# A review on the charging station planning and fleet operation for electric
freight vehicles

Md Rakibul Alam  Zhaomiao Guo  Department of Civil, Environmental and
Construction Engineering
Resilient, Intelligent, and Sustainable Energy Systems Cluster
University of Central Florida, FL 32766

###### Abstract

Freight electrification introduces new opportunities and challenges for
planning and operation. Although research on charging infrastructure planning
and operation is widely available for general electric vehicles, unique
physical and operational characteristics of Electric Freight Vehicles coupled
with specific patterns of logistics require dedicated research. This paper
presents a comprehensive literature review to gain a better understanding of
the state-of-the-art research efforts related to planning (charging station
siting and sizing) and operation (routing, charge scheduling, platoon
scheduling, and fleet sizing) for EFVs. We classified the existing literature
based on the research topics, innovations, methodologies, and solution
approaches, and future research directions are identified. Different types of
methodologies, such as heuristic, simulation, and mathematical programming
approaches, were applied in the reviewed literature where mathematical models
account for the majority. We further narrated the specific modeling
considerations for different logistic patterns and research goals with proper
reasoning. To solve the proposed models, different solution approaches,
including exact algorithms, metaheuristic algorithms, and software simulation,
were evaluated in terms of applicability, advantages, and disadvantages. This
paper helps to draw more attention to the planning and operation issues and
solutions for freight electrification and facilitates future studies on EFV to
ensure a smooth transition to a clean freight system.

###### keywords:

Freight electrification, Charging station planning, Green logistics, Electric
freight vehicle operation, Freight system optimization

††journal:

##  1 Introduction

###  1.1 Background

With the rapid development of e-commerce, freight Vehicle Miles Travelled
(VMT) has been rapidly increasing over the past decades [Jaller et al., 2022]
and is projected to further increase 57% by 2049 [Federal Highway
Administration, 2022]. Despite only accounting for 4% of the total road
vehicles in the U.S., freight vehicles have been the second largest
contributors (23.6%) of the Greenhouse Gas (GHG) emissions in the
transportation sector, which has the highest share (28.7%) of GHG emissions in
the U.S. [U.S.EPA, 2021].

A significant percentage (45%) of US ton-miles of freight is transported by
trucks in 2020 and this percentage has kept increasing over the past decade.
Due to the decarbonization trend in the power sector 11121% reduction since
1990, see https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions,
EFVs could play a critical role to mitigate the GHG emissions for freight. The
North American Council for Freight Efficiency suggests that the U.S. and
Canada can convert more than 5 million medium- and heavy-duty trucks from
fossil fuels to electricity without disrupting the freight flow [St John,
2021].

Recently, several auto manufacturers, such as Daimler, Mack, Tesla, Nikola,
Volvo, Navistar, Rivian, and DAF, have announced plans to launch EFVs in the
market [Howard, Bill, 2019]. Most of the major delivery companies are also
starting to expand their electric fleet. For example, UPS has initiated a
purchase for 10,000 electric delivery vehicles, while Amazon has committed to
acquiring 100,000 EFVs through the emerging company Rivian. Additionally, in
February 2021, the United States Postal Service granted a contract for the
production and deployment of 50,000 to 165,000 EFVs over a span of 10 years
according to a source[USPS, 2022].

As the adoption of Electric Vehicles in the freight industry is a promising
trend, questions arise in terms of how to plan the supporting infrastructure
and how to operate EFVs efficiently. First, due to lower energy efficiency and
tighter time constraints, research on Charging Station (CS) planning is needed
to mitigate the range anxiety and improve the operational feasibility of EFVs.
CS planning includes optimizing siting and sizing of CSs, the allocation of
different types of charging plugs (i.e., Electric Vehicle Supply Equipments)
at each CS and feasibility study on the existing CS network. Second, research
is also needed to better optimize the operational scheduling (i.e., the
decision of charging, routing, platooning, and fleet sizing) of EFVs by
incorporating the unique freight characteristics (e.g., pickup and delivery
time windows, driver’s rest time, etc). Although the previous research on EVs
mainly focuses on light-duty EVs, such as private cars [Qian et al., 2011,
Baghali and Guo, 2021], conventional taxis[Yang et al., 2017], and ride-
sourcing vehicles [Alam and Guo, 2022, Alam et al., 2022], some recent studies
have looked into EFVs [Link and Plötz, 2022, Teoh et al., 2018, Jahangir Samet
et al., 2021, Bac and Erdem, 2021]. A survey of the state-of-the-art research
on CS planning and operational scheduling for EFVs will help to draw more
attention to this field and facilitate future studies on EFVs to ensure a
smooth transition to a clean freight system.

###  1.2 Goals and Contributions

The goal of this review is to gain a better understanding of the state-of-the-
art research on planning and operation of EFVs. In particular, we aim to
answer the following research questions (RQs) in this review paper.

  * •

RQ1: What justifies the necessity for a dedicated research into EFVs?

  * •

RQ2: What aspects of CS planning and operational scheduling were addressed in
the existing literature??

  * •

RQ3: What methodologies have been employed to tackle the planning and
operational scheduling for EFVs?

  * •

RQ4: How can we develop a fundamental mathematical programming model to
address planning and operational scheduling for EFVs?

  * •

RQ5: In what ways are various logistical configurations incorporated into
modeling approaches for EFVs?

  * •

RQ6: What solution approaches have been employed to address mall to large-
scale problems, and how do they differ?

  * •

RQ7: What are the potential research areas and challenges that have not been
adequately explored?

Limited review papers are identified on the CS planning, operational
scheduling, and coupling of CS planning and operational issues for EFVs
considering different logistics stages (e.g., first-/last-/middle-miles). Up
to November 2023, two review papers have been identified, which include
selected topics on the operation of EFVs. For example, Bektaş et al. [2019]
provided a review of the application of mathematical optimization on road and
maritime freight systems, which included a brief discussion on the routing
problem for EFVs. Similarly, Patella et al. [2020] reviewed literature on
optimization, scheduling, policy, and sustainability issues for the last-mile
logistics systems, where a section was devoted to routing and route-scheduling
for EFVs.

Our study offers a comprehensive examination of prior research related to CS
planning, operational scheduling, and the integration of CS planning with
operational considerations for EFVs. This review article can serve as a
valuable resource for two distinct audiences. Firstly, researchers who are
just embarking on their exploration of this field may benefit from this
overview, which consolidates existing studies on EFV logistics across various
freight patterns and may assist in identifying research gaps. For those
interested in modeling specific aspects of the problem, this study can help
identify previously employed modeling techniques and potentially highlight
some modeling best practices. It also delves into the formulation of the most
prevalent and favored approach, namely mathematical modeling, and provides
insights into solving methods for such models. Secondly, decision-makers, such
as EV infrastructure planners and logistics coordinators, may find this study
to be a valuable resource for gaining insights into the current landscape of
CS planning and operational scheduling techniques. It has the potential to
offer guidance on transition policies.

The remainder of this paper is organized as follows. Section 2 provides a
detailed description of our approach to retrieving relevant literature and
presents analyses regarding the distribution of literature across journals,
publication years, and the geographical locations of authors. Section 3-8
provide answers to the aforementioned research questions by analyzing existing
literature. Finally, Section 9 provides conclusions and discusses future
research directions (also the answer of last RQ).

##  2 Literature Search

The following keywords and their combinations were used to guide the search
process: “freight electric vehicles”, “electric truck”, “green freight
vehicles”, “charging station allocation”, “charging station planning”,
“charging”, “routing”, “scheduling”, and “logistics”. The following search
engines were used: Science Direct, IEEE Explore, Web of Science, Scopus,
Springer, and Google Scholar. Peer-reviewed journals, conference papers, and
book chapters written in English were considered. After a review of the
collected articles, we identified a total of 60 studies that were closely
related to our study scope.

(a) Trend of research publication per year

(b) Research contribution from publishers

(c) Research contribution from the journal, book, conference, etc.

