogr. 2019, 80, 102500. [Google Scholar] [CrossRef]
  183. Ehrler, V.C.; Schöder, D.; Seidel, S. Challenges and perspectives for the use of electric vehicles for last mile logistics of grocery e-commerce—Findings from case studies in Germany. Res. Transp. Econ. 2019. [Google Scholar] [CrossRef]
  184. Ørving, T.; Fossheim, K.; Andersen, J. Public Sector Facilitation of Cargo Bike Operations to Improve City Logistics; Elsevier Inc.: Amsterdam, The Netherlands, 2019; ISBN 9780128176962. [Google Scholar]
  185. Cheng, G.; Liu, C. Research on business operating model of new energy battery electric vehicles used as urban logistics cars. Int. J. Multimed. Ubiquitous Eng. 2016, 11, 387–400. [Google Scholar] [CrossRef]
Figure 1. Procedure for literature selection.
Figure 1. Procedure for literature selection.
Sustainability 13 00006 g001
Figure 2. Number of papers considered per category and year of publication.
Figure 2. Number of papers considered per category and year of publication.
Sustainability 13 00006 g002
Figure 3. Representativeness of every category and of e-commerce related works.
Figure 3. Representativeness of every category and of e-commerce related works.
Sustainability 13 00006 g003
Figure 4. Colored map—number of authors per country.
Figure 4. Colored map—number of authors per country.
Sustainability 13 00006 g004
Table 1. Most active authors (more than three papers) sorted by number of citations.
Table 1. Most active authors (more than three papers) sorted by number of citations.
AuthorInstitutionNumber of PapersNumber of Citations
Teodor Gabriel CrainicCIRRELT, Montréal31081
Maria LindholmChalmers University of Technology of Göteborg3521
Fraser McLeodUniversity of Southampton4336
Tom CherrettUniversity of Southampton4336
Julian AllenUniversity of Westminster of London3286
Antonio ComiTor Vergata University of Rome3198
Cathy MacharisVrije Universiteit Brussel7156
Javier FaulinPublic University of Navarre4154
Gilbert LaporteHEC, CIRRELT Montréal4141
Joeri Van MierloVrije Universiteit Brussel5135
Philippe LebeauVrije Universiteit Brussel5135
Ola JabaliPolytechnic University of Milan3125
Samuel PelletierHEC, CIRRELT Montréal3125
Dirk Christian MattfeldUniversity of Braunschweig375
Tessa T. TaefiReutlingen University371
Nils BoysenFriedrich-Schiller-Universität of Jena368
Stefan SchwerdfegerFriedrich-Schiller-Universität Jena368
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Patella, S.M.; Grazieschi, G.; Gatta, V.; Marcucci, E.; Carrese, S. The Adoption of Green Vehicles in Last Mile Logistics: A Systematic Review. Sustainability 2021, 13, 6.

AMA Style

Patella SM, Grazieschi G, Gatta V, Marcucci E, Carrese S. The Adoption of Green Vehicles in Last Mile Logistics: A Systematic Review. Sustainability. 2021; 13(1):6.

Chicago/Turabian Style

Patella, Sergio Maria, Gianluca Grazieschi, Valerio Gatta, Edoardo Marcucci, and Stefano Carrese. 2021. "The Adoption of Green Vehicles in Last Mile Logistics: A Systematic Review" Sustainability 13, no. 1: 6.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop