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Understanding Freight Forwarders Time-of-Day Choice Decision Making FrameworkA Greek Case Study
Ioanna Kourounioti*
University of the Aegean,
Department of Shipping, Trade and Transport
PhD Candidate
Korai 2A,
82100, Chios, Greece
Tel: +30-22710-35284
E-mail: [email protected]
Amalia Polydoropoulou
University of the Aegean,
Department of Shipping, Trade and Transport
Professor
Korai 2A,
82100, Chios, Greece
Tel: +30-22710-35263
E-mail: [email protected]
*corresponding author
Word count: 5589 words+1 Figure + 1 Table = 6089 words
Submitted for presentation at the 94th Annual Meeting of the Transportation Research Board
January 2015 and publication in Transportation Research Record
Under the TRBβs Intermodal Freight Transportation Committee (AT050) Call for Papers
August1st, 2014
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ABSTRACT
The general aim of this paper is to illustrate the decision-making framework of Freight
Forwarders (FFs) regarding arrival time-of-day at a seaport terminal to pick-up an import
container. Container terminals are regarded as a key element of logistics chains since they are
a link between sea and the hinterland transportation modes. Workload forecasting is essential
when it comes to truck arrivals for the avoidance of bottlenecks and the smooth integration of
container terminals in the supply chain. Up to now, only simulation models have been
developed to predict the time-of-day arrival of trucks in terminals. This research proposes the
development of a behavioral framework that incorporates the various factors affecting the
time-of-day choice. In order to identify these factors a pilot study was conducted involving
Greek FFs. The suggested methodology will require the estimation of a hybrid model system
consisting of a discrete choice model and a latent variable model. The developments of such
predictive tools are expected to enhance decision and policy making at a terminal, supply
chain and national level.
Key words: Behavioral Framework, modeling freight forwarders behavior, commitment to
reliability, arrival time-of day, seaport container terminal.
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1. INTRODUCTION
Today, containerization has led to a great reduction in the cost of international trade by
increasing the speed of cargo turnover and by improving port production (1). In addition, the
container shipping industry is currently facing a period of remarkable structural change in
terms of vessel sizes and volumes, challenging not only container terminal operations but the
entire logistics chain. Sea and inland container terminals as well as hinterland connections
(roads, rail etc.) are confronted with increasing volumes of containers that need to be
transported efficiently, quickly and safely.
The introduction of new, large container vessels such as the Maersk Majestic with a
capacity of 14000 twenty-foot equivalent units (TEUs) and the new Triple-E class vessels
with a capacity of 18000 TEUs are part of the abovementioned problem. In the design of
these vessels, the driving force is the achievement of a competitive advantage in terms of
economies of scale (2). Consequently, the size of the new vessels raises terminal, intermodal
and commercial issues. The challenges imposed on terminal operators can be summarized as
follows (3):
 the ability to berth these large vessels;
 the capacity to load and unload such vessels within appropriate time-windows;
 the capabilities to deliver containers to consignees on time;
 the existence of effective linkages;
 technical difficulties such us sufficient and adequate equipment, stack height
limitations, etc.
Since large ships influence terminal operations, it seems likely that the lack of efficient
hinterland connections could become a bottleneck, hindering the move towards door-to-door
transportation systems.
In addition, transport terminals are considered the main regulators of freight flows,
and influence the setting and operation of supply chains in terms of location, capacity and
reliability. The idea of the terminalization of supply chains, meaning that terminals function
as buffers and absorb delays and inefficiencies that are created in other parts of the supply
chain (4). Therefore, many shippers tend to opt for cheap storage of their consignments in the
terminal areas instead of using their own or hired warehouse premises. The increasing
container volumes, in combination with the lack of land availability, have led to the
emergence of terminal operations as key players in the logistics chain, through the imposition
of monetary and operational restrictions such as truck arrival windows, DT charges etc. (4).
It is essential for operators to increase the productivity and efficiency of their
operations so that they can cope with the increasing demand and lack of available space, and
to provide themselves with a competitive advantage. Hence, planners need to be assisted at
every stage of operation, with tools that support the decision-making process. An accurate
workload forecast will allow port operators to provide just enough resources, be they
manpower, equipment or container movers etc. The avoidance of over-providing or underproviding will have cost and efficiency implications. Excess resources may result in
unproductive operations and consequently higher operating costs. Insufficient resources can
cause long queues to build up, and congestion inside or even outside the terminal, leading to
unhappy customers.