Figure 1: Summary of literature search by year, publication type and
publishers

Figure 1(a) presents the distribution of the collected literature by research
focus and the year of publication. The total numbers of literature on CS
planning, operational scheduling, and coupling CS planning and EFV operation
are 9, 38, and 13, respectively. This research field starts receiving more
attention from the scientific community since 2018. Figure 1(b) shows the
distribution of the collected literature by publisher. The majority of the
studies are published in Elsevier, with a total of 25 papers. MDPI published a
total of 6, Taylor & Francis published a total of 4, on the other hand,
Springer, IEEE, and Growing Science published 3 articles each. The remaining
publishers published one article each except Wiley (2 articles). Figure 1(c)
presents the distribution of literature by journal names and publication types
(i.e., conference proceedings, book chapters, and university repositories).
The majority of these literature was published in the European Journal of
Operational Research (with a total of five studies), Transportation Research
Part E: Logistics and Transportation Review (with a total of four studies),
Transportation Letters and International Journal of Industrial Engineering
Computation (each with a total of three studies). Conferences published five
pieces of literature. Literature with a focus on operational scheduling has a
higher share compared with literature on CS planning, as shown in Figure 1.
Figure 2 shows that the United States and Germany emerge as prominent leaders
in EFV research related to CS planning and operational scheduling, with China
and Canada closely following suit.

Figure 2: Geographic locations of existing literature

##  3 RQ1: What justifies the necessity for dedicated research into EFVs?

The travel patterns of freight vehicles differ significantly from those of
passenger vehicles. Freight vehicle trips are more frequent and adhere to
tight pickup and delivery schedules, in contrast to the less frequent and less
rigorously scheduled trips made by passenger vehicles, with the exception of
commercial passenger vehicles like ridesourcing and dial-a-ride services. The
more frequent freight trips necessitate more charging due to lower energy
efficiency resulting from the heavy freight payload.

Unlike private EV users who have the flexibility to choose CS with varying
speeds, such as Level 1, Level 2, and fast chargers, based on their
contingencies and specific time of day, EFVs require a dense distribution of
Direct Current Fast Charger (DCFC) networks to meet tight freight schedules
Speth et al. [2022]. While interstates and highways feature DCFC stations,
most of these are presently inadequate and cause challenges in accommodating
and maneuvering large EFV Hurtado-Beltran et al. [2021]. Furthermore, freight
drivers require mandatory rest periods, particularly during long-distance
middle-mile freight deliveries. These unique characteristics of EFV demand
special attention when planning CS infrastructure and operational scheduling
for EFV. In addition, different freight logistics patterns demand
modifications in operational planning.

The extensive literature on EV has primarily concentrated on passenger
vehicles within the context of CS planning[Alam et al., 2022, Cui et al.,
2019, Liu et al., 2019, Arslan and Karaşan, 2016, Guo et al., 2016, Baouche et
al., 2014]. Unplanned CS allocation has been identified as a key hindrance to
EV adoption Xu et al. [2022] that can be equally true for EFV. This issue can
be exacerbated when operational scheduling for EFVs is implemented without
considering the unique characteristics of these vehicles.

##  4 RQ2: What aspects of CS planning and operational scheduling were
addressed in the existing literature?

###  4.1 Siting and sizing of CSs

The CS siting and sizing problem decides the locations of CSs and the number
of EVSE at those CSs. As electrifying freight vehicles is a new concept, few
literature [Sauter et al., 2021, Speth et al., 2022] are found in recent days
that considered unique operational characteristics of EFV in CS planning
studies for EFVs . Flexible charging amount at DCFCs were considered and truck
driver’s break time (45-minute mandatory break after 4.5 hours of driving) was
leveraged for en-route DCFC charging. Alp et al. [2022] partitioned the entire
region into uniform-size grids with the center of each block to be potential
DCFC locations. The allocation of DCFCs would be done by freight industries
annually to minimize both the investment costs (e.g., procurement cost and
salvage value) and operational costs (e.g., electricity price, driver wage,
maintenance of trucks and EVSE). Nonetheless, the planning analyses presumed
the immediate availability of land for the deployment of CSs, a condition that
might not always be achievable, especially for large EFVs that require extra
space for maneuvering. To reduce the infrastructure costs of CSs, Bischoff et
al. [2019] considered the current gas stations as candidate DCFCs locations in
Sweden and estimate the charging demand considering the variable charging
speed as a function of State Of Charge (SOC) and the queue at the CS.

Since the integration of electric transportation within the freight sector is
in its early phases, the adverse effects stemming from the substantial
charging load on the power grid have received limited attention in prior
research on EFVs. However, with the increasing adoption of EFVs, the impacts
of their charging load on the power grid can be significant due to much higher
charging power. As a result, there exists a need for a well-grounded CS
planning strategy that takes into account the interconnected nature of
transportation and power requirements to enhance efficiency and reliability
for EFV charging. Recently Li et al. [2023] conducted a study that decides the
optimal number of EVSE of different charging power and the upgrade plan of
power distribution systems considering two charging strategies: unmanaged
(immediate charging without central coordination of power supply) and managed
charging (smart charging with central coordination of power supply).

###  4.2 Feasibility study for existing CS

The feasibility study aims to evaluate if a certain charging infrastructure
deployment plan is feasible to support EFVs’ charging needs. Hurtado-Beltran
et al. [2021] assessed the maximum area that can be served by EFVs considering
DCFCs at truck stops (i.e., resting and parking facilities) along the US-
Interstate Highway Network. It is found that service coverage is less for the
states located west of the Mississippi River (excluding California, Oregon,
and Washington) due to sparser road networks and fewer truck stops. Borlaug et
al. [2021] assessed the potential impacts of depot charging on the electric
grid during an average of 14 hours of off-shift dwell time for heavy-duty EFVs
considering three charging strategies: charge at 100 kW immediately (EVs
charged as soon as possible), charge at 100 kW delayed (EVs charged as late as
possible), and charge at a constant minimum power (EVs charged as slowly as
possible). The data was based on the National Renewable Energy Laboratory
(NREL) Fleet DNA database. Their study found that the existing power
distribution systems can meet the EFV charging demand with 100kW EVSE.
Jahangir Samet et al. [2021] conducted a study to measure the range anxiety of
medium and heavy-duty EFVs under the existing CS network in Finland and
Switzerland. They found that the current CS network (consists of minimum 40kW
CSs) is capable to serve 93% and 89% of trip mileages in Finland and
Switzerland, respectively, for EFVs with a maximum of 30-ton payload capacity.
However, the CSs within the depots and vendor-owned CSs (e.g., Tesla
Supercharger) only serve the proprietary EFVs, which can be resolved by proper
charging sharing policy. Therefore, an economic-feasibility study considering
different business models of sharing existing CS in Stockholm, Sweden was also
conducted by Melander and Wallström [2022]. It was found that sharing CS
policy increases utilization of EVSE and enables more environment-friendly
freight transport.

###  4.3 Routing problem

Routing problems are crucial in the freight industry with EFVs especially
considering the limited driving range, sparse charging stations, and tight
delivery schedules. This decides the transportation links to be traversed by
EFVs and the time of traversing these edges for freight delivery. The classic
routing algorithms such as Dijkstra’s algorithm or Bellman-ford algorithm
cannot be directly applied because the “edge cost” is negative during the
charging event at CS, whereas the cost is positive while traveling (e.g.,
energy consumption and travel time) [Ioannou et al., 2020].

Besides the unique physical and operational characteristics of EFVs, different
patterns of logistics and the consideration of various important factors such
as energy consumption uncertainty, stochastic travel time, stochastic battery
depletion and a mixture of fleets generate different variants of Vehicle
Routing Problem (VRP).

For example, Afroditi et al. [2014], Napoli et al. [2021], Wang et al. [2021a]
studied Electric Vehicle Routing Problem with Time Windows (EVRPTW)
considering homogeneous EFV fleet, whereas Celebi et al. [2021], Goeke and
Schneider [2015] studied Mixed vehicle routing problem with Time Windows
(MVRPTW) considering a mixture of EFVs and Internal Combustion Engine
Vehicles.

Zhao et al. [2021] tried to optimize the EFV dispatch and routing for
dedicated-service (pickup and delivery) logistics considering delivery time
windows, flexible charging, dynamic energy consumption due to freight load
change, and dynamic traffic situation. The routing problem was named Electric
Vehicle Routing Problem with Pickup and Delivery, Time Windows, and Partial
Recharge (EVRP-PD-TW-PR).

Although most studies consider fixed delivery locations, Sadati et al. [2022]
introduced flexible freight delivery locations in Electric Vehicle Routing
Problem with Flexible Deliveries (EVRP-FD) given delivery time windows. All of
these studies considered customer locations for either pickup or freight
delivery, Yilmaz and Kalayci [2022] provided flexibility for both pickup and
delivery at the same customer location by introducing Simultaneous Pickup and
Delivery (SPD) in Electric Vehicle Routing Problem with Simultaneous Pickup
and Delivery (EVRP-SPD).

