COMPARING DECISION SUPPORT TOOLS FOR CARGO SCREENING PROCESSES
Peer-Olaf Siebers(a), Galina Sherman(b), Uwe Aickelin(c) , David Menachof(d)
(a, c)

(a)

School of Computer Science, Nottingham University, Nottingham NG8 1BB, UK.
(b, d)
Business School, Hull University, Hull HU6 7RX, UK.

pos@cs.nott.ac.uk, (b)g.sherman@2008.hull.ac.uk, (c)uxa@cs.nott.ac.uk, (c)d.menachof@hull.ac.uk

ABSTRACT
When planning to change operations at ports there
are two key stake holders with very different interests
involved in the decision making processes. Port
operators are attentive to their standards, a smooth
service flow and economic viability while border
agencies are concerned about national security. The
time taken for security checks often interferes with the
compliance to service standards that port operators
would like to achieve.
Decision support tools as for example Cost-Benefit
Analysis or Multi Criteria Analysis are useful helpers to
better understand the impact of changes to a system.
They allow investigating future scenarios and helping to
find solutions that are acceptable for all parties involved
in port operations.
In this paper we evaluate two different modelling
methods, namely scenario analysis and discrete event
simulation. These are useful for driving the decision
support tools (i.e. they provide the inputs the decision
support tools require). Our aims are, on the one hand, to
guide the reader through the modelling processes and,
on the other hand, to demonstrate what kind of decision
support information one can obtain from the different
modelling methods presented.
Keywords: port operation, service standards, cargo
screening, scenario analysis, simulation, cost benefit
analysis, multi criteria analysis
1.

INTRODUCTION
Businesses are interested in the trade-off between
the cost of risk mitigation and the expected losses of
disruptions (Kleindorfer and Saad 2005). Airports and
seaports face an additional complexity when conducting
such risk analysis. In these cases there are two key stake
holders with different interests involved in the decision
processes concerning the port operation or expansion
(Bichou 2004).
On the one hand we have port operators which are
service providers and as such interested in a smooth
flow of port operations as they have to provide certain
service standards (e.g. service times) and on the other
hand we have the border agency which represent
national security interests that need to be considered.
Checks have to be conducted to detect threats such as

weapons, smuggling and sometimes even stowaways. If
the security checks take too long they can compromise
the service standard targets to be achieved by the port
operators. Besides these two conflicting interest there is
also the cost factor for security that needs to be kept in
mind. Security checks require expensive equipment and
well trained staff. However, the consequences for the
public of undetected threats passing the border can be
severe. It is therefore in the interest of all involved
parties to find the right balance between service,
security, and costs.
But how can we decide the level of security
required to guarantee a certain threshold of detection of
threats while still being economically viable and not
severely disrupting the process flow? A tool frequently
used by business and government officials to support
the decision making is Cost-Benefit Analysis (CBA)
(Hanley and Spash 1993). While CBA is useful in our
case to find the right balance between security and costs
it struggles to provide decision support for the
consideration of service quality. This is due to the fact
that service quality is difficult to be expressed in
monetary terms. Multi Criteria Analysis (MCA) is a
tool that allows taking a mixture of monetary and non
monetary inputs into account. It can use the results of a
CBA as monetary input and service quality estimators
as non monetary input and produce some tables and
graphs to show the relation between cost/benefits of
different options (DCLG 2009).
Different modelling methods can be used to
conduct CBA. Damodaran (2007) lists Scenario
Analysis (SA), Decision Trees (DT) and Monte Carlo
Simulation (MCS) as being the most common ones in
the field of Risk Analysis. Less frequently used in Risk
Analysis but often used in Operational Research to
investigate different operational practices is Discrete
Event Simulation (DES) (Turner and Williams 2005;
Wilson 2005). Depending on the world view the
modeller adopts DES models can either be process
oriented or object oriented (Burns and Morgeson 1988).
Besides being useful for estimating factors required for
CBA, DES models also allow to investigation how well
service standards are reached in the system under
investigation as it considers delays that one experiences
while moving through the system (Laughery et al 1998).
It is therefore well suited, in conjunction with CBA, to

provide all the inputs required for a MCA. However,
sometimes it is even possible to find a solution that does
not require any investment. It might be feasible to
achieve the goal simply by changing certain working
routines. In such cases DES might be able to provide
the information required for making a better informed
decision without the need to conduct a full MCA.
In previous work (Sherman et al 2010) we
compared the efficiency of different methods for
conducting CBA by using the same case study. This
strategy allowed us to contrast all modelling methods
with a focus on the methods themselves, avoiding any
distortions caused by differences in the chosen case
studies. In this paper we continue our investigation but
this time we focus on the two competing DES
approaches and what information they can provide to
assist our analysis.
The remainder of the paper is structured as follows.
In Section 2 we introduce our case study system, the
Port of Calais. In Section 3 we show in detail how to
conduct a CBA using SA for our case study system. In
Section 4 we discuss the additional features DES has to
offer and we explain how to implement a model of the
case study system using different world views. Once we
discussed the specific features of the different
implementations, we demonstrate by an experiment
how to use DES to provide decision support. In Section
5 we summarise the findings from Sherman et al (2010)
and this paper in form of a table that shows real world
phenomena that can be modelled with the different
modelling methods, the data requirements, and the
decision support information that is provided. Finally
we suggest some future research activities.
2.

