Simulation Modeling of Maritime Piracy using Discrete Event

and Agent-Based Approaches

A. Emre Varol

and Murat M. Gunal

Decision Support Dept., Turkish Naval Command, Ankara, Turkiye

Industrial Engineering Dept., Turkish Naval Academy, Istanbul, Turkiye

Keywords: Maritime Piracy, Discrete Event, Agent based, Hybrid, Simulation.

Abstract: In the last decade, maritime piracy has affected the global economy that forced the countries to react. Most

of the reaction is regarding force deployment in the affected regions. In this study, we present a simulation

based analysis tool that aims at understanding the relationship between naval forces deployment and

preventing piracy. We consider three stakeholders’ views; pirates, maritime transportation, and naval forces.

We initially created a classical Discrete Event Simulation (DES) model and adopted event scheduling

approach. However, later, we discovered that since the behaviours of entities, interaction between entities,

and the autonomy properties of entities are significant, Agent Based Simulation (ABS) concepts are

appropriate for modeling. Finally, we ended up with a hybrid DES and ABS model. Our conceptual model

is created using event graphs and the model is implemented using SharpSim DES library. Additionally, we

coupled an open source Geographic Information System (GIS), GreatMap, with SharpSim.

1 INTRODUCTION

Continuing impact of piracy and armed robbery at

sea remains a significant concern to the international

community, nations, non-governmental and

commercial organizations. Over 90 percent of the

world’s trade is carried via maritime transportation

(International Maritime Organization, 2011) and as a

consequence, anything that restrains or imperils the

free transit of vessels in the maritime domain can

have serious social, economic and security

implications. In fact, the major concern is economics

related since maritime piracy is estimated to be

costing to the international economy between $7 to

$12 billion per year (One Earth Future Foundation

2010). The cost incurred is related to ransoms,

insurance premiums, rerouting ships, protection

forces, security equipments, piracy deterrence

organizations, and effects to the regional economies.

Although maritime security has been a major

concern internationally, interestingly, Operational

Research/Management Science (OR/MS)

community had very little involvement at this

problem domain. Jakob et al. (2011a and 2011b) is

an exception since they applied Agent Based

Modelling and Simulation (ABMS) concepts into

maritime security and a simulation tool, AgentC, has

been developed. Although OR/MS toolbox includes

many other methods and tools that can be applied to

help create solutions, the distinction of this problem

domain is that maritime piracy is a matter of

“behaviour”. According to the IMB (International

Maritime Bureau), piracy is “An act of boarding or

attempting to board any ship with the apparent intent

to commit theft or any other crime and with the

apparent intent or capability to use force in the

furtherance of that act”. We believe that this

definition suggests that an OR/MS modeller must

focus on finding ways of modelling behaviours of

pirates, as well as the protection against piracy.

The main motivation of this study is to exploit

modelling and simulation to support decision

making for maritime security issues and the efforts

to protect maritime transportation. We evaluated that

an ABS model would depict the behavioural

complexities in this problem domain and, as a tool,

would also help decision makers understand the

relationship between parties involved. In the next

section, we review ABS briefly and then present our

methodology for this particular study. Rest of the

paper is dedicated to the details of our modelling

work. First, we present Maritime Security

Operations Library (MSOLib), secondly we give

some information about the simulation model we

438

Emre Varol A. and M. Gunal M..

Simulation Modeling of Maritime Piracy using Discrete Event and Agent-Based Approaches.

DOI: 10.5220/0004431004380445

In Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2013),

pages 438-445

ISBN: 978-989-8565-69-3

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

developed. Thirdly, geographical information

system part of our study is presented. We

additionally give experimental results to draw some

conclusions.

2 BACKGROUND

INFORMATION

2.1 Agent Based Simulation

Agent-based modelling and simulation (ABMS) is a

simulation approach that models the overall

behaviour of a system through use of autonomous

system components (called agents) that

communicate with each other. ABMS continues to

be a rising value of simulation domain for the reason

that the main advantage of ABMS is its potential to

exhibit the system combined behaviour while just

modelling individual agent behaviour.

Siebers et.al (2010) asserts that ABMS enables

people to model their real-world systems in ways

that either not possible or not readily adapted using

traditional modeling techniques. Macal and North

(2010) is a classical ABMS tutorial and they suggest

that an ABS model have three elements: agents,

environment, relation between agents and method of

interaction. An agent independently lives in an

environment, interacts with its environment as well

as with other agents according to its behaviors and

experiences in accordance with local information.