Some studies of routing problems also investigate the potential charging load
in the power distribution network and the consumption of energy during
traveling by energy model. Ahmadi et al. [2021] investigated the importance of
vehicle dynamic parameters (e.g., road gradient, acceleration/deceleration,
and extra mass) in the energy model for Electric Vehicle Routing Problem with
Pickup and Delivery (EVRP-PD). Arias-Londoño et al. [2021] studied [
([)2]eLCVRP-CS-PDS for EFVs where the CSs were connected at power distribution
nodes. The uncertainty of energy consumption was addressed by Pelletier et al.
[2019] in Electric Vehicle Routing Problem with Energy Consumption Uncertainty
(EVRP-ECU).

Uncertain behavior of traffic parameters such as traffic volume and travel
time is also considered in a few studies of routing problems. For example,
Zhao et al. [2020] emulated the real traffic situation in Electric Vehicle
Routing Problem with Time-varying Traffic Conditions (EVRP-TTC). Similarly,
the uncertainty of travel time was addressed by Reyes-Rubiano et al. [2019] in
Electric Vehicle Routing Problem with Stochastic Travel times (EVRP-ST).

Unlike routing only for plug-in CSs, Fan et al. [2022] proposed a more
generalized Half-Open Time-Dependent Multi-Depot Electric Vehicle Routing
Problem Considering Battery Recharging and Swapping (HOTDMD-EVRP-BRS) approach
where EFVs have charging opportunities in both plug-in CSs and Battery
Swapping Stations.

###  4.4 Charge scheduling

Only the decision of charging locations from CS planning is not sufficient,
efficient charge scheduling contributes to successful freight operation.
Charge scheduling decides the charging location among CSs, time to charge, and
charging amount. Based on charging location, charging scheduling studies can
be categorized into three categories: charging at depot [Al-Hanahi et al.,
2022, Klein and Schiffer, 2022], charging at both depots and en-route CSs
[Woody et al., 2021, Sadati et al., 2022, Akbay et al., 2022, Ahmadi et al.,
2021, Afroditi et al., 2014, Bac and Erdem, 2021, Goeke and Schneider, 2015,
Goeke, 2019] and charging at customer’s locations (e.g., [Elahi and Avakh
Darestani, 2022]).

Full charging at CS [Andelmin and Bartolini, 2019, Yilmaz and Kalayci, 2022]
causes a huge peak load that necessitates the upgrade of the power supply,
whereas charge scheduling considering the flexibility of charging quantity or
mixed charging rate coordinated with the power supply can avoid the additional
cost of power system upgrade. To justify this statement, Woody et al. [2021]
investigated four charging behaviors, including immediate-full charging,
delayed-full charging, immediate-sufficient charging, and delayed-sufficient
charging. These charging behaviors were characterized by departure/arrival
time (e.g., charging EFVs to target SOC right before departure in delayed
charging; charging EFVs immediately after arrival in immediate charging) and
battery SOC (e.g., full charging before departure from depot; sufficient
charging to complete daily trips before departure). To resolve such issue, Al-
Hanahi et al. [2022] proposed a smart charging system at commercial facilities
(depot only) for heavy-duty EFVs considering Operational Schedule during
Parking Time (OSPT), Incentive-Based Demand Response (IBDR), and Vehicle To
Grid (V2G) technologies. Similarly, Elahi and Avakh Darestani [2022]
considered charging scheduling for CSs with EVSE with mixed charging rate.

###  4.5 Platoon scheduling

Although the long charging duration of EFVs may delay the shipment, and the
platooning requires perfect coordination between trucks’ travel schedules over
space and time, there is a great opportunity of saving energy consumption if
these two technologies can be co-optimized in platoon scheduling that decides
the time and location when EFVs form platoons along the route. On one hand,
the energy saving by platooning will significantly reduce the need for en-
route charging and avoid shipment delays. On the other hand, EFVs’ charging
activities provide flexibility for them to coordinate their spatial-temporal
trajectories, which offers more opportunities for EFVs to platoon [Alam and
Guo, 2023].

Scholl et al. [2023] optimized the total cost of energy consumption
considering platoon position using meta-heuristic approaches whereas Alam and
Guo [2023] minimized the total cost of delivery delay, hub charging, and en
route charging considering the charging station capacity and solved the
problem by CPLEX. These studies consider different platooning settings and
objectives. For example, Scholl et al. [2023] proposed platoon formation at
service points (some service points have charging facilities) along the route
where platoons are formed among waiting trucks. On the other hand, Alam and
Guo [2023] proposed platoon formation at CSs with consideration of CS capacity
and driver’s rest time.

###  4.6 Coupling CS planning and EFVs operation

While optimizing the CS siting and sizing, some studies explicitly model EFV
routing to better reflect the operational performance. These decision problems
are termed as Location Routing Problem (LRP), which integrates the Electric
Vehicle Routing Problem (EVRP) and the CS allocation problem in a single
modeling framework.

In traditional LRP, the allocation of depots is decided to serve customers in
a particular service area and the routes are designed from different depots to
serve customers using a fleet of vehicles. LRP can be tailored for EFV
logistics system by deciding the allocation of CSs instead of depot
allocation, and the route is designed from the depot to serve customers using
a fleet of EFVs, as shown in Figure 3. In summary, the goal of the LRP is to
consider the interaction between planning and operation when optimizing the CS
locations and the routing of EFV simultaneously.

Figure 3: Traditional LRP vs EFV-LRP

Li-ying and Yuan-bin [2015] proposed an optimization model considering
multiple types of CSs in LRP with delivery time window for freight systems.
This model was further extended by Schiffer and Walther [2017] to optimize
fleet size at the depot. Schiffer et al. [2018] decided the siting of CS and
optimal route while minimizing the Total Cost of Ownership (TCO) for a given
planning period using the data of a German non-food retailer, TEDi. TCO
includes purchasing costs of EFVs, installation costs of CS, drivers’ wage,
and operational costs.

In another study, Schiffer and Walther [2018] considered the uncertainty in
geographical customer distribution in the LRP through a robust location-
routing approach. Paz et al. [2018] introduced a multi-depot logistic system
for the first time in LRP with delivery time windows. The authors assumed that
EFV departs and returns to the same depot for each shipment. The multi-depot
study was further extended to multiple service periods (i.e., different depots
have different service times) in Wang et al. [2022].

The service to both linehaul (pickup freight) and backhaul (deliver freight)
customers along the route were introduced in LRP study by Yang et al. [2022].
The heterogeneous fleet of EFVs considering different freight capacity,
battery capacity, and operating cost was introduced in Çalık et al. [2021].
Wang et al. [2021b] decided the location of depots with either of two energy-
restore facilities (i.e., plug-in charging facility or BSS) and routing
decision simultaneously. Koç et al. [2019] studied the CSs allocation problem,
which could be invested and shared by several companies, along with the
routing decisions.

The coupling of CSs planning and operational scheduling was done for two
different mobility patterns, such as contracted mobility pattern and
subcontracted mobility pattern [Londoño and Granada-Echeverri, 2019]. In the
contracted mobility pattern, the operation schedule was formulated as EVRP
(i.e., minimize total traveling cost and consider freight load dynamics),
whereas subcontracted mobility was formulated as Shortest Path (SP) problem
(i.e., minimize total travel distance only, no consideration of load
dynamics).

##  5 RQ3: What methodologies have been employed to tackle the planning and
operational scheduling for EFVs?

The existing methodologies of CS planning and operational studies can be
categorized into four methodologies such as coverage- and queuing-based,
mathematical programming, simulation, and survey approaches as shown in Table
1. Mathematical programming models are most frequently used, particularly in
operational scheduling and coupling of location and routing decision problems.
However, for CS planning coverage-based and queuing-based approaches are
popular. As mathematical programming is a popular approach in operational
scheduling, the fundamental formulation is also described in section 6. The
extension of such formulation with corresponding logistic patters is described
in section 7.

Methodology | No. of literature
---|---
Coverage and queuing model | 2
Mathematical programming |
MIP | 27
IP | 7
MINLP | 4
LP | 2
NLP | 1
Heuristic | 4
CP | 1
Simulation | 8
Survey | 1
Table 1: Usage of different methodologies in existing literature

###  5.1 Coverage-based and queuing-based approach

The coverage-based approach aims to allocate charging stations to cover a
service area. Speth et al. [2022] and Sauter et al. [2021] decided the siting
of CSs by coverage-based approaches, which defines the candidate charging
locations with predefined distance (25 or 50 km in Speth et al. [2022]; 50 or
100 km in Sauter et al. [2021]). Charging events were estimated heuristically
based on a predefined battery range (300 km) and fixed percentage of charging
from the public CSs (assumed 50% in Speth et al. [2022]; 25% in Sauter et al.
[2021]) to satisfy the given travel demand. While simple to be implemented,
the coverage-based approach relies heavily on the parameter assumptions and
cannot be effectively used to determine the optimal sizing of CSs.