CASE STUDY
Our case study involves the cargo screening
facilities of the ferry Port of Calais (France). In this area
of the port there are two security zones, one operated by
French authorities and one operated by the UK Border
Agency (UKBA). The diagram in Figure 1 shows the
process flow in this area.
According to the UKBA, between April 2007 and
April 2008 about 900,000 lorries passed the border and
approximately 0.4% of the lorries had additional human
freight (UKBA 2008). These clandestines as they are
called by the UKBA are people who are trying to enter
the UK illegally - i.e. without having proper papers and
French Border Control Offices and Detention Facilities

French
Passport Check

French
Screening
Facilities

Tickets

French
Deep Search
Facilities

Controlled by
Calais Chamber of Commerce (CCI)

documents.
The search for clandestines is organised in three
major steps, one by France and two by the UKBA. On
the French site all arriving lorries are screened, using
passive millimetre wave scanners for soft sided lorries
and heartbeat detectors for hard sided lorries. If lorries
are classified as suspicious after the screening further
investigations are undertaken. For soft sided lorries
there is a second test with CO2 probes and if the result
is positive the respective lorry is opened. For hard sided
lorries there is no second test and they are opened
immediately.
Although 100% of lorries are screened at the
French site, not all clandestines are found. This shows
that the sensor efficiency in the field is less than 100%.
Unfortunately it is not known for any of the sensors
how much less their efficiency is and field tests cannot
be undertaken as it would be unethical to deliberately
lock someone in the back of a lorry. Another problem
with estimating the sensor efficiency is that the sensor
data has to be interpreted by human operators, who
might misinterpret them. Again, no data exist about the
efficiency of operators.
On the British site only a certain percentage of
lorries (currently 33%) is searched at the British sheds.
Here a mixture of measures is used for the inspection,
e.g. CO2 probes, dogs, and opening lorries. Once the
lorries passed the British sheds they will park in the
berth to wait for the ferry. In the berth there are mobile
units operating that search as many of the parked lorries
as possible before the ferry arrives, using the same
mixture of measures than in the sheds. As shown in
Table 1 only about 50% of the clandestines detected
were found by the French, about 30% in the sheds and
20% by the mobile units in the berth. The overall
number of clandestines that are not found by the
authorities is of course unknown.
Table 1: Statistics from Calais
Statistic
Total number of lorries entering Calais harbour
Total number of positive lorries found
Total number of positive lorries found on French site
Total number of positive lorries found on UK site
… In UK Sheds
… In UK Berth

Value
900,000
3474
1,800
1,674
890
784

The key question is: What measures should we
British Border Control Offices and Detention Facilities

British
Passport Check

British
Search Facilities

Berth Parking
Space

British
Deep Search
Facilities

Controlled by
UK Border Agency (UKBA)

Figure 1: Border control operations at Calais

employ to reduce the overall number of clandestines
that make it through to the UK? One way to improve
detection rates could be to intensify the search
activities. As we can see, clandestines are found at all
stages of the cargo screening process and we can be
sure that not all clandestines will be found in the end.
However, when increasing search activities we also
have to consider the disruptions this might cause to
traffic flow. As mentioned in Section 1 we have
different stakeholders with different interests involved
in the decision making process. The two key objectives
on which to base the decision are as follows: minimise
costs (for the tax payer) and maximise service quality
(by minimising waiting times and disruptions)
3.