Bonabeau (2002) puts forward that agent-based

modeling has seen a number of applications,

especially in real world business. ABMS

applications are not only in business, but also they

are widely being applied to spanning human social,

physical, healthcare and biological systems. When

we update the review, we see that in the last decade

the number of papers increased, as well as the

domains applied, such as marketing (Siebers et al.,

2007), (Filatova et al., 2009), (North et al., 2010),

agriculture (Bert et al., 2010), air traffic control

(Conway, 2006), biology and health (Emonet et al.,

2005), (Davilia and An, 2010), (Kurahashi and An,

2010), (Tang et al., 2011), and military (Moffat et

al., 2006), (Hill et al., 2006).

2.2 The Framework of this Study

In our study, we coupled three separate systems to

create a tool for evaluating maritime security opera-

tions, as shown in Figure 1. The first part is a class

library, Maritime Security Operations Library

(MSOLib), to maintain the data structures and

provide a backbone for the study. The second part is

the simulation model. We used SharpSim (Ceylan

and Gunal, 2011) DES engine to create an ABS

model. To do this, we built Event Graphs for

modeling behaviors of every class of simulated

units, such as pirates, fishing boats, cargo ships,

naval and air units. Finally, to display information

and animate the entities (agents) on a map, we

created an interface by using GMap.Net (GreatMap,

2011) GIS package.

3 MARITIME SECURITY

OPERATIONS LIBRARY

Maritime Security Operations Library (MSOLib) is

a dynamic link library which maintains basic

maritime contact objects, motion types, detection

tools and navigation tools required to simulate a

maritime security operation. It is created to work

with a simulation library and to maintain objects and

data structures. MSOLib hosts;

• Contact,

• Motion,

• Area,

• Sensor,

• Navigation classes.

We implement MSOLib by using C# and it provides

full support for object-oriented programming. It

works harmoniously with SharpSim and GMap.Net.

Contact class in MSOLib inherits from SharpSim

Entity class. A contact instance is an agent in the

simulation and its behavior is determined by event

graphs as discussed in the next section. There are

three main Contact types: Surface Contact, Air

Contact and SubSurface Contact.

Motion class is the base class for all motion

related activities. There are four motion types and all

inherits from basic linear motion. We used Dead

Reckoning (DR), Latitude Sailing and Longitude

Sailing methods for the linear motion of any

maritime vessel at sea. DR is the process of

estimating present position by projecting course and

speed from a known past position (Bowditch 2002).

It is also used to predict a future position by

projecting course and speed from a known present

position. The DR position is only an approximate

position because it does not allow for the effect of

leeway, current, helmsman error or gyro error. We

neglected these effects and errors. Latitude and

longitude sailing methods also use DR sailing when

a maritime vessel sail on respectively on latitude and

longitude.

SimulationModelingofMaritimePiracyusingDiscreteEventandAgent-BasedApproaches

439

Figure 1: Main component of the analysis tool.

Traverse motion is the motion of the ship between

two positions. It moves back and forth between these

two positions at specified times. When the contact

hits the waypoint, it alters its route to opposite

toward to the other waypoint. In random motion, the

Contact creates random waypoints in a directed area

and when hits the waypoint, it alters its course

towards to the next waypoint until it reaches the end

point. Path motion works like Random Motion

however the waypoints are predetermined by the

user.

Area class is required to maintain the agents in

an area. It is essentially the environment in the ABS

model. Each operation region composed of several

number of areas. Each area has its own color,

identifier, name, list of points on an operation map

and at command and control headquarter.

Geometrically, area is a polygon that is declared and

specified by naval forces.

Sensor class indicates detector component of the

contact. Sensor behaves like a radar system and use

IsInsideTheRange method to sweep the environment

in sensor range to seek and detect the target. The

target’s information is provided to the seeker. For

example, if a pirate agent attacks a cargo ship, it

tries to make a decision according to their freeboard

and physical condition by using this sensor. In the

sensor class, we used the basic cookie-cutter sensor

concept suggested by Buss and Sanchez (2005). This

concept assumes that when an entity enters the range

of the sensor, a detection occurs.

Navigation classes include necessary utilities for

navigational calculations.

4 SharpSim SIMULATION

MODEL

SharpSim is a general purposed DES library written

in C# to implement models developed with Event

Scheduling (ES) approach (Ceylan and Gunal 2011).

SharpSim is an open-source code library

(http://sharpsim.codeplex.com) and was used in

other domains before (Ceylan, Gunal, and Bruzzone

2012).

Since in this study we aimed at building an ABS

model, we created Event Graphs (EG) to reflect the

behaviors of agents. Once EGs are built, it is easy to

convert them to a SharpSim model. Using EGs

approach reveals the thin line between DES and

ABS, or the power of EGs as a conceptual modeling

method.