Queuing models can be leveraged to decide the sizing of each CS. The arrival
of EFVs at a CS was typically assumed to follow Poisson distributions. The
charging time was assumed to follow an approximate normal distribution in
[Sauter et al., 2021] or Markovian distribution in [Speth et al., 2022]. The
queuing model was solved by approximation formulas (extension of the Pollaczek
Khinchine formula) as described in Funke [2018].

###  5.2 Mathematical programming model

The above-mentioned coverage-based and queue-based approaches provide good
guidance for CS planning, however, the decisions made are not guaranteed to be
system optimal. To quantitatively optimize the sitting and sizing decisions of
CSs, mathematical programming approaches can be used. The application of the
mathematical programming for allocation of CS were proposed in LRPs.

In contrast to modeling most of the operational problems using holistic
mathematical programming approaches such as Mixed Integer Linear Programming
(MIP), Integer Linear Programming (IP), Mixed Integer Non-Linear Programming
(MINLP) etc., some studies used heuristic procedures to determine “good”
routing strategies, which may not be optimal. Montoya et al. [2016]
implemented Multi-space Sampling Heuristic (MSH) (introduced in Mendoza and
Villegas [2013]) for EFV routing problems. MSH has two phases: sampling and
assembling. During the sampling phase, routes from the traveling-salesman-
problem tours are sampled, whereas the assembling phase uses the set-
partitioning formulation. The first phase builds a pool of routes via a set of
randomized route-first cluster-second heuristics and the second phase
assembles a routing decision by solving a set partitioning formulation over
the routes that are stored in the pool. The former controls the feasibility
and the cost of each route while the latter controls the feasibility and the
cost of the whole solution. Andelmin and Bartolini [2019] implemented a multi-
start local search heuristic that is built based on the idea of a multi-start
method proposed in Martí et al. [2013]. Among the three phases of the multi-
start local-search heuristic, the first two phases iteratively construct new
solutions which are further improved by the local search algorithm and
finally, all vehicles’ routes associated with improved solutions are stored in
a route pool. The set of optimal routes from this route pool is decided from a
set partitioning algorithm in the third phase, which is further improved by
local search algorithms.

Reyes-Rubiano et al. [2019] implemented a simheuristic approach (integration
of simulation techniques within a heuristic framework to address
stochasticity) which consists of two components. First, a meta-heuristic
component searches for and stores promising solutions. Second, the simulation
component estimates different performance statistics of these solutions
considering the stochastic environment. Here, Reyes-Rubiano et al. [2019]
proposed a biased-randomized multi-start framework that integrated Monte Carlo
Simulation (MCS) to reflect the stochasticity of EFV travel time. Woody et al.
[2021] studied charging emission in terms of Marginal Emission Factor (MEF)
considering four charging profiles based on charging SOC (full, flexible) and
timing of charging at depot (immediate, delayed). MEF was estimated with the
regression method that was proposed in Donti et al. [2019]. The sensitivity of
the GHG emission was also studied in Woody et al. [2021] using a simulation
technique that was proposed with details in [Deetjen and Azevedo, 2019].

###  5.3 Simulation-based approach

Although mathematical programming is a rigorous quantitative approach for
making optimal operational decisions, this approach could be computationally
expensive for large-scale problems in the real world and formulating such
models may become challenging for very complex systems and introducing the
uncertainty. In contrast to the pursuit of optimal decisions, utilizing
simulation models can prove to be an effective method for comprehending the
intricate dynamics exhibited by a system when the simulation environment can
be effectively calibrated.

For example, after siting CSs in depots, Borlaug et al. [2021] investigated
the required upgrades in power distribution systems to serve the charging
demand of heavy-duty EFVs using bootstrap sampling of different scenarios in a
simulation-based framework. Considering the installation of DCFCs at existing
gas stations, Bischoff et al. [2019] developed a simulation model using MATSim
to estimate charging demand. Considering the installation of CSs at truck
stops, Hurtado-Beltran et al. [2021] developed a GIS model to study the
driving coverage of EFVs. Jahangir Samet et al. [2021] implemented simulation
model ( Battery Electric Vehicle POtential models) [Melliger, Marc, 2020] to
investigate the range anxiety and potential of EFVs adoption.

In context of operational scheduling problem, both commercial and open-source
simulation software were used. Napoli et al. [2021] used commercial
transportation planning software TransCAD (which has built-in route
optimization solving feature) for EFV routing problems. Commercial traffic
simulation software VISUM was used to simulate traffic network states (e.g.,
travel time, speed, etc) along the route in Chen et al. [2021] given the route
flow calculated from optimization models. Ewert et al. [2021] simulated food
retail distribution using open-source agent-based microscopic simulation
software MATSim. The jsprit was integrated into MATSim for route optimization
purposes. However, it’s worth noting that these software solutions are not
specifically designed for EFVs’ operational scheduling, and customization may
be needed to realistic represent the system and achieve optimal operational
decisions.

###  5.4 Survey approach

In addition to quantitative siting and sizing studies, Melander and Wallström
[2022] conducted qualitative surveys to compare four business strategies of
sharing CSs, including sharing existing privately owned infrastructure,
jointly building new infrastructure at a shared location, jointly building new
infrastructure at customers’ site, and organizing sharing through a third
party, to explore CS planning strategies to improve economic and environmental
benefits.

##  6 RQ4: How to formulate fundamental mathematical programming model?

LRP and operational scheduling problems were found to be formulated as IP, MIP
and MINLP models, with single study formulating MIP for CS planning. Two key
components of mathematical programming models such as objective functions and
constraints are different for CS planning, operational scheduling and coupling
of CS planning and operations. To enumerate all constraints and objective
function components in this review paper will be lengthy and unnecessary.
Instead, we will only mention the common and fundamental objective function
components and constraints found in the existing literature.

###  6.1 Model Objective

CS planning The deployment and installation costs of CSs with EVSE and
operational costs are minimized in the objective function of CS planning given
the unit cost of each CS, EVSE and CS operations [Alp et al., 2022, Li et al.,
2023].

Operational scheduling Due to limited traveling distance and the range anxiety
of EFVs, a common objective in the existing operational scheduling studies
(particularly routing problems) is found to minimize total traveled distance
[Sadati and Çatay, 2021, Andelmin and Bartolini, 2019, Sadati et al., 2022,
Afroditi et al., 2014, Goeke, 2019, Elahi and Avakh Darestani, 2022, Goeke and
Schneider, 2015, Yilmaz and Kalayci, 2022, Akbay et al., 2022] or its
corresponding energy consumption costs [Zhao et al., 2021, Pelletier et al.,
2019, Napoli et al., 2021, Scholl et al., 2023]. Since en-route charging will
significantly delay the freight delivery, minimizing the quantity of en-route
charging energy or its corresponding charging cost is also found in the
existing studies [Pelletier et al., 2018, Alam and Guo, 2022, Elahi and Avakh
Darestani, 2022, Arias-Londoño et al., 2021]. At the depot, the peak load
charge could be costly when significant charging activities are conducted at
the same time. Al-Hanahi et al. [2022] proposed a smart charging system at the
depot that minimizes the peak demand of the aggregate charging load. There are
some studies that converted all cost components into time or Value Of Time
(VOT). For example, traveling time and charging time are minimized in Ahmadi
et al. [2021], Cortés-Murcia et al. [2019] and VOT is minimized in Reyes-
Rubiano et al. [2019], Chen et al. [2021], Van Duin et al. [2013].

In the freight industry, some other costs are also significant due to the
unique freight characteristics. For example, due to the higher hourly wage of
a truck driver, minimizing truck driver’s wage costs including overtime costs
have been considered in the existing studies [Bac and Erdem, 2021, Goeke and
Schneider, 2015, Pelletier et al., 2019, Van Duin et al., 2013, Zhao et al.,
2021, Wang et al., 2021a]. Due to tight delivery schedule and to ensure
customer satisfaction in the competitive freight industry, minimizing delay
cost (i.e., the penalty for the delivery delay) is also considered in recent
studies [Alam and Guo, 2023, Zhao et al., 2020, Bac and Erdem, 2021, Zhao et
al., 2021]. Compared with traditional ICEV, EFV is more expensive currently.
Therefore, operational studies, especially the studies of fleet sizing
problems, minimize EFV dispatching cost incurred from purchasing (fixed) or
renting (per unit time of usage) vehicles [Van Duin et al., 2013, Pelletier et
al., 2019, Zhao et al., 2020, Hiermann et al., 2016].