USING CBA & SA FOR DECISION SUPPORT
CBA seeks to value the expected impacts of an
option in monetary terms (DCLG 2009). It involves
comparing the Total Expected Costs (TEC) of each
option against the total expected benefits, to see
whether the benefits outweigh the costs, and by how
much (Nas 1996). The aim is to determine the
efficiency of the interventions relative to each other and
the status quo. In our case total expected costs comprise
the investments we have to make to increase the search
activities. This might include things like increasing staff
level, staff training, getting new sensors with better
technology or building new search facilities. The total
expected benefits will be the money that is saved for
each clandestine that does not make it into the UK.
Clandestines that made it to the UK are very likely to
work illegally and therefore causing some income tax
losses. Furthermore, they will not pay their
contributions to health insurance and pensions.
Therefore, the government will have to look after them
once they are ill or old.
Uncertainty in the CBA parameters is often
evaluated using a sensitivity analysis, which shows how
the results are affected by changes in the parameters. In
our case we have one parameter for which it is
impossible to collect any data: the number of positive
lorries that make it into the UK. A positive lorry is a
lorry that has at least one clandestine on board. Usually
clandestines attempt to cross the border in small groups.
Conducting a sensitivity analysis cannot solve this
problem but it can show the impact of changing the key
parameter on the decision variable and can
consequently provide some additional information to
aid the decision making process and improve
confidence in the decision made in the end.
SA is the process of analysing possible future
events by considering alternative possible outcomes
(Refsgaarda et al 2007). To define our scenarios we
consider the following two factors: Traffic Growth (TG)
and Positive Lorry Growth (PLG). For each of the
factors we define three scenarios. Table 2 shows the
factors and scenarios investigated and an estimate of the
likelihood for each of the scenarios to occur in the
future. A justification for the scenario definitions can be
found in the following two paragraphs.

Table 2: Two factors with three scenarios each and their
probability of occurrence
Factor 1
Scenario 1
Scenario 2
Scenario 3
Factor 2
Scenario 1
Scenario 2
Scenario 3

TG

p(TG)
0%
10%
20%

PLG

0.25
0.50
0.25
p(PLG)

-50%
0%
25%

0.33
0.33
0.33

Our TG scenarios are based on estimates by the
port authorities who are planning to build a new
terminal in Calais in 2020 to cope with all the additional
traffic expected. According to DHB (2008) between
2010 and 2020 the traffic in the Port of Dover is
expected to double. We assume that this is also
applicable to the Port of Calais and have therefore
chosen the 10% per annum growth scenario as the most
likely one, while the other two are equally likely.
Our PLG scenarios are reflecting possible political
changes. The number of people trying to enter the UK
illegally depends very much on the economical and
political conditions in their home countries. This is
difficult to predict. If the situation stabilises then we
expect no changes in the number of attempts to illegally
enter the UK. However, as our worst case scenario we
assume a 25% growth. Another factor that needs to be
considered is that trafficking attempts very much
depend on the tolerance of the French government to let
clandestines stay nearby the ferry port while waiting for
the opportunity to get on one of the lorries. However, it
is currently under discussion that the French authority
might close the camps where clandestines stay which
would reduce the number of potential illegal immigrants
drastically. Therefore we added a scenario where the
number of attempts is drastically reduced by 50%. As
there is currently no indication of which of these
scenarios is most likely to occur we have assigned them
the equal probabilities of occurrence. We will assume
that any changes in clandestine numbers will
proportionally affect successful and unsuccessful
clandestines.
The question that needs to be answered here is how
the UKBA should respond to these scenarios. We
assume that there are three possible responses: not
changing the search activities, increasing the search
activities by 10% or increasing the search activities by
20%. For the CBA Search Growth (SG) is our primary
decision variable.
The cost for increasing the search activities in
Calais is difficult to estimate, as there is a mixture of
fixed and variable cost and operations are often jointly
performed by French, British and private contractors.
However, if we concentrate on UKBA’s costs, we can
arrive at some reasonable estimates, if we assume that
any increase in searches would result in a percentage
increase in staff and infrastructure cost. Thus we
estimate that a 10% increase in search activity would
cost £5M and a 20% increase £10M.

Now we need to estimate the benefits we would
expect when increasing the search activities. First we
need to derive a figure for the number of Positive
Lorries Missed (PLM) and how much each of these
lorries cost the tax payer. A best guess of “successful”
clandestines is approximately 50 per month (600 per
year). With an average of four clandestines on each
positive lorry an estimated 150 positive lorries are
missed each year. It is estimated that each clandestine
reaching the UK costs the government approx. £20,000
per year. Moreover, UKBA estimates that the average
duration of a stay of a clandestine in the UK is five
years, so the total cost of each clandestine slipping
through the search in Calais is £100,000, resulting in
£400,000 per PLM.
It is probably a fair assumption that an increase in
searches will yield a decrease in the number of positive
lorries and an increase in traffic will yield an increase in
PLM. In absence of further information we assume
linear relationships between the two parameters. Table 3
shows the number of PLM) assuming there is no PLG.
Equation 1 has been used to produce the table.
PLM(TG,SG)=PLM*(1+TG)/(1+SG)

(1)

Table 3: PLM for (PLG=0)
PLG 0%
TG 0%
TG 10%
TG 20%

SG 0%
150.00
165.00
180.00

SG +10%
SG +20%
136.36
125.00
150.00
137.50
163.64
150.00

With this information we can now calculate the
Economic Cost (EC) for all the different scenarios (see
Table 4) using Equation 2:
EC(TG,SG,PLG)=PLM(TG,SG)*(1+PLG)