Our conceptual model (Event Graph) has more

than 50 events and it is not possible to demonstrate

the whole, an excerpt is given in Figure 2. This EG

is a part of navy patrol ship model and is described

in the following section.

At the beginning of the simulation, a Run event

is scheduled to the future event list which triggers

the Ping, Convoy, Group, and all agent creator

events for aircraft, fishing boats, cargo ships, patrol

navy ships, and convoy (escort) navy ships. Ping

event is the basic animation event which essentially

acts as a screen refresher event. It updates all agents’

positions on the map. This approach was used by

Mack (2000).

4.1 Aircraft Model

An aircraft agent starts its motion and turns on its

sensor to detect and identify the contacts in its

environment. If the agent identifies a pirate vessel it

shares this information with naval forces. Aircrafts

use path motion in a patrol area. After patrolling,

each aircraft returns its home base. The “sweep and

detect (SWandDET)” and “Dispatcher” events are

circular events which simulates periodical detection

efforts. For example, if the patrolling aircraft detects

a pirate vessel, the dispatcher event schedules a new

SWandDET event, allow SWandDET event to loop

itself, and also schedules update pirate list event.

When aircraft finishes patrolling and return its home

base, it cancels next SWandDET event by using the

Maritime Security

Operations Library

(MSOLib)

Simulation

Model

(SharpSim)

Geographical

Information System

(GMap.Net©)

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Figure 2: An example of an Event Graph for Navy Patrol Ship.

cancelling edge in the event graph. This edge deletes

first SWandDET event from Future Event List

(FEL) to turn off its sensor.

4.2 Fishing/Pirate Ship Model

Number of fishing boats and their origin ports are

defined by the user. When a fishing ship arrival

occurs, next fishing ship arrival event is scheduled

after a delay (interarrival time of fishing ship). A

fishing boat may also be a pirate ship if certain

conditions are met. For example, if a fishing boat’s

freeboard is low and its speed is high, it is likely that

this boat is a pirate boat rather than a fishing boat.

Piracy events start with proceeding to cargo ship

transit corridor. When the pirate starts to proceed to

transit corridor it turns on its sensor (human vision

with binoculars) to detect any vulnerable cargo ship

to capture and any naval forces to conceal and

protect itself. After each SWandDET event, pirate

considers next step of the strategy at the dispatcher

event. If pirate ship detects any ship, it tries to

classify and identify the contact. After this process,

if pirate ship detects any vulnerable ship, it updates

its attackable ships list and makes a decision to

select a target based on the targets’ freeboard,

distance, and proximity to any naval forces. It

selects the most vulnerable cargo ship (target) by

taking into account of these three elements and

calculates a risk factor. According to the risk factor

it makes a decision to attack. For example, if there is

a naval helicopter in the close perimeter, pirate does

not decide to attack since helicopter movement agile

and nimble, it can easily neutralize the pirate vessel.

Pirate starts fishing after decision of “do not attack”.

On the other hand, it starts to attack the target until

pirate captures it for ransom or is neutralized by the

naval forces. Unfortunately, if it captures the target,

pirate achieved its own mission and successful pirate

attack statistics are updated. If it is neutralized by

the naval forces, failed pirate attack number and

successful naval protection number is incremented.

At the time of pirate attacking, a pirate checks its

own condition (neutralized or not) and decides to

continue the attack or changes its own condition to

neutralized and deletes itself from the simulation.

4.3 Cargo Ship Model

Cargo ships’ behavior is simple since a cargo ship

starts to proceed to the waiting area of the transit

corridor. Cargo ship motion on the transit corridor is

supervised by the convoy and group scheduled

events.

4.4 Navy Ship Model

Naval ships have two tasks; patrolling in a specified

area and escorting cargo ships for secure transit in

the corridor. We refer Naval Patrol Ship (NPS) in

charge of patrolling and Naval Convoy Ship (NCS)

for escorting. As in the other agents, NPS and NCS

have also sensors to sweep and detect (SWandDET)

the environments. After each SWandDET event,

NPS considers next action at the dispatcher event.