Workload forecasting related to truck arrivals is of essential importance for storage
planning, daily and hourly equipment allocation, and human resources management.
Goodchild and Noronha indicate that even small improvements in the amount of information
available on truck arrival times and sequences at a port for container pick-up may reduce
unproductive moves (UPM) of containers (5). The rehandling of containers for any purpose
besides inspections and customs is strongly related to the inefficiency of a container terminal.
In order to increase their efficiency, some ports have tried to implement Truck Appointment
Systems (TAS) but, in general, information on the times at which trucks will arrive to pick up
containers remains unreliable and scarce. Yard planners make decisions on stacking and
rehandling policies mainly based on their experience. However, if the exact time-pf-day a
container was to be discharged from the terminal was known in advance, operators would be
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able to organize the yard appropriately so as to be able to retrieve containers with higher pickup probabilities more easily. For the purpose of this research on import containers will be
examined.
The overview (not an exclusive one) conducted in Section 2 of this paper reveals that,
although information on truck arrivals is crucial for shortening container process times,
improving cost structures and for the overall integration of container terminals into supply
chains, the policies and strategies implemented by container terminals have not always been
successful so far. In Section 3, and with an overview of the literature as a basis, gaps at the
freight modeling framework are investigated. Section 4, presents pilot case study conducted in
Greece aiming to understand how Freight Forwarders (FF) decide on time-of-day arrivals in
container terminals. Section 5 presents, a behavioral framework that incorporates the effect
that commitment to reliability has at FFs choice of pick-up time-of-day and the proposed
modeling methodology. Conclusions are drawn in Section 6.
2. PREDICTION OF GATE-OUT WORKLOAD
With drayage trucks being the predominant transportation mode for moving
containers in and out of terminals, terminal gates are facing overwhelming congestion,
causing extraordinary delays with major impacts extending not only to the surrounding road
networks but to the entire logistics chain (7). Trying to achieve better demand management,
terminal operators are implementing innovative strategies such as TAS and congestion pricing
(8).
The implementation of a TAS is regarded in many studies ((9), (10), (11), and (12))
as a sufficient solution to reduce truck waiting times and improve terminal productivity. In (9)
a simulation-based approach was applied to assess the effect of an appointment system in
truck turn time and crane utilization. The results proved that regulating trucks can be effective
when a specific number of trucks are allowed to enter the terminal. The effect of a TAS
implementation on truck arrival times and crane utilization was investigated in (10) by
applying a heuristic algorithm. In addition, different scheduling rules for trucks were
investigated using simulation (11). Simulation techniques were also applied to check the
efficiency of different TAS strategies (12). The results showed the benefits of assigning
individual appointments to truckers on the movements of trucks in and out of the terminal and
on the elimination of unnecessary truck queues.
In their research, Zhao and Goodchild developed a simulation tool to evaluate the use
of truck arrival information to reduce the UPMs of containers, that is rehandles, during the
import container retrieval process (13). Two approaches were considered for the purpose of
reducing rehandling work. The first was to try to eliminate the possibility of future rehandles
by carefully determining the storage location of rehandled containers. In the second they tried
to evaluate how information availability influences the number of rehandles. Three scenarios
of information availability were taken into consideration: (1) the complete sequence of trucks
is known; (2) only group information is provided, meaning that the group in which a truck
will arrive is known, but the exact order of truck arrivals within any group is not available; (3)
partial sequence is known: the arrival groups are known, and the arrival sequence within the
first group is known. The results proved that even truck arrival information results in
significant reductions in the number of rehandles. In fact, the authors proposed using existing
gate appointment systems, which can provide some information about truck arrival time
windows, or phone calls from approaching trucks, to provide the necessary information.
Utilizing such information would not incur much effort or cost; however, it would require
cooperation and collaboration between the terminal and trucking operations.