(2)

Table 4: EC for different SG options
SG 0%
TG 0%
TG 10%
TG 20%
SG 10%
TG 0%
TG 10%
TG 20%
SG 20%
TG 0%
TG 10%
TG 20%

PLG -50%
PLG 0%
PLG 25%
£30,000,000
£60,000,000
£75,000,000
£33,000,000
£66,000,000
£82,500,000
£36,000,000
£72,000,000
£90,000,000
PLG -50%
PLG 0%
PLG 25%
£27,272,727
£54,545,455
£68,181,818
£30,000,000
£60,000,000
£75,000,000
£32,727,273
£65,454,545
£81,818,182
PLG -50%
PLG 0%
PLG 25%
£25,000,000
£50,000,000
£62,500,000
£27,500,000
£55,000,000
£68,750,000
£30,000,000
£60,000,000
£75,000,000

To be able to calculate the benefit we need to know
the combined probabilities of each scenario’s likelihood
to occur (listed in Table 2). We get this by multiplying
the probabilities of the individual scenarios as shown in
Equation 3. The results of these calculations can be
found in Table 5.
p(TG,PLG)=p(TG)*p(PLG)

(3)

Table 5: Combined probabilities
TG 0%
TG 10%
TG 20%

PLG -50%
PLG 0%
PLG 25%
0.0833
0.0833
0.0833
0.1667
0.1667
0.1667
0.0833
0.0833
0.0833

Now we multiply the EC from Table 4 with the
probabilities from Table 5 to receive the TEC for each
SG option, using Equation 4. The results are shown in
Table 6. The final step is to calculate the Net Benefit
(NB) by using SG=0 as the base case. The NB can be
calculated using Equation 5 (where C = cost for SG).
TEC(SG)=∑(EC(SG,TG,PLG)*p(TG,PLG))

(4)

NB(SG)=TEC(SG=0)-TEC(SG)-C(SG)

(5)

Table 6: CBA for different SG options
Option
SG
TEC
C
NB

1

2

0%
£60,500,000
£0
£0

10%
£55,000,000
£5,000,000
£500,000

3
20%
£50,416,667
£10,000,000
£83,333

The results of the CBA suggest that there is a small
benefit in choosing option 2. However, we need to keep
in mind that the calculations are based on a lot of
assumptions. Therefore, a small difference in the NB
might just be random noise. In particular we have one
factor that we do not know anything about - PLM. In
order to learn more about the impact of this factor we
can conduct a sensitivity analysis. Running CBA for
several different PLMs reveals that for a higher PLM
option 3 would give us the most benefits but for a lower
PLM option 1 would be the best choice. The sensitivity
analysis shows how important it is to get a good
estimate of the PLM.
£8,000,000
£6,000,000
£4,000,000

100 PLM
150 PLM

£2,000,000
200 PLM
250 PLM

£0
SG=0

SG=10%

SG=20%

-£2,000,000
-£4,000,000

Figure 2: Sensitivity analysis results
If nothing else, the process of thinking through
scenarios is a very useful exercise. In our case SA
helped us to define the scenarios of interest. The
sensitivity analysis later helped us to understand the
importance of PLM when choosing an option. Overall
SA only allows investigating a small number of factors
but it seems to be a useful tool for structuring the
overall investigation and get a first estimate regarding
the benefits that one could gain from the different
option.

4.

USING DES FOR DECISION SUPPORT
The main benefit of DES models is that time and
space can be taken into account which allows us for the
first time to assess service quality (in terms of waiting
time) and consider real world boundaries (e.g. space
limitations for queues). As we said before our goal is to
find a balance between service quality and security.
CBA on its own or in conjunction with any of the
methods introduced before does not provide us with
information about the impact of changes on service
quality. Another benefit we get by using DES modelling
is that it simplifies adding more operational details
about the real system and better supports the utilisation
of real world data which both make the models
themselves and results more credible.
In the following two sub sections we describe two
different DES modelling approaches: Process Oriented
DES (PO DES) and Object Oriented DES (OO DES).
The differences between these methods will be
described later in the subsequent sections. Here we look
at commonalities. For both modelling approaches we
need additional data regarding arrival rates, cycle times
(time lorries spend in a shed for screening), space
availability between stations for queuing, and resources
for conducting the searches.
In order to be able to emulate the real arrival
process of lorries in Calais we created hourly arrival
rate distributions for every day of the week from a year
worth of hourly number of arrival records that we
received from UKBA. These distributions allow us to
represent the real arrival process, including quiet and
peak times. In cases where this level of detail is not
relevant we use an exponential distribution for
modelling the arrival process and the average arrival
time calculated from the data we collected as a
parameter for this distribution.
The cycle times are based on data that we collected
through observations and from interviews with security
staff. In order to represent the variability that occurs in
the real system we use different triangular distribution
for each sensor types. Triangular distributions are
continuous distributions bounded on both sides. In
absence of a large sample of empirical data a triangular
distribution is commonly used as a first approximation
for the real distribution (XJTEK 2005). Every time a
lorry arrives at a shed a value is drawn from a
distribution (depending on the sensor that will be used
for screening) that determines the time the lorry will
spend in the shed.
We did not put any queue size limits into our case
study simulation model. However, we have a run time
display variable for each individual queue that displays
the maximum queue length of that queue. In this way
we can observe which queue is over its limit without
interrupting the overall simulation run. If necessary,
queue length restrictions could easily be added. In fact,
in one of our experiments we restrict the capacity of UK
shed queue and let lorries pass without searching them
if the queue in front of the shed is getting too long. This