Coupling CS planning and operational scheduling Most of the studies on LRP
minimized costs for installing new CSs, maintenance of these CSs, charging,
driver’s wage, and delay[Alp et al., 2022, Londoño and Granada-Echeverri,
2019, Li-ying and Yuan-bin, 2015, Yang et al., 2022, Wang et al., 2021b, Koç
et al., 2019]. In addition, total traveled distance (corresponds to energy
consumption costs) was minimized [Çalık et al., 2021].

###  6.2 Constraints

CS planning A set of constraints were formulated for planning the deployment
of CS and installation of EVSE over the planning horizon. Balance-equation
constraints for EVSE balanced the purchasing and salvaging of EVSE. Another
set of constraints was added to ensure the completion of freight delivery. The
budget constraints controlled the new investment for CSs and EVSE considering
discount factors for different years.

Operational scheduling Freight network constraints: Freight networks can be
modeled as directional graphs. To ensure the realistic representation of
freight operation, the following graph constraints are typically imposed
(e.g., [Yilmaz and Kalayci, 2022, Schneider et al., 2014, Ahmadi et al.,
2021]).

  * •

In-degree and out-degree constraints define that exactly one edge enters and
leaves each node of the freight network associated with each customer,
respectively. This constraint ensures that EFV departs after visiting a
customer or CS and flow conservation is guaranteed in routing.

  * •

To run the shipment cycle on a regular basis, EFV should return to its origin
after completing the deliveries along the route. This property can be ensured
by constraining that the number of EFVs leaving the depot equals the number of
EFVs entering the depot.

  * •

Sub-tour elimination constraint prevents a trip visiting few cities in the
given set and return to the starting city.

  * •

Each customer is visited exactly once which enforces the connectivity of
customer visits so that overall freight transportation is organized and
streamlined.

Freight load constraints: Freight load constraints are crucial for tracking
freight load while EFV moving along the route and satisfying customer demand.
Some typical constraints found in the existing literature (e.g., Yilmaz and
Kalayci [2022], Schneider et al. [2014], Ahmadi et al. [2021], Arias-Londoño
et al. [2021]) are as follows.

  * •

The freight flowing through each edge is less than the freight capacity of the
EFVs.

  * •

The amount of freight to be delivered by an EFV after visiting the CS remains
the same because charging has no impact on freight load [Yilmaz and Kalayci,
2022].

  * •

The weight of the EFVs right after leaving the depot is equal to the sum of
the freight weight and EFV’s own dead weight.

Battery SOC constraints: The constraints for tracking battery SOC are
fundamental to model the energy dynamics and charging behavior of EFVs.
Dynamics of battery SOC can be modeled by the following constraints [Van Duin
et al., 2013, Pelletier et al., 2018, Yilmaz and Kalayci, 2022]

  * •

The real-time battery energy of each EFV shall not exceed its battery
capacity. Based on the distance traveled, the battery SOC is reduced along the
route unless charged. The remaining distance (i.e., the distance that can be
traveled by the remaining battery SOC) is also monitored. The initial
remaining distance is equal to the maximum battery range of the EFV. The
remaining distance is updated by subtracting the distance traveled between
nodes based on MPGe. No trip shall be conducted with a trip distance exceeding
the remaining distance of a vehicle.

  * •

Without loss of generality, a constraint is imposed to ensure that the SOC
needs to be greater than a minimum percentage and below the maximum percentage
of battery capacity. For example, EFV are not allowed to reach extremely low
SOC or constantly be fully charged to enhance the longevity of battery
performance and/or emulate real-life user behavior. However, full charging is
assumed at en-route CS by some existing studies (e.g., Elahi and Avakh
Darestani [2022]).

  * •

In charging scheduling problem, each EFV will occupy at most one charger per
period [Pelletier et al., 2018]. Such constraint is needed to determine the
charging availability and charger allocation.

  * •

To address the variable charging speed based on different battery SOC, a
charger-specific piece-wise linear function needs to be implemented to
formulate a Linear Programming (LP) model.

  * •

Majority of the studies assume that EFVs leave the depot fully charged. A
constraint is required to ensure this initial condition.

  * •

Considering smart charging with V2G capability where bi-directional power flow
is considered, in the real-time operational scheduling mode, the lower bound
of charged power (i.e., the upper bound of discharged power) at a particular
timestamp for an EFV is determined by the availability of the EFV at the CS
and the maximum discharge power at that particular timestamp. On the other
hand, the upper bound of charge power is determined by the availability of the
EFV at the CS and the maximum charge power [Al-Hanahi et al., 2022].

Fleet sizing constraints: For the fleet sizing problem, the number of utilized
EFVs per period can not exceed the available number of EFVs [Elahi and Avakh
Darestani, 2022, Yeomans, 2021, Ioannou et al., 2020]. In addition, the total
number of EFVs allocated for an OD pair should be greater or equal to the
demand of the OD pair [Chen et al., 2021].

Platoon constraints: The total of EFVs that join a platoon may not exceed the
maximum platoon capacity and at least two EFVs are required to form a platoon
[Alam and Guo, 2023]. When two or more EFVs form a platoon at any stopping
point, their respective departure times should be the same. When considering
the vehicle sequence in the platoon (e.g., a leading vehicle and following
vehicles), constraints are also needed to indicate the position of each
vehicle [Scholl et al., 2023]

Coupling CS planning and operational scheduling The majority of the
constraints are found similar to mathematical programming in CS planning and
operational scheduling. A new graph constraints, in a multi-depot-based
freight network, is that each route is constrained to include only one depot
Wang et al. [2021b]. Given the consideration of multiple service periods of
the depot, the sequence of routes that are performed by each EFV needs to be
maintained in LRP [Wang et al., 2022]. These constraints are not unique
constraints of LRP, therefore, these can also be valid for operational
scheduling problems in the context of multi-depot-based freight network.

##  7 RQ5: How do different logistics patterns give rise to new challenges

We introduce different freight delivery patterns found in the existing
literature in the following subsections to facilitate the discussion of
operational problems. Different types of logistic patterns cause new
challenges to adjust the methodologies accordingly for proper planning and
operational decisions. In existing EFV literature, these adjustments due to
unique logistic pattern in fundamental model are also mentioned after the
introduction of each pattern.

Freight delivery patterns considered in the existing EFV literature can be
classified into five categories, including heterogeneous customers with and
without due time, flexible delivery location, non-simultaneous pickup and
delivery (i.e., dedicated-service logistics), simultaneous pickup and delivery
and two-echelon delivery.

###  7.1 Heterogeneous customers with and without due time

Figure 4(a) illustrates Periodic Routing of Electric Vehicles with Due Time
(PREV-DT) where customers’ orders are due for delivery at a scheduled time
period. Some customers must receive their orders prior to the delivery due
time, while others can wait until the end of the period. In Figure 4(a), we
illustrate two EFVs serving two different routes, which are plotted with solid
and dash line types, respectively. Both EFVs depart from the depot initially
and need to finish their delivery tasks along the routes and return to the
depot. Each route has different charging infrastructure accessibility and
different customers have different delivery due times. The EFV serving the
solid-lined route can access to onsite charging infrastructure at customer “2”
and recharge some energy amount so that it can deliver customer “3” in time
given that customer “3” has delivery due time. On the other hand, the second
EFV chooses to fully recharge the battery due to no urgency given that
customer “5” does not have a delivery due time.

In order to avoid any penalty or delay cost, customers with due time need to
be served within the delivery time window [Akbay et al., 2022, Zhao et al.,
2021, Al-Hanahi et al., 2022, Hiermann et al., 2016, Napoli et al., 2021, Taş,
2021]. These constraints are crucial for routing problems and can be modeled
as hard constraints to guarantee compliance with the time windows.