Apart from implementing TAS, other strategies have been proposed to improve
predictability. The research in (7) was an attempt to evaluate different gate strategies, such as
TAS and extended weekend and weekday gate hours, implemented in various container
terminals. The paper presents simulated gate traffic operations under different scenarios such
as truck congestion, delays etc. The results indicate that, in order to handle the increasing
container traffic, terminals should provoke a shift in demand to off-peak hours by imposing
pricing mechanisms such as congestion pricing. Hence, it has been recommended that
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extended gate hours should be combined with other strategies such as congestion pricing and
appointment systems. Towards this direction was the research of Sgouridis et al. that
simulated the container terminal of Thessaloniki and investigated the effects of different truck
arrival patterns on the service level of the terminal. They proved that an even distribution of
trucks during the day would decrease the total truck turn-around time by 15% (14). In
addition, they proposed that a shift of truck arrivals to off-peak hours would improve truck
turn-around times by 40% and equipment productivity by 7%. The alternative measure to
provide real time information to truck drivers through gate web-cameras was investigated in
(15). Assuming that truckers made rational decision real time information acquisition could
minimize congestion in seaport container terminal gates.
Not all container terminals, however, have succeeded in imposing strategies such as
truck appointments and extended gate operations on their users. The first TAS was introduced
in the ports of Los Angeles and Long Beach in order to deal with congestion and air pollution
(16). The unsuccessful implementations of appointment systems at those ports can be
attributed to a failure to consult with the key port stakeholders. Other factors hindering these
initiatives have been that truckers have tended to believe that TAS only shift queues from the
gates to the terminal yard and they have been unable to keep appointments (when not
mandatory) made 24 hours in advance. The abovementioned container terminals tried to
implement extended-gate operations with a program called PierPASS. They provided
incentives to their users (carriers and cargo owners) to shift the transportation of their
consignments to off-peak hours and days. Nevertheless, no significant shift in traffic was
reported.
The summarized reflection is that the presented research supports the idea that of a
tool that can predict the arrival times of drayage trucks without relying on TAS and other
policies that restrict the clients while providing disputed results. In addition, the majority of
the studies conducted so far use simulation methods to predict the time-of-day a truck arrives
at the terminal. One of the innovations of this study is therefore, the utilization of choice
models to estimate pick-up time of day.
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3. UNDERSTANDING FREIGHT FORWARDERS’ DECISIONAL FRAMEWORK
Based on the analysis of container terminal gate operation policies, we conclude that there is
grounds for expecting that the pick-up time-of-day of containers can be predicted by applying
behavioral econometric models. Previous studies have focused on the preferences of logistic
managers in terms of mode choice, port choice, and carrier choice. The most important
attributes used in the freight literature are: (1) cost, (2) frequency, (3) flexibility, (4) safety,
(5) the risk of delay, and (6) intermodality (17). Another important point that should be
explicitly accounted for is the interaction of different agents in the decision-making process.
Preferences and decisions among different actors may vary depending on the influence they
exert on the context in which they operate. (17) and (18) point out the importance of
information flow across the value chain. Since up-to-date information is not necessarily
always available, it is crucial for terminal managers to be able to comprehend and predict the
behavior of the various key players in the logistics chain. In addition, the relationships
between the key decision makers of the supply chain have been examined in prior work (19).
Specifically, competition between ocean carriers determines the routes and service charges.
Shippers search for the optimal combination of transportation services that minimizes cost
and distance. They also choose a sequence of carriers, including FFs and 3PL companies,
based on the carriers’ pricing and routing decisions.
(20) developed ordered probit models based on the findings of a questionnaire-based
survey of 65 manufacturers. The results showed that travel cost, travel time, punctuality and
damage losses were the most important service attributes taken into consideration by logistics
managers when selecting freight transportation modes. In 2006, they continued their research
by gathering stated and revealed preference data from 99 firms in Italy through telephone
interviews. The results of their survey reinforced the importance FFs give to the
abovementioned freight attributes. In the same direction, (21) quantified the effects of
attributes such as the value of time, reliability and frequency on modal shifts. They developed
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a policy tool based on mixed logit models by conducting SP experiments on a sample of 45
freight-forwarding companies in Spain. (22) developed a macro-scan methodology to assess
the impacts of different European transportation methodologies on modal shifts.