strategy improves the service quality but has a negative
impact on security. Simulation allows us to see how big
the impact of this strategy is in the different scenarios.
We also added some more details to the UK berth
operation representation. We now consider the (hourly)
arrival of the ferry boat. When the ferry arrives all
search activities in the berth area are interrupted and all
lorries currently in the berth area are allowed to board
the ferry (as long as there is enough space on the ferry),
regardless if they have been checked or not. Again, this
strategy improves the service quality but has a negative
impact on security. This is an optional feature of the
DES model that can either be switched on or off.
Finally, in DES we can consider resources in a
more detailed way. While previously resources have
only been playing a role as a fixed cost factor (cost for
SG), we can now take into account how different
resource quantities influence the process flow and
subsequently the service quality. These quantities can
vary between different scenarios, but also between
different times of day (e.g. peak and quiet times).
Clearly, as we can see from the above, DES helps
to improve the decision making process. It allows
besides the monetary estimates to get information on
service quality and gain further insight into system
operations. This additional insight can be used for
decision making but also for optimising system
operations. DES also allows you to conduct a sensitivity
analysis to find high impact factors that need to be
estimated more carefully. Of course this does not come
without costs. DES models usually take much longer to
build and need additional empirical data.
4.1. DES using a process oriented world view
As before, we use the standard procedure for calculating
the TEC for the different SG options that enables us to
conduct a basic CBA. For this the PO DES delivers the
number of Positive Lorries Found (PLF) which allows
us to calculate the number of PLM under the
assumption that there is a linear relationship between
these two measures. This number can then be used as an
input for the CBA.
On a first view the PO DES model looks very
similar to the MCS model presented in Sherman et al
(2010) and in fact it is an extended version of this
model. In addition to the routing information defined in
the MCS model we have added arrival rates, cycle times
for screening the lorries and available resources. The
data required for implementing these additions into the
model has been provided by UKBA.
We now have a stochastic and dynamic simulation
model that allows us to observe the evolution of the
system over time. This is a big benefit compared to the
static models we used previously as it allows us for the
first time to consider our second objective in the
analysis - to provide high quality service. One of the
key factors for providing high quality service is to keep
average waiting times below a specified threshold
(service standard). By adding arrival rates, cycle times
and available resources the model is able to produce

realistic waiting time distributions, which gives us an
indication of the service quality we are achieving with
different parameter settings.
Another useful output that PO DES provides is
resource utilisation. This information allows us to finetune our CBA as we are able to better estimate SG
costs. So far we have assumed that SG has a fixed cost
which linear correlated with TG. In reality however, the
cost for SG might depend on the utilisation of the
existing resources. If in the current situation facility and
staff utilisation is low then SG can perhaps be
implemented without any additional costs. PO DES
allows to test at what level of TG additional resources
are required (throughput analysis).
The PO DES model also allows us to analyse queue
dynamics. A useful statistic to collect in this context is
the “maximum queue sizes" which enables us to find
bottlenecks in our system. With this information we can
then pinpoint where in the system we should add
additional resources to achieve maximum impact.
Removing bottlenecks improves the system flow and
consequently service quality.
Another interesting feature of our simulation model
is that it allows us to represent temporal limited
interventions and see what their effect is on system flow
and detection rates of positive lorries. These procedures
could be officers speeding up when queues are getting
longer (missing more positive lorries) or changing
search rates over time (less search at peak times) or
stopping search completely once a certain queue length
has been reached. PO DES can help us to find out,
which of these strategies is best.
However, the process oriented modelling approach
as described above has some limitations with regards to
flexibility. We are using proportions for defining the
vehicle routing (and detection rates), based on historic
data. This assumes that even if we have changes in the
system these proportions would always remain the
same. While this is acceptable for many situations (in
particular for smaller changes) there are occasions
where this assumption does not hold. For example, if
we change the proportion of vehicles searched in the
berth from 5% to 50% we cannot simply assume that
the detection rate grows proportionally. While it might
be easy to spot some positive lorries as they might have
some signs of tampering (so the detection rate for these
is very high and these where the ones reported in the
historic data), it will get more difficult once these have
been searched and a growth in search rate does not yield
an equivalent growth in detection rate any more. This
needs to be kept in mind when using a PO DES
approach.
Furthermore, the assumption of a linear relationship
between number of PLF and number of PLM which is
one of our key assumptions for the CBA is quite weak
in connection with PO DES as this relationship can be
influenced by some of the interventions. For example,
the temporal limited intervention of stopping search
completely once a certain queue length has been
reached influences the overall number of clandestines in