(a) PREV-DT

(b) EVRP-FD

(c) EVRP-PD-TW-PR

(d) EVRP-SPD

Figure 4: Popular logistic patterns frequently implemented in EFV studies

###  7.2 Customers with flexible delivery locations

Figure 4(b) illustrates the flexible delivery locations with 10 customers and
a total of 23 alternative delivery locations in the system. The scattered
numbers represent the index of customers and the same numbers represent the
alternative delivery locations of that customer. For instance, customer “2”
has four scattered alternative delivery locations. EFV “1” (traveling on the
solid-line route) departs from the depot, visits customers “1”,“2”,“3”,“4”,
and stops at CS. After recharging, it resumes by visiting customers “5” and
“6”, and then returns to the depot at the end of its tour. Similarly, EFV “2”
(traveling on the dashed-line route) departs from the depot, serves customers
“7” through “10”, completes its tour, and returns to the depot without
recharging.

###  7.3 Non-simultaneous (Dedicated-service logistics) pickup and delivery

Two types of freight pickup and delivery patterns such as non-simultaneous
(i.e., dedicated-service logistics) and simultaneous pickup and delivery were
found in the existing studies of operational scheduling problems. Figure 4(c)
illustrates non-simultaneous pickup and delivery (i.e., dedicated-service
logistics) with en-route flexible charging (i.e., flexible charging amount)
given pickup and delivery time windows. 222The “time windows” refer to the
earliest and latest time range of events, such as departure or delivery.
Assume a set of customers that have scheduled for pickup and delivery with
specific time windows as shown in circles and diamonds, respectively. The
numbers of the locations denote the customer indexes. At the depot, a CS is
deployed for overnight charging. The EFVs should start from and return to the
depot within the scheduled hours. There could be other en-route CSs in the
system. To extend the range of EFVs, flexible charging en-route is allowed. In
this figure, two EFVs depart from the home base (e.g., depot) and serve
customers “1” and “2”, respectively. The en-route charging is not needed for
serving customer “1”, whereas en-route charging is needed for serving customer
“2”.

In dedicated-service logistics, an additional constraint is needed so that an
identical EFV will pick up and deliver the freight for the same customer, and
the pick-up should be finished before the delivery for the same customer [Zhao
et al., 2021].

###  7.4 Simultaneous pickup and delivery

SPD is a generalization of non-simultaneous pickup and delivery by allowing
flexibility to both pickup and delivery of freights at each customer location.
Figure 4(d) illustrates the SPD freight logistics pattern. Every EFV has been
labeled by three parameters - “the amount of freights to be delivered/the
amount of freights being picked up/the current SOC of the EFV”. The amount of
freight in the EFV changes depending on the pickup and delivery requests by
the customers. For example, an EFV (starts with 100% SOC) needs to deliver 55
units of freights and pickup packages along the solid-lined route. To finish
these delivery and pickup tasks, it recharges the battery at en-route CSs to
its full capacity. When this EFV returns to the starting point of the route
(e.g., depot), it delivered all of the freights and picked up 47 units from
the customers to whom the freights are delivered. After arriving at the home
base, this EFV will then deliver 47 units of packages to another set of
customers along the dash-lined route.

In contrast to freight vehicles for delivery only, vehicles in SPD needs
additional constraint so that freight flowing through each edge does not
exceed the freight capacity of the EFVs after adjustment of simultaneous pick
up and delivery. For SPD, freight weight leaving the depot is zero because no
freight will be picked up by EFVs from the depot to customers.

###  7.5 Two-echelon delivery

In two-echelon delivery, first echelon refers to the freight transportation
from the central warehouse to satellites (i.e., the last stop of middle-mile
trip) by ICEV trucks and the second echelon refers to the transportation from
satellites to customers using EFVs Akbay et al. [2022]. The variants of
routing problem from this logistic pattern is called Two Echelon Electric
Vehicle Routing Problem with Time Windows (2E-EVRP-TW). The charging event can
be initiated at satellites (first-echelon) and an alternative transportation
mode (e.g., walking, drones, bikes, etc.) for freight delivery to close-
distanced customers during charging (second echelon) Cortés-Murcia et al.
[2019].

The equality constraint of freight to be delivered by an EFV before and after
visiting the CS is relaxed in a two-echelon delivery where an alternative
transportation mode is assigned to deliver freight to nearby customers when
charging EFVs.

##  8 RQ6: Which solution approaches have been utilized in addressing
mathematical programming? How do they differ?

In small-scale case studies, exact methods were used by calling commercial
global optimal solvers, such as Gurobi and CPLEX [Arias-Londoño et al., 2021,
Pelletier et al., 2018, Alam and Guo, 2023, Bac and Erdem, 2021, Cortés-Murcia
et al., 2019, Akbay et al., 2022]. However, for the large-scale problems,
traditional exact methods could be time-consuming to obtain a global optimal
solution due to NP-hardness. Meta-heuristic and hybrid of multiple meta-
heuristic algorithms can be used to obtain solutions of reasonable quality for
practical purposes. Choosing meta-heuristic algorithms and determining how to
hybridize them is contingent upon the specific nature of the problems,
available computational resources, and desired levels of solution quality.
However, limited studies has compared the computational performance of
different computational algorithms based on standardized test cases, which is
a valuable future research direction.

Mathematical programming solving approach | No. of literature
---|---
Solver | CPLEX | 4
| Gurobi | 3
| chuffed | 1
| jsprit | 1
| shortec | 1
| TransCAD built-in solver | 1
| DICOPT | 1
Decomposition | Branch-and-price | 3
| Benders decomposition | 1
| LR-ADMM decomposition | 1
| Column generation | 1
Heuristics | Heuristics | 6
Meta-heuristics | Adaptive LNS | 6
| SAA | 2
| Adaptive ACA | 1
| BA | 1
| GA | 1
| LNS | 1
| ACA | 1
| CW | 1
| DP | 1
| VNS | 1
Hybrid meta-heuristics | VNS-TS | 2
| Adaptive VNS-TS | 1
| ALNS-DP | 1
| CW-Adaptive VNS | 1
| CW-VNS | 1
| CW-IG-ALNS | 1
| GMCA-INSGA-II | 1
| Iterated Local Search-VND-Set partitioning | 1
Table 2: Solving approaches implemented for mathematical programming models in
prior studies

Among nature-based meta-heuristic algorithms, Genetic Algorithm (GA) was
implemented in [Wang et al., 2021a] in which the study problem had two parts:
first, use Case-Based Reasoning (CBR) to forecast energy consumption and
travel time of each route by implementing neural network and fuzzy inference
mechanism; second, optimize vehicle routing by implementing GA using the
forecast energy consumption and travel time. In contrast to GA, which can only
be solved centrally, Ant Colony Algorithm (ACA) is well suited for distributed
computing, which makes itself compatible to combine with other meta-heuristic
algorithms. ACA and adaptive ACA was implemented in Zhao et al. [2021] and
Zhao et al. [2020], respectively, for the routing problem. Bat Algorithm (BA)
is another nature-based meta-heuristic, which is adopted in Yeomans [2021] to
produce multiple alternative routing solutions.

In addition to nature-based meta-heuristic algorithms, there exists another
category of meta-heuristics based on local search. While nature-based
algorithms navigate the search space using a predefined population to discover
high-quality solutions, local-search-based algorithms begin with an initial
solution and focus on seeking improved solutions within a defined
neighborhood. However, some local search heuristics may not achieve a high-
quality solution, as they prioritize time-efficient solutions that balance
optimality with achieving satisfactory outcomes.

Local-search-based meta-heuristic algorithms typically combine perturbation
and greedy approaches to escape local minimum. For example, Simulated
Annealing Algorithm (SAA) tries to overcome the dependency of initial value
and being stuck on local optimal solutions by utilizing a probability function
(i.e., metropolis criterion) which allows “locally” worse solutions to be
selected during the optimization process [Elahi and Avakh Darestani, 2022].
Besides, SAA has desired properties, such as robustness, flexibility, global
search, and ease of implementation.SAA was implemented for routing problems
[Fan et al., 2022, Elahi and Avakh Darestani, 2022]. Variable Neighborhood
Search (VNS) algorithm is a combination of the descent method that moves to a
local minimum, and the shaking method that escapes from being trapped in a
local minimum [Bac and Erdem, 2021]. Yilmaz and Kalayci [2022], Akbay et al.
[2022] implemented VNS leveraging the initial solution using Clarke and Wright
(CW) saving algorithm. Variable Neighborhood Descent (VND) is deterministic
version of VNS that was implemented by Bac and Erdem [2021] for the local
search procedure leveraged solution from VNS. An adaptive general VNS was also
implemented for milk collection problem in Erdem [2023]. Compared with VNS,
another local-search meta-heuristic algorithm Large Neighborhood Search (LNS)
[Pelletier et al., 2019] has a high possibility to escape local optima and
explore new regions of the search space, because if there is no yield of
improvements from local search, LNS applies a large-scale perturbation through
destroy-repair approach (the destroy component destructs some parts of the
current solution while the repair method rebuilds the destroyed solution) to
the current solution. The extension of LNS, adaptive LNS was found popular to
be implemented in the existing literature [Hiermann et al., 2016, Goeke and
Schneider, 2015]. Unlike LNS, adaptive LNS is not restricted to one destroy
and repair heuristic rather, it chooses solutions in every iteration from a
pool of heuristics based on past success [Lutz, 2015].