Tongzon investigated the main criteria influencing port selection. He conducted a survey
wherein FFs were asked to state their level of agreement regarding the selection of the port to
which they would send their shipments (23). The findings of the survey showed that port
efficiency is the most important factor, followed by shipping frequency, adequate
infrastructure, location and port charges. His findings are in agreement with a study (24)
conducted on Nigerian ports, which highlighted the importance of port efficiency as a
determinant of port choice.
The findings of the literature review show an absence of scientific research focusing
on the determinants of the time-of-day when FFs choose to discharge trucks to pick up
containers from terminals.
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1) The consignee: The receiver of the goods sets important requirements in terms of delivery
time. In most of the transport cases fulfilling his requirements has a major influence on the
organization of the entire chain.
2) The Freight Forwarder. The FF is assigned by the consignee (or by the consignor) to
organize the delivery process. He is responsible for the on-time delivery of the container to
the consignee. If the FF does not have his own rolling stock or the rolling stock is insufficient
the forwarder can choose to cooperate with a road haulier. He is also responsible to complete
all the necessary paperwork. The FF determines when the truck will arrive at the terminal.
3) The Truck Company. The road haulier has to adapt to the demands set by the FF and the
consignee. The trucking company must send an appropriate truck to the container terminal to
pick up the container on time and deliver it on time.
The findings of the literature review revealed total absence of behavioral model
applications in predicting truck time-of-day arrivals. For the purpose of gaining insight on the
decision making framework regarding time-of-day choice a pilot survey was conducted to
Greek FFs. The remaining the section describes the survey and presents the main findings.
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4.1 Survey description
Personal interviews were conducted involving 10 Greek freight forwarding companies during
October 2013 in Athens.
Each interview consisted of three sections:
1. Part 1. In the first part, general information on the main characteristics of the freightforwarding company was requested. This information is important because it will
permit the segmentation of the sample into groups based on their size (e.g. small,
medium and large companies) and/or the range of their operations (national, local or
international). Each group is expected to have its own decision-making process
regarding time of day of pick-ups and different predictive models can be developed
for each group.
2. Part 2. In this part, the FFs were asked to describe a typical pick-up from the terminal
(container dimensions, truck type, whether the container was hazardous or reefer,
commodity, time-window for delivery to the client, possible late delivery penalties).
All this information will be included as explanatory variables in the choice models.
3. Part 3. In addition, they were asked about the factors that influence the time-of-day
choice. They were also asked to state the level of their agreement with the different
port policies such as clients perceive the implementation of different policies such as
TAS and extended gate hours.
4. FRAMING AND UNDERSTANDING THE DECISION-MAKING PROCESS OF
GREEK FREIGHT FORWARDERS
This research tackles the issue of understanding the factors that determine the decisions of key
stakeholders regarding the time-of-day shipment receipt from container terminals. Three key
decision makers were identified and their relations (25) are presented below:
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4.2 Survey Results
Based on the results of the survey FFs were segmented in two groups based on the number of
their employees: 1) large companies with more than 30 employees and; 2) small companies
with less than 30 employees.
Greek FF characteristics are summarized in Table 1.
TABLE 1. Greek FF Characteristics
Large
Local, National, International
building materials, food and
beverages, and agricultural
products
Yes
monthly More than 300
container
Small
Local, National
building materials, food and
beverages, and agricultural
products
No
Less than 10
Yes
Frequent but not loyal clients
Yes
Yes
Yes
Loyal clients with occasional
shipments
Yes
Yes
Yes
Yes
Range of operations
Main commodities handled
Operate own fleet
Number
of
shipments
to
terminal of Piraeus
Customs clearance services
Client type
Storage yards
Reefer container
transportation
Hazardous container
transportation
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The size of FF companies does not influence the commodities handled, the type of
containers transported and the services they offer. Larger companies operate their own fleet;
make international deliveries and more shipments to the container terminal. Furthermore, the
clientele of small companies consists of loyal clients with occasional shipments compared to
large companies’ clients who make frequent shipments but are not loyal to the company.