the system (when calculating this value by adding up
the number of PLF and number of PLM), although this
number should not be influenced by any strategic
interventions. This needs to be considered in the
analysis of the results..
4.2. DES using an object oriented world view
OO DES has a very different world view compare to PO
DES. Here we model the system by focusing on the
representation of the individual objects in the system
(e.g. lorries, clandestines, equipment, or staff) rather
than using predefined proportions for the routing of
entities based on historic data. In fact we only use
historic data as inputs (arrival rates, search rates,
staffing) and for validation purposes (numbers of PLF
at the different stages). The system definition is now
based on layout information and assumptions on sensor
capabilities. Taking this new perspective means that we
transfer all the “intelligence” from the process
definition into the object definition and therefore
change our modelling perspective from top down to
bottom up.
Unlike in DT, MCS, and PO DES we do not use a
probabilistic framework for directing lorries (and
deciding which of the lorries are positive). Instead we
aim to recreate processes as they appear in the real
system. At the entrance of the port we mark a number of
lorry entities as positive (to simulate clandestines
getting on board to lorries). This gives us complete
control over the number of positive lorries entering the
system. As the lorries go through the system they will
be checked at the different stages (French side, UK
sheds, UK berth). For these checks we use sensors that
have a specific probability of detecting true and false
positives. Only lorries that have been marked positive
earlier can be detected by the sensors as true positives.
The marked lorries that are not detected at the end (port
exit) are the ones that will make it through to the UK.
Officers can decide how to use the detectors (e.g. speed
up search time if queues are getting to long, change
search rates, etc.) depending on environmental
conditions.
One of the advantages of this simulation method is
that it is much easier to manipulate object and system
parameters, like for example the sensor detection rates
and search rates. When we change these parameters we
do not have to worry about linear dependencies any
more. In fact, we can find out relationships between
variables by looking at the evolution of the system over
time. However, the most interesting thing here is that
due to the fact that we model sensors as objects with
each having individual detection rates the number of
PLM becomes an output of the simulation. Yet, we have
to keep in mind that this output is still a function of
unknowns: PLM = f(lorries marked positive, sensor
detection rates ...). But overall, this is very useful
information as we can now do some what-if analysis
and see the trends of how changes in the system setup
impact on number of PLM. We do not rely on the
implicit assumption of a linear relationship between

PLM and SG any more. While thiss is not directly
solving our problem of estimating how many positive
lorries we miss it gives us some additional information
about system behaviour that might help us to decide in
one way or another.
4.3. Experimentation with the OO DES model
In this sub section
ion we want to show how we can use our
DES model to test alternative scenarios. We have
implemented our model in AnyLogicTM 6.6, a JavaTM
based multi-paradigm
paradigm simulation software. Figure 3
shows a screenshot of a section of the simulation model
(berth area) during execution.
To set up our OO DES we tried to reproduce the
base scenario (as defined in Table 1) as closely as
possible by calibrating our model to produce the correct
values for the number of PLF at the different stages of
the search process. We can do this by varying
varyin the
number of positive lorries entering the port, the sensor
detection rates, and the berth search rate. The results of
the calibration exercise are presented in Table 7
(Scenario 1). To get somewhere close to the real PLF
values at the different stages we had to increase the
number of positive lorries entering the port. Hence, also
the PLM value is much higher than the best guess we
used in our CBA. The detection rates for the UK sheds
and the UK berth had to be much higher than the ones
on the French side,
de, in order to match the true rates. We
assume that in the real system this is due to the fact that
UKBA uses some intelligence when choosing the
lorries to be screened. Therefore their success rate is