When SWandDET event occurs, NPS does not only

sweep and try to detect all over its environment in

distance of sensor range but also checks cargo ships’

alert state. If it detects any piracy act or any alert

state about piracy from cargo ship, it starts to

prevent piracy. First, if its helicopter is airborne, it

gives orders to the helicopter to quit surveillance and

approach to pirate ship. If its helicopter is not

airborne, it takes off immediately and proceeds to

SimulationModelingofMaritimePiracyusingDiscreteEventandAgent-BasedApproaches

441

the pirate ship. Meanwhile the NPS approaches to

the target. After the operation, if it fails to prevent

piracy, NPS ship reports the result to headquarter

and starts to chase the pirate ship. In this case the

cargo ship is taken to the captivity region by the

pirate. On the other hand, if the operation is

successful, NPS neutralizes pirate ship and resumes

its patrolling duty and makes a decision for

helicopter to land or continue to surveillance. When

an NCS arrival occurs, next arrival event is

scheduled after a delay (interarrival time of NCS).

After each arrival, arriving ship proceeds to waiting

area of transit corridor and NCS motion on transit

corridor is supervised by convoy scheduled event

which will be detailed later

4.5 Convoy and Group Model

There are two transit sailing policy at internationally

secured corridors; group transit and convoy transit.

The main difference between these two transits is

that convoy transit has a naval ship in the group to

escort ships, whereas a group transit does not. The

aim is to form ships into groups that sail through the

secured corridor together. For each group, there is a

schedule with entry time and a group speed to

bundle ships together. Participation to these groups

is neither an obligation nor compulsory. This

binding operation is crucial for establishing the

security and controlling the transit corridor.

Convoy and group events are time scheduled

events. A convoy event has a naval ship which

escorts cargo ships to protect them. If the escort

navy ship detects any pirate ship or discerns piracy

attack alert from cargo ships, it starts to prevent and

neutralize pirate ship as mentioned in Navy Ship

Model. After the operation, it continues to its transit

and escort duty. A group event, however, does not

have any naval ship. All cargo ships passes through

transit corridor in a bundle that enlarges sensor

range of group and causes to alert naval ships before

any attack has occurred.

5 GEOGRAPHIC INFORMATION

SYSTEM (GIS)

A Geographic Information System (GIS) is a

computer-based tool for mapping and analyzing

geographic phenomenon on Earth. A GIS is

particularly useful for representing input and output

of a geospatial environment. As discussed earlier, in

our modeling approach we also used a GIS system to

display simulating entities on maps. There are three

ways of coupling GIS with simulation (Westervelt,

2002): (1) Loose Coupling: A loose connection

usually involves the asynchronous operation of

functions within each sys-tem. (2) Moderate

Coupling: This category encapsulates techniques

between loose and tight/close coupling. (3) Tight or

Close Coupling: This type of linkage is depicted by

simultaneous operation of systems allowing direct

intersystem communication during simulation

program execution.

There are many GIS packages and tools available

and some are already used to couple with simulation.

For example OpenMap, an open source Java based

GIS package was coupled with Simkit (Mack, 2000).

GeoKIT is a commercial Java based GIS package

and also was used for a simulation study (Yildirim et

al., 2009). We sought to use a .Net compatible GIS

package, since our simulation package, SharpSim, is

also in .Net. Therefore, we choose GreatMap GIS

software (GreatMap, 2011) which is open-source

and can use many online map providers, including

Google, Yahoo, Bing, OpenStreetMap, ArcGIS,

OviMap and CloudMade. On the other hand, it also

supports offline use.

In Figure 3, the Graphical User Interface (GUI)

of the model is shown. It is the animation of one of

the scenarios created for experimentation. Colored

icons represent animated agents.

6 EXPERIMENTATION

In our experiments, we investigate the effects of

resource allocation and patrolling plans on the

success of preventing piracy to make the transit

corridor safer for cargo ships.

We made some assumptions that weather

conditions are stable and do not influence piracy

activity. We also had to neglect possible effects of

armed guards at cargo ships.

6.1 Design of Experiments

We altered the values of the inputs; NPS and NPH

patrol times, south and west side fishing boat rates,

inhibitor coefficients, helicopter ranges, and volume

of cargo ship traffic. Number of ships passed

through Suez Canal is assumed to be the inputs to

the Gulf of Aden, and on average, 17.000 ships pass

the canal annually. Number of pirate acts, however,

is difficult to adapt into the model. We assumed that

some proportion of fishing boats that exist in the

region, under some conditions, acts as pirate ships.

Examining the historical piracy acts in the Gulf, we

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observe no trends.

The performance values in the model are general

variables for measuring success of naval operations.

Model outputs are average successful piracy ratio,

average neutralized pirate ratio, average success

ratio of pirate ships, average neutralization ratio of

NPS, average neutralization ratio of NPH, average

neutralization ratio of NCH, utilization of NPS, and

utilization of NCS.

Table 1: Factors and Levels of Experimental Design.