In recent years, the hybridization of meta-heuristic algorithms has become
popular due to its ability to combine the strengths and complementary features
of different meta-heuristic algorithms to create more powerful optimization
algorithm. Sadati and Çatay [2021], Sadati et al. [2022] proposed a hybrid
algorithm of VNS and Tabu Search (TS) in which TS was implemented within VNS
for solving problem-specific neighborhood structures as part of the local
search mechanism [Sadati and Çatay, 2021, Sadati et al., 2022]. It is found
that the hybrid of VNS and TS outperforms adaptive VNS and MSH in terms of
computation time with an acceptable solution. When the problem consists of
several routing problems, iterated local search meta-heuristic framework is a
good fit. This framework generates a set of local optimal routes in Cortés-
Murcia et al. [2019] by solving MIP model using VND algorithm (alternating
local search and perturbation of customer sets). Once the iterated local
search is done, the set partitioning (IP) optimization model is solved to find
optimal routes.

In addition to hybridizing meta-heuristic algorithms, there are instances
where hybrids of exact algorithms such as branch and bound, and decomposition
methods (e.g., column generation) are also implemented.  Klein and Schiffer
[2022], Klein [2022], Hiermann et al. [2016] implemented branch and price
algorithm to solve large-scale case studies of charging scheduling for EFVs.
Compared with meta-heuristic algorithms, a hybrid of exact algorithms
guarantee global convergence theoretically, but the convergence rate can be
slow for some larger-scale cases. Combining meta-heuristic and exact
algorithms is also possible to identify good initial solutions first using
meta-heuristic algorithms and then use exact algorithms to close the
optimality gap.

Besides, other tools/solvers that were used for routing problems are “Chuffed”
[Ahmadi et al., 2021], “jsprit” [Ewert et al., 2021], and “Shortec” [Van Duin
et al., 2013]. Chuffed is a Constraint Programming (CP) solver based on lazy
clause generation. A lazy clause generation is a hybrid approach to constraint
solving that combines features of finite domain propagation and boolean
satisfiability [Chu et al., 2018]. Note that compared to MIP, CP provides a
greater degree of flexibility when non-linear constraints are handled. The
Java-based open-source solver jsprit is dedicated to solve traveling salesman
and vehicle routing problems. Besides open-source solvers, commercial software
such as Shortec was also implemented for EFVs routing problems that use
sequential insertion heuristics to solve the problems [GraphHopper, 2014].

In small-scale case studies for LRP, exact methods were used for a globally
optimal solution using commercial solvers such as Gurobi [Schiffer and
Walther, 2017], CPLEX [Li-ying and Yuan-bin, 2015, Paz et al., 2018, Londoño
and Granada-Echeverri, 2019] and DICOPT [Londoño and Granada-Echeverri, 2019].
For large-scale problems, directly leveraging commercial solvers could be
time-consuming to obtain a globally optimal solution. Therefore, decomposition
algorithms (e.g., bender’s decomposition [Çalık et al., 2021]) and a hybrid of
exact algorithms (e.g., branch and price [Wang et al., 2021b]) were
implemented to solve large-scale case studies. A hybrid of Lagrangian
relaxation (LR) and Alternating Direction Method of Multipliers (ADMM)
decomposition framework was proposed by Yang et al. [2022] to decouple the LRP
into a recharging station location problem and an electric vehicle routing
problem. The solution quality was evaluated by the optimality gap for each
iteration.

Due to complexity and scaling issues, similar to operational scheduling
studies, many LRP studies also proposed meta-heuristic, and hybrid meta-
heuristic algorithms for approximate solutions. For example, Koç et al. [2019]
implemented LNS algorithm and introduced several new operators, as well as
initialization, intensification, and diversification procedures. The hybrid of
adaptive LNS and Dynamic Programming (DP) was found effective compared with
exact solver solutions for small-scale instances [Schiffer et al., 2016, 2018,
Schiffer and Walther, 2018]. Similarly, a hybrid of adaptive VNS and TS was
compared with exact solutions of small-scale instances in [Li-ying and Yuan-
bin, 2015] where TS was implemented for intensification mechanisms. A DP-based
heuristic, which combines restricted DP and Prim’s algorithms was implemented
to solve larger instance of dynamic routing optimization problem with
uncertainties [Ünal et al., 2022]. Wang et al. [2022] introduced a hybrid of
Gaussian Mixture Clustering Algorithm (GMCA) and Improved Nondominated Sorting
Genetic Algorithm-II (INSGA-II) for the proposed LRP, where GMCA assigns
customers with depots in various service periods to reduce computational
complexity and INSGA-II obtains the Pareto optimal solution. This hybrid
approach was also compared with multi-objective GA, Paricle Swarm Algorithm
(PSA), and ACA. In another study, Guo et al. [2022] solved the LRP considering
non-linear charging profile and battery degradation using a hybrid of three
algorithms such as CW, Iterative Greedy algorithm (IG) and adaptive LNS.

##  9 Conclusions and RQ7: What are the potential research areas and
challenges that have not been adequately explored

Transportation electrification in the freight sector is a possible trend in
the near future. This paper reviews the existing research on the challenges
and opportunities of adopting electric vehicles in freight transportation. In
particular, we focus on three major themes, including CS planning, EFVs
operational scheduling, and the coupling of CS planning and operation, with a
detailed classification of the proposed methodologies and solution approaches.
Based on our review, we identified further research directions that can
further facilitate freight electrification, as summarized below.

  1. 1.

The existing research typically focuses on centralized decision-makers to
optimize the planning and operational decisions. However, these decisions can
involve multiple stakeholders, such as charging station providers, power
utilities, and freight companies, who make decisions in a decentralized manner
to optimize their own objectives but whose decisions will interact with each
other. The application of a game theoretical modeling framework can be an
effective strategy to better understand their decentralized interaction and
derive effective policy incentives to guide the system evolvement for societal
benefits.

  2. 2.

The existing literature mainly focuses on the planning and operation of EFVs
considering transportation systems. However, freight electrification may have
significant impacts on both power transmission and distribution systems due to
higher charging power and larger battery capacity. In addition to planning
charging stations, a necessary power system upgrade may be needed to support
the additional charging power. Furthermore, considering V2G, EFVs fleet can be
valuable distributed energy resources to support power system operation
compared with passenger vehicles because of larger battery capacity and more
predictable travel and charging patterns.

  3. 3.

Most of the studies focus on planning and operation of EFVs for normal
scenarios considering economic efficiency. However, freight electrification
could face significant challenges during disruptions due to the urgency of
good delivery and potential charging inaccessibility due to power or
transportation disruptions. More studies will be needed to consider the
challenges and opportunities of EFVs during emergencies to enhance system
reliability and resilience.

  4. 4.

From a modeling perspective, there are some critical features that can be
further considered to shed light on the efficiency and feasibility of adopting
EFVs, including the uncertainties of charger/driver availability, the
influence of charging/electricity prices, human-drive v.s. automated, special
types of logistics (e.g., logistics for humanitarian, human organs,
refrigerated goods, etc), energy consumption factors (e.g., road pavement
condition [Alam et al., 2020], seasonal and geospatial impact [Hao et al.,
2020]), and land use restriction.

  5. 5.

Although existing studies have proposed different computational algorithms,
there is still a lack of understanding on the comparison of computational
performance and solution qualities among different solution approaches. A
comparison using standardized models and benchmark test systems for different
type of problems will be beneficial.

  6. 6.

Solutions of planning/operational decision models can be validated through
field experiments (e.g., pilot studies). Validated results from pilot studies
will further encourage more stakeholders in freight systems to consider EFVs
in their decision-making process and promote EFVs adoption.