much higher. In particular in the berth officers can drive
around and pick suspicious lorries without any time
pressure.
Our scenarios are defined by TG and SG. All other
parameters (grey) depend on these values (with the
exception of the queue size restriction). All
dependencies are explained in Section 3. For this
experiment we assume that there is no change in the
number of people trying to get into the UK (PLG=0). In
Table 7 we only show the changes in the scenario setup.
Empty fields mean that there is no change in the set-up
set
for that specific parameter.
For this experiment we have defined a service
standard that needs to be achieved. We have a threshold
time in system
ystem that should not be exceeded by more
than 5% of the lorries that go through the system. We
have one intervention that allows us to influence
influe
the
process flow. We can define a threshold for the
maximum queue size in front of the UK sheds (queue
size restriction). If this threshold is exceeded lorries are
let pass without screening. While this intervention
improves the flow there is a risk that
th more positive
lorries are missed as less lorries are inspected.
The first three scenarios deal with TG. There is no
problem in regards to compliance with service
standards. Resource utilisation does not change as the
number of searches does not change. However, the
number of PLF is decreasing while the number of PLM
is going up. The next two scenarios deal with SG. The
increase in search activities causes some delays and at a

Figure 3: OO DES running in AnyLogicTM (screenshot of berth area only)

Table 7: OO DES simulation experiment set-ups and results (10 replications)
Scenarios
Traffic Growth (TG)

1

Queue size restriction

990000

1080000

900000

0.00550

0.00500

0.00458

0.00550

UK Sheds

0.330

0.300

0.275

0.363

0.396

UK Berth

0.600

0.545

0.500

0.660

0.720

2

3

4

5

0%
Arrivals

900000

0.41

UK Sheds

0.80

UK Berth

0.95

UK Sheds

10

9
7

0.858

1.019

1.268

0.863

0.859

0.860

0.863

UK Sheds

2.612

2.474

2.321

3.452

5.046

3.940

3.763

Service problem

1.831

1.783

1.856

2.439

3.620

2.901

2.788

18.099

18.085

18.155

18.517

19.274

18.893

18.834
0.049

0.019

0.019

0.020

0.036

0.068

0.052

UK Sheds

0.676

0.676

0.677

0.744

0.812

0.803

0.801

UK Berth

0.808

0.808

0.809

0.868

0.915

0.914

0.914

1774.9

1765.5

1745.9

1780.5

1774.3

1757.5

1769.7

900.8

814.0

733.8

981.2

1078.0

1061.2

1042.8

France
UK Sheds
UK Berth
Missed

699.9

658.4

630.7

715.9

743.0

746.5

746.8

1590.1

1697.2

1797.0

1480.7

1365.7

1361.7

1358.1

SG of 20% the system does not comply with service
standards any more. On the other hand, as expected, an
increase in search activities improves the number of
PLF and reduces the number of PLM. Scenario 5
indicates that service standards cannot be achieved with
the current staff/facilities and that an investment has to
be made in order to reduce the number of PLM and
comply with service standards. However, the last
scenario shows that there is also a strategic solution
possible that does not require any investment. By
managing the queues in front of the UK sheds it is
possible to reduce the number of service problems to
fewer than 5% (compliant with service standards) while
still keeping the number of PLM at a very low level.
Therefore, when applying this intervention no
investment is required.
As we have found a solution for our problem that
does not require balancing costs and benefits there is no
need to conduct a MCA. However, for other scenarios
where we are not so lucky we might want to consider
using MCA. A good guide to MCA with a worked
example is provided by DCLG (2009).
5.

6

France
Overall

Positive lorries

7

20%

off
1

Time in system (avg)
Resource utilisation

10%

6

0.44

France

Results
Waiting times (avg)*1)

5
0%

Positive

Detection Rates

4

20%

Soft-sided
Search rate

3

10%

Search Growth (SG)
Lorries

2
0%

CONCLUSIONS
To help the managers and analysts to decide which
method best to use for supporting their decision
processes concerning port operation and expansion it is
important to be clear about the real world phenomena
that can be considered with the different methods and
the decision support information that can be obtained by
applying them. Table 8 list these for all the methods
discussed in Sherman et al (2010) and in this paper.
The methods can be divided in two categories:
static and dynamic. For static methods the passage of
time is not important (Rubinstein and Kroese 2008).

SA, DT, and MCS belong to this category. The lack of a
concept of time in these methods makes is impossible to
analyse service quality of a system as all performance
measures for such analysis rely on the passage of time.
On the contrary, dynamic methods consider the passage
of time. DES belongs to this category and allows
evaluate a system’s compliance with service standards.
Besides, it provides other useful information about the
dynamics of the system that can be used for optimising
processes and improving flows.
DES can be implemented in different ways, either
as PO DES or OO DES. In our experience PO DES
seems to be easier to implement but less flexible (easier
to manipulate). OO DES seem to be more flexible but
also more difficult to implement. However, it has some
advantages. In our case OO DES was the only tool that
also provided an estimate of the number of PLM, which
helps us to better evaluate the effect of different
interventions.
For evaluating the trade-off between security and
cost CBA is a valuable tool. If we want to balance
security, cost and service then MCA is a better choice.
While CBA relies on monetary inputs, MCA allows
using monetary and non-monetary inputs. This is useful
as service quality is difficult to capture by monetary
measures. However, sometimes none of these tools is
required for the analysis as the answer might come
directly from the model as we have shown in Section
4.3.
A natural extension of the OO DES modelling
approach would be to add “intelligent” objects that have
a memory, can adapt, learn, move around, and respond
to specific situations. These could be used to model
officers that dynamically adapt their search strategies
based on their experiences, but also clandestines