Both small and large companies agreed that the delivery time-windows set by their
client are an important factor affecting their time-of-day choice. When the container is
delivered to the client it has to be emptied and returned to the shipping line within two hours.
If empties were not returned within two hours, extra charges would be imposed on the FF by
the shipping line. This two-hour time-window, however, could be modified depending on the
business relationship developed between the shipping line and the FF. All the respondents
agreed that when they schedule pick-ups they allow time for returning the empty container to
the shipping line. Customs was identified the most time-consuming bottleneck in the pick-up
process.
Larger companies reacted more positively towards combining import container pickups with export container delivery because it would be economically advantageous. However,
this is not a common practice due to the different factors involved with import and export
containers. In addition large FFs tended to give priority to the most urgent containers or the
ones that contained perishable goods. Priority is also given to reefer and hazardous containers.
The results of the interviews showed that the respondents would agree to pick up containers at
an appointed time if it meant that they received a discount or experienced smaller delays at
the terminal. Finally, large companies prioritize clients based on the quantity the ship
compared to small companies that give priority to loyal clients.
To summarize, the pilot study revealed that pick-up time is affected by: 1) distance,
2) commodity; 3) client type; and 4) container type. Time-windows imposed by the consignee
and customs were identified as the most important constraints in time-of-day choice. A
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difference between the way that large and small clients made decisions was observed. The
results of the survey will be incorporated in the modeling framework that follows.
5. METHODOLOGICAL FRAMEWORK
A starting point for the proposed methodology is the combination of a choice model with a
latent variable model. That is, the framework of a Hybrid Choice Model (HCM) that has been
developed to account for latent factors such as perceptions and attitudes ((26), (27)). Figure 1
describes the modeling framework.
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FIGURE 1. Methodological Framework
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In Figure 1 ellipses are used to represent non observable variables called latent
variables. Observable variables are denoted with rectangles. The exogenous characteristics
that influence FFs time-of-day arrival at the terminal decision can broadly categorized into
five groups. The first group consists of the container attributes, such as type, size, weight,
commodities, destination to where the container must be delivered. The second group
includes the characteristics of the FF, such as location of facilities, size, number of
employees, type of services it provides (local or international), fleet ownership, etc. The third
group includes characteristics related to information availability. FFs can acquire information
on the status of the container from the Port Community System or congestion information
from the Road Traffic Information System. The availability of a TAS will also influence their
decision making. Cost elements, such as shipment distance, predetermined discounts and
monetary penalties for late deliveries, belong in group four (28). Finally, the fifth group
consists of the transportation system characteristics e.g. the existence of toll roads, number of
lanes of the road leading to the container terminal, availability of alternative modes serving
the port, congestion, etc.
The delivery window the consignee set to the FF as well as possible constraints set by
customs (e.g. customs operation schedule, customs clearance) constraint time-of-day choice.
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FFs will be requested to state their level of agreement and disagreement with
statements regarding how they perceive their commitment to company’s reliability. These
statements will be used as indicators in the latent variable “commitment to reliability”. For
instance, not on-time delivery, damages or losses of containers increases FF liability for
delayed shipments. While the exact cost for late delivery cannot be calculated FFs perception
about the liability will influence time-of-day selection.
Stated Preference (SP) data will collected by asking FFs to choose the container pickup time they prefer among different hypothetical alternatives. Each alternative will have
different attributes, such as traffic conditions, container characteristics (weight, commodity,
and type), destination and type of client. The attributes of the alternative will be inserted as
explanatory variables in the models.
The HCM that will be developed is composed by two parts: a discrete choice model
and a latent variable model. The notation of the model is:
observed variables
X
*
latent (unobservable) variables
X
indicators of latent variable
I
Ui
utility of alternative i
vector of utilities
U
yi
choice indicator; equal to 1 if alternative i is chosen and 0 otherwise
y
vector of choice indicators
 ,  ,  unknown parameters
ε, υ, η
random distribution terms
𝛴𝜀 , 𝛴𝜐 , 𝛴𝜂 covariance of random disturbances
The HCM comprises of structural and measurement equations. A more detailed description
can be found in (29).