## ABBREVIATIONS

GHG

    Greenhouse Gas
2E-EVRP-TW

    Two Echelon Electric Vehicle Routing Problem with Time Windows
ACA

    Ant Colony Algorithm
ADMM

    Alternating Direction Method of Multipliers
BA

    Bat Algorithm
BEVPO

    Battery Electric Vehicle POtential model
BSS

    Battery Swapping Station
CBR

    Case-Based Reasoning
CS

    Charging Station
CP

    Constraint Programming
DP

    Dynamic Programming
EV

    Electric Vehicle
CW

    Clarke and Wright
DCFC

    Direct Current Fast Charger
EFV

    Electric Freight Vehicle
eLCVRP-CS-PDS

    Electric Light Commercial Vehicles Routing Problem with Charging Station Location and impact on Power Distribution System
EVRP

    Electric Vehicle Routing Problem
EVRP-ECU

    Electric Vehicle Routing Problem with Energy Consumption Uncertainty
EVRP-FD

    Electric Vehicle Routing Problem with Flexible Deliveries
EVRP-PD

    Electric Vehicle Routing Problem with Pickup and Delivery
EVRP-PD-TW-PR

    Electric Vehicle Routing Problem with Pickup and Delivery, Time Windows, and Partial Recharge
EVRP-ST

    Electric Vehicle Routing Problem with Stochastic Travel times
EVRPTW

    Electric Vehicle Routing Problem with Time Windows
EVRPFTW

    Electric Vehicle Routing Problem with Flexible Time Windows
EVRPTWsc

    Electric Vehicle Routing Problem with Time Windows and satellite customers
EVRP-TTC

    Electric Vehicle Routing Problem with Time-varying Traffic Conditions
EVRP-SPD

    Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery
EVSE

    Electric Vehicle Supply Equipment
GA

    Genetic Algorithm
GMCA

    Gaussian Mixture Clustering Algorithm
GTS

    Granular Tabu Search
GVWR

    Gross Vehicle Weight Ratings
HOTDMD-EVRP-BRS

    Half-Open Time-Dependent Multi-Depot Electric Vehicle Routing Problem Considering Battery Recharging and Swapping
IBDR

    Incentive-Based Demand Response
ICEV

    Internal Combustion Engine Vehicle
IG

    Iterative Greedy algorithm
IP

    Integer Linear Programming
INSGA-II

    Improved Nondominated Sorting Genetic Algorithm-II
LRP

    Location Routing Problem
LNS

    Large Neighborhood Search
LP

    Linear Programming
LR

    Lagrangian relaxation
MSH

    Multi-space Sampling Heuristic
MVRPTW

    Mixed vehicle routing problem with Time Windows
MCS

    Monte Carlo Simulation
MEF

    Marginal Emission Factor
MDGVRP

    Multi-Depot Green Vehicle Routing Problem
MIP

    Mixed Integer Linear Programming
MINLP

    Mixed Integer Non-Linear Programming
OSPT

    Operational Schedule during Parking Time
PREV-DT

    Periodic Routing of Electric Vehicles with Due Time
PSA

    Paricle Swarm Algorithm
QP

    Quadratic Programming
SAA

    Simulated Annealing Algorithm
SOC

    State Of Charge
SP

    Shortest Path
SPD

    Simultaneous Pickup and Delivery
TS

    Tabu Search
TCO

    Total Cost of Ownership
VOT

    Value Of Time
VMT

    Vehicle Miles Travelled
VND

    Variable Neighborhood Descent
VNS

    Variable Neighborhood Search
VRP

    Vehicle Routing Problem
V2G

    Vehicle To Grid
Univ.

    University
TR Part E

    Transportation Research Part E
Sci.

    Science
COR

    Computers and Operations Research
WSEAS

    World Scientific and Engineering Academy and Society
TCS

    Transactions on Circuit and Systems
SORT

    Statistics and Operations Research Transactions
Idescat

    Statistical Institute of Catalonia
TR Part B

    Transportation Research Part B
EST

    Environmental Science and Technology
TR Part C

    Transportation Research Part C
MPE

    Mathematical Problems in Engineering
EJOR

    European Journal of Operational Research
IJPR

    International Journal of Production Research
TRP

    Transportation Research Procedia
TRENG

    TRansportation ENGineering
NCST

    National Center for Sustainable Transportation
IJIEC

    International Journal of Industrial Engineering Computation
T-ITS

    Transactions on Intelligent Transportation Systems
ESPR

    Environmental Science and Pollution Research
SCS

    Sustainable Cities and Society
BSE

    Business Strategy and the Environment
ERIS

    Environmental Research: Infrastructure and Sustainability
ITOR

    International Transactions in Operational Research
Transp. Lett.

    Transportation Letters
JESTR

    Journal of Engineering Science & Technology Review
EUT

    Edizioni Università di Trieste
SD-LCI

    Schloss Dagstuhl – Leibniz Center for Informatics
IOP

    Institute Of Physics
Fraunhofer ISI

    Fraunhofer-Institut für System- und Innovationsforschung
EMaTTech

    Eastern Macedonia and Thrace institute of Technology

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  *[EFV]: Electric Freight Vehicle
  *[VMT]: Vehicle Miles Travelled
  *[GHG]: Greenhouse Gas
  *[EFVs]: Electric Freight Vehicle
  *[CS]: Charging Station
  *[CSs]: Charging Station
  *[EVs]: Electric Vehicle
  *[EV]: Electric Vehicle
  *[DCFC]: Direct Current Fast Charger
  *[EVSE]: Electric Vehicle Supply Equipment
  *[DCFCs]: Direct Current Fast Charger
  *[SOC]: State Of Charge
  *[VRP]: Vehicle Routing Problem
  *[EVRPTW]: Electric Vehicle Routing Problem with Time Windows
  *[MVRPTW]: Mixed vehicle routing problem with Time Windows
  *[EVRP-PD-TW-PR]: Electric Vehicle Routing Problem with Pickup and Delivery, Time Windows, and Partial Recharge
  *[EVRP-FD]: Electric Vehicle Routing Problem with Flexible Deliveries
  *[SPD]: Simultaneous Pickup and Delivery
  *[EVRP-SPD]: Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery
  *[EVRP-PD]: Electric Vehicle Routing Problem with Pickup and Delivery
  *[[]:
  *[EVRP-ECU]: Electric Vehicle Routing Problem with Energy Consumption Uncertainty
  *[EVRP-TTC]: Electric Vehicle Routing Problem with Time-varying Traffic Conditions
  *[EVRP-ST]: Electric Vehicle Routing Problem with Stochastic Travel times
  *[HOTDMD-EVRP-BRS]: Half-Open Time-Dependent Multi-Depot Electric Vehicle Routing Problem Considering Battery Recharging and Swapping
  *[OSPT]: Operational Schedule during Parking Time
  *[IBDR]: Incentive-Based Demand Response
  *[V2G]: Vehicle To Grid
  *[LRP]: Location Routing Problem
  *[EVRP]: Electric Vehicle Routing Problem
  *[TCO]: Total Cost of Ownership
  *[BSS]: Battery Swapping Station
  *[SP]: Shortest Path
  *[LRPs]: Location Routing Problem
  *[MIP]: Mixed Integer Linear Programming
  *[IP]: Integer Linear Programming
  *[MINLP]: Mixed Integer Non-Linear Programming
  *[MSH]: Multi-space Sampling Heuristic
  *[MCS]: Monte Carlo Simulation
  *[MEF]: Marginal Emission Factor
  *[VOT]: Value Of Time
  *[ICEV]: Internal Combustion Engine Vehicle
  *[LP]: Linear Programming
  *[PREV-DT]: Periodic Routing of Electric Vehicles with Due Time
  *[2E-EVRP-TW]: Two Echelon Electric Vehicle Routing Problem with Time Windows
  *[GA]: Genetic Algorithm
  *[CBR]: Case-Based Reasoning
  *[ACA]: Ant Colony Algorithm
  *[BA]: Bat Algorithm
  *[SAA]: Simulated Annealing Algorithm
  *[VNS]: Variable Neighborhood Search
  *[CW]: Clarke and Wright
  *[VND]: Variable Neighborhood Descent
  *[LNS]: Large Neighborhood Search
  *[TS]: Tabu Search
  *[CP]: Constraint Programming
  *[LR]: Lagrangian relaxation
  *[ADMM]: Alternating Direction Method of Multipliers
  *[DP]: Dynamic Programming
  *[GMCA]: Gaussian Mixture Clustering Algorithm
  *[INSGA-II]: Improved Nondominated Sorting Genetic Algorithm-II
  *[PSA]: Paricle Swarm Algorithm
  *[IG]: Iterative Greedy algorithm