Levels/

Factors

Level-1 Level-2 Level-3

Level-

MPRA

a patrol in

300 minutes

a patrol in

600

minutes

not

planned

for

patrolling

NPS

and

NPH

doubled size

NPS

w/helicopter

support

NPS with

helicopter

support

only NPS

ships

none

NPS

and

NPH

NCS

five NCS per

day

one NCS

per day

two or

three

NCS per

day

We produced 36 different scenarios (3x4x3=36)

(3:MPRA Levels; 4:NPS Levels; 3:NCS Levels) by

changing parameters of patrol policy and resource

allocation parameters in Table 1. There are normally

3 NPSs, and 5 convoy groups in 24 hours. In table 1,

NCS level 1 means that each convoy is escorted by 1

NCS, and in level 2, 1 NCS per day is scheduled.

Each scenario is run for 5 times. Outputs generated

by the model are average successful piracy ratio,

average neutralized pirate ratio, average success

ratio of pirate ships, utilization of NPS, and

utilization of NCS. The run length is for one

simulation year after a warm-up period of one

month.

6.2 Results

Since we aim to make some general inferences on

the relationship between naval resource levels and

success of operations, simulation results are given in

a relative manner and should not be interpreted in an

absolute manner. In all our experiments, the

confidence interval values are small and negligible;

therefore the mean values are enough for analysis.

Although comprehensive experimentation results

exist (Varol, 2012) we give a limited version in this

paper. For example, as the output variable, we have

only taken “Average Prevent Piracy” (APP) ratio

into account. Scenarios no. 13, 16, 22 and 23 have

generated the uppermost values which are

respectively; 0.9980, 0.9973, 0.9970, and 0.9966.

Additionally, scenarios no. 9, 8, 33, 7, 3, and 6 bring

forth the lowermost values which are 0.5695,

0.8220, 0.8493, 0.9075, 0.9225, and 0.9301,

respectively. Figures 4, 5, and 6 shows that the

uppermost results are gained in NPS factor at

“double sized” level at each level of MPRA and

NCS factors (plus signs). On the other hand, NPS

factor at “no ships” level caused the lowermost

results (diamonds). In addition, increasing the

number of NCS ships in the region causes to

increment in APP ratio. Furthermore, when NCS

factor hits the peak, results also reach topmost level.

Keeping both NCS and MPRA factors levels at

their own highest points, the simulation creates

nearly the same results (all shapes except diamonds

at Figure 6) at different levels of factor NPS except

“no ships” level (diamonds at Figure 6).

In addition, rectangles and triangles at Figure 4,

5, and 6 show us, either using or not using helicopter

to prevent piracy has a little effect on the APP ratio

and the difference is not significant. Therefore,

having helicopter resource on-board is not necessary

if NCS ships carry helicopters.

7 CONCLUSIONS

Maritime piracy has been an issue for over a decade

and caused some economical problems in global

scale. OR/MS methods and tools can help tackle

such issues and in our study we made an attempt to

this end. We created a simulation tool for decision

makers to understand the relations between pirate

behavior and naval force planning.

Our methodology was developed in three stages;

firstly we created a class library to create a back-

bone for the study. This includes contact, motion,

area, sensor, and navigation classes. Secondly, we

built an EG to model behaviours conceptually. Later,

based on the EGs we converted the conceptual

models to DES models. In this way, we treated a

DES model as an ABS model. Finally, to animate

agents on a map, we coupled the MSOLib, the

SharpSim model, and a GIS software.

Experiments presented in this paper are given for

demonstrative purposes. The interpretation for the

given experiments is that deploying a helicopter

either on NPSs or NCSs is fulfilling the tasks for

preventing piracy.

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443

Figure 3: Graphical User Interface of the Model.

B: NPS & NPH

300 mins 600 mins No patrol

A: MPRA

Ave_Prev_Pir_Ratio

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

Interaction

Figure 4: Interaction of Factors for APP Ratio (NCS at

level 1).

B: NPS & NPH

300 mins 600 mins No patrol

A: MPRA

Ave_Prev_Pir_Ratio

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

Interaction

Figure 5: Interaction of Factors for APP Ratio (NCS at

level 2 or 3).

B: NPS & NPH

300 mins 600 mins No patrol

A: MPRA

Ave_Prev_Pir_Ratio

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

Interaction

Figure 6: Interaction of Factors for APP Ratio (NCS at

level 5).

ACKNOWLEDGEMENTS

The views and conclusions contained herein are

those of the authors and should not be interpreted as

necessarily representing the official policies or

endorsements, either expressed or implied, of any

affiliated organization or government.

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