Structural equations
For the latent variable model, we need the distribution of the latent variables given the
observed variable, f 1 ( X * | X ;  ,   ) . For example:
X *  h( X ;  )  
*
For the choice model, we need the distribution of the utilities, f 2 (U | X , X ; ,   ) . For
example:
U  V ( X , X * ; )  
Note that the random utility is decomposed into systematic utility and a random disturbance,
and the systematic utility is a function of both observable and latent variables.
Measurement equations
For the latent variable model, we need the distribution of the indicators conditional on the
*
values of the latent variables, f 3 ( I | X , X ;  ,   ) . For example
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I  g( X , X * ; )  
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Each indicator will have one measurement equation.
For the choice model, we express the choice as a function of the utilities. For example,
assuming utility maximization:1
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1, if U i  max {U j }
j
yi  
0,
otherwise

The covariances of the error terms in the latent variable structural and measurement model are
constrained to be equal to zero (denoted by the Σ diagonal notation).
Likelihood Function
Maximum likelihood techniques can be used to estimate the unknown parameters. The
likelihood function for the integrated model presented above can be written as:
f 4 ( y, I | X ; ,  ,  , ) 
 P( y | X , X
*
;  ,  ) f3 ( I | X , X * ; ,  ) f1 ( X * | X ; ,  )dX *
X*
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Note that the first term of the integrand corresponds to the choice model; the second term
corresponds to the measurement equation from the latent variable model, and the third term to
the structural equation of the latent variable model. The latent variable is only known to its
distribution, and so the joint probability of y, I, and X * is integrated over X * .
6. CONCLUSIONS
Container terminals are regarded as the link in multi-modal container transport between the
sea and the hinterland transportation modes. According to (30), the efficient integration of
container terminals demands a synchronization of the process that connects the sea with the
landside transportation. The increase in vessel sizes, combined with the enduring growth of
containerization has compelled terminals to comply with specific customer and market needs.
The increasing differentiation of supply chains and related logistics network structures are
driving the relationships formed between the terminals and their clients. In response, terminal
operators are redesigning their strategies and policies to increase their competitiveness and to
add value for their clients, taking into account the operational limitations. It is therefore
crucial for terminal operators to become productive and efficient in order to cope with the
increasing demand and capacity constraints. High investments and high operating costs
necessitate that improvements be made to terminal operations. Optimization can be facilitated
by the design and development of decision-making tools for planners.
The scope for this proposed methodological framework is the creation of a planning tool that
will help terminal operators to predict hinterland workloads, achieve optimal staff and
equipment allocations, and design and implement effective stacking policies. The
unsuccessful implementation of extended gate hours and TAS in many terminals worldwide
has emphasized the importance of providing reliable information to yard planners without
imposing undesired measures on users. Up to now the problem of predicting trucks time-ofday arrivals has been tackled using simulation methods. One of the most important
innovations of this proposed the development of HCM that estimate time-of-day choice.
This research tackles the issue of understanding the factors that determine the decisions of key
stakeholders regarding the time-of-day at which a truck will be sent to pick up a container
from a terminal. The existing literature focuses only on factors influencing mode, port and
network choices. Therefore, an added benefit of this research is that it proposes a framework
that can assess FFs decision making process regarding pick-up time-of-day. The latter are
regarded as the main decision makers, from the landward side of the pick-up process, and it is
expected that their role in pick-up time of day is crucial.
A pilot survey conducted in Greece pointed in this direction revealing the main
factors affecting time-of-day choice. A segmentation of FFs was observed based on the size
number of employees. The results of the survey were incorporated in design of the proposed
framework which expected to enhance decision and policy making on three levels.
 Terminal operations level: Terminal operators may use this information in the
context of optimizing yard operations, human resources and equipment planning.
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Supply chain level: The results could help key supply chain stakeholders with mode
and port choice decisions as well as with designing the pricing policies they impose
on their clients.
 National level: Information on truck arrival rates may enhance national policy in
terms of road congestion pricing and decisions on road investments.
Regarding future research, a questionnaire-based survey focusing on the consignees will
be developed and personal or web-surveys will be carried out on FFs and consignees
operating in Greece and internationally. The collected data will be analyzed in order to
estimate time-of-day HCM models.
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