Table 8: Comparison of modelling methods regarding real world phenomena that can be considered in the model (black)
and decision support information that can be obtained from the model (red)
SA
Scenarios (factors and
the decision variable)
Linear relationships
TEC
PLM, PLF

DT
Scenarios (factors and
the decision variable)
Linear relationships
TEC
PLM, PLF
System structure
Existing resources

MCS
Scenarios (factors and
the decision variable)
Linear relationships
TEC
PLM, PLF
System layout
Existing resources
System variability

learning from failed attempts and improving their
strategies when trying again. We are currently working
on implementing such “intelligent” objects.
REFERENCES
Bichou, K., 2004. The ISPS code and the cost of port
compliance: An initial logistics and supply chain
framework for port security assessment and
management. Maritime Economics and Logistics,
6(4), 322-348.
Burns, J.R. and Morgeson, J.D., 1988. An objectoriented world-view for intelligent, discrete, nextevent simulation. Management Science, 34(12),
1425-1440.
Damodaran, A., 2007. Strategic Risk Taking: A
Framework for Risk Management. New Jersey:
Wharton School Publishing.
Department for Communities and Local Government,
2009. Multi-Criteria Analysis: A Manual. London:
DCLG. Available from: http://eprints.lse.ac.uk/
12761/1/Multi-criteria_Analysis.pdf [accessed 19
July 2011].
Dover Harbour Board, 2008. Planning for the next
generation - Third round consultation document.
Dover: DHB. Available from http://www.dover
port.co.uk/_assets/client/images/collateral/dover_c
onsultation_web.pdf [accessed 20 July 2011]
Hanley, N. and Spash, C.L., 1993. Cost-Benefit
Analysis and the Environment. Hampshire:
Edward Elgar Publishing Ltd.
Kleindorfer, P.R. and Saad, G.H., 2005. Managing
disruption risks in supply chains. Production and
Operations Management, 14 (1), 53-98.
Laughery, R., Plott, B. and Scott-Nash, S., 1998.
Simulation of Service Systems. In: J Banks, ed.
Handbook of Simulation. Hoboken, NJ: John
Wiley & Sons.

PO DES
OO DES
Scenarios (factors and
Scenarios (factors and
the decision variable)
the decision variable)
Linear relationships
TEC
TEC
PLM, PLF
PLM, PLF
System layout
System layout
Existing resources
Existing resources
System variability
System variability
Service time distributions Servic time distributions
Resource utilisation
Resource utilisation
Dynamic system
Dynamic system
constraints
constraints
(e.g. peak times)
(e.g. peak times)
System throughput
System throughput
Waiting times
Waiting times
(service quality)
(service quality)
Time in system
Time in system
Bottleneck analysis
Bottleneck analysis
Dynamic decisions by
Dynamic decisions by
system
objects
Sensor detection rates
Number of pos. lorries
entering the system

Nas, T.F., 1996. Cost-Benefit Analysis: Theory and
Application. Thousand Oaks,CA: Sage Publishing.
Refsgaarda, J.C., Sluijsb, J.P., Højberga, A.L. and
Vanrolleghem, P.A., 2007. Uncertainty in the
environmental modelling process - A framework
and guidance. Environmental Modelling &
Software, 22(11), 1543-1556.
Rubinstein R.Y. and Kroese, D.P., 2008. Simulation
and the Monte Carlo Method (2nd Edition). New
York: John Wiley & Sons.
Sherman, G., Siebers, P.O., Aickelin, U. and Menachof,
D., 2010. Scenario Analysis, Decision Trees and
Simulation for Cost Benefit Analysis of the Cargo
Screening
Process.
Proceedings
of
the
International Workshop of Applied Modelling and
Simulation (WAMS). May 5-7, Buizos, Brasil.
Turner, K. and Williams, G., 2005. Modelling
complexity in the automotive industry supply
chain. Journal of Manufacturing Technology
Management, 16(4), 447-458.
UK Border Agency, 2008. Freight Search Figures
provided by the UK Border Agency [unpublished]
Wilson, D.L., 2005. Use of modeling and simulation to
support airport security. IEEE Aerospace and
Electronic Systems Magazine, 208, 3-8.
XJTEK, 2005. AnyLogic User's Guide. St. Petersburg,
Russian Federation: XJ Technologies.

