Comparing Discrete Event and Agent Based Simulation in
Modelling Human Behaviour at Airport Check-in Counter
Mazlina Abdul Majida, Uwe Aickelinb, Peer-Olaf Sibersb,
mazlina@ump.edu.mya, uxa@cs.nott,ac,uk, pos@cs.nott.ac.uk
a
Dr, Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, Kuantan, 26300, Malaysia
Elsevier use only: Received date here; revised date here; accepted date here
Abstract
Simulation is a well-established what-if scenario analysis tool in Operational Research (OR). Discrete Event Simulation
(DES) and System Dynamics Simulation (SDS) are the predominant simulation techniques in OR. However, a new simulation
technique, namely Agent-Based Simulation (ABS) is gaining more attention in modelling human behaviour. In our research
we focus on modelling human behaviour using DES and combined DES/ABS. The contribution made by this paper is the
comparison of DES and combined DES/ABS for modelling human reactive and different level of detail of human proactive
behaviour in service systems e.g. check-in services in an airport. The results of our experiments show that the level of
proactiveness considered in the combined DES/ABS model has a big impact on the simulation outputs. Therefore, for service
systems of the type we investigated we would suggest to use combined DES/ABS as the preferred analysis tool.
© 2001 Elsevier Science. All rights reserved
Research Highlights
 We compare DES against combined DES/ABS in modelling human behaviour
 We model human reactive and proactive behaviours in an airport check-in counter
 Modelling human behaviours as actual as possible in DES/ABS model shows a significant impact on the outputs
 Significant to model human behaviour realistically on a queuing service system
Keywords : Simulation,;Discrete Event Simulation,;Agent Based Simulation; Reactive Behaviour;Proactive Behaviour
1. Introduction
Simulation has become a preferred tool in Operation
Research for modelling complex systems (Kelton et al.
2007). Simulation is considered a decision support tool
which has provided solutions to problems in industry since
the early 1960s (Shannon 1975). Studies in human
behaviour modelling and simulation have received
increased focus and attention from simulation research in
the UK (Robinson, 2004). Human behaviour modelling and
simulation refers to computer-based models that imitate
either the behaviour of a single human or the collective
actions of a team of humans (Pew and Mavor 1998).
Discrete Event Simulation (DES) and Agent Based
Simulation (ABS) are simulation approaches often used for
modelling human behaviour in Operational Research (OR).
Examples can be found in Brailsford et al. (2006) and
Siebers et al (2010). The capability of modelling human
behaviour in both simulation approaches is due to their
ability to model heterogeneous entities with individual
behaviour. Another simulation approach commonly used in
OR is System Dynamic (SD). However, this approach
focuses on modelling at an aggregate level and is therefore
not well suited to model a heterogeneous population at an
individual level. Because of this limitation, SDS is not
considered in the present study. Human behaviour can be
categorised into different types, many of which can be
found in the service sector. When talking about different
kinds of human behaviour we refer to reactive and
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proactive behaviour. Here, reactive behaviour is related to
staff responses to the customer when being requested and
available while proactive behaviour relates to a staff
member’s personal initiative to identify and solve an issue.
When providing services proactivity of staff plays an
important role in an organisation's ability to generate
income and revenue (Rank et al, 2007). But the question
here- is it useful to consider proactive behaviour in
models of service systems and which simulation
techniques is the best choice for modelling such
behaviour? Thus, in this paper we investigate the
impact of modelling different levels of proactive
behaviour in DES and combined DES/ABS. We
compare both simulation techniques in term of
simulation result using a real world case study:
check-in services in the airport.
Previously we have studied the capabilities of
DES and combined DES/ABS in representing the
impact of reactive staff behaviour (Majid et al, 2009)
and mixed reactive and proactive behaviour in a retail
sector (Majid et al, 2010) and public sector (Majid et
al, 2012). In this paper we look at the capabilities of
DES and combined DES/ABS in representing the
impact of mixed reactive and proactive behaviour on
another complex public sector system.
The paper is structured as follows: In Section 2
we explore the characteristics of DES and ABS and
discuss the existing literature on modelling human
behaviour in service sector. In Section 3 we describe
our case study and the simulation models
development and implementation. In Section 4 we
present our experimental setup, the results of our
experiments, and a discussion of these results.
Finally, in Section 5 we draw some conclusions and
summarise our current progress.
2
Literature Review
2.1 Human Behaviour Modelling in DES and
ABS
This chapter reviews existing research studies on
modelling human behaviour using DES and ABS
techniques. As explained by Pew and Mavor (1998),
Human Behaviour Representation (HBR), also
known as human behaviour modelling, refers to
computer-based models which imitate either the
behaviour of a single person or the collective actions
of a team of people. Nowadays, research into human
behaviour modelling is well documented globally and
discussed in a variety of application areas. Simulation
appears to be the preferred choice as a modelling and
simulating tool for investigating human behaviour
(ProModel 2010). This is because the diversity of
human behaviours is more accurately depicted by the
use of simulation (ProModel 2010).
Throughout the literature, the best-known
simulation techniques for modelling and simulating
human behaviour are DES and ABS. Among existing
studies on modelling human behaviour, the use of
DES is presented by Brailsford et al. (2006), Nehme
et al. (2008) and Baysan et al.(2009). On the other
hand, Schenk et al. (2007), Siebers et al. (2007) and
Korhonen et al. (2008a; 2008b) recommend ABS for
modelling human behaviour.
Brailsford et al. (2006) claim that, based on their
experiments of modelling the emergency evacuation
of a public building, it is possible to model human
movement patterns in DES. However, the complex
nature of DES structures where entities in the DES
model are not independent and self-directed makes
the DES model inappropriate for modelling largescale systems. This characteristic of entities in DES
is agreed by Baysan et al.(2009), who have used DES
in planning the pedestrian movements of the visitor
to the Istanbul Technical University Science Center.
However, due to the dependent entities in the DES
model, the pedestrian movement pattern in their
simulation model is restricted to pre-determined
routes. By contrast, Korhonen (2008a; 2008b) has
developed an agent-based fire evacuation model
which models people-flow in free movement
patterns. He states that the decision to use ABS is due
to the fact that agent-based models can provide a
realistic representation of the human body with the
help of autonomous agents. In addition to modelling
human behaviour using DES, Nehme et al (2008)
have investigated methods of estimating the impact
of imperfect situational awareness of military vehicle
operators. They claim that it is possible to use the
DES model to understand human behaviour by
matching the results from the DES model with
human subjects. Schenk et al. (2007) comment that
modelling consumer behaviour when grocery
shopping is easier using ABS because this model has
the ability to integrate communication among
individuals or consumers. Siebers et al. (2007) assert
that their research in applying ABS to simulate
management practices in a department store appears
to be the first research study of its kind. They argue
that ABS is more suitable than DES due to the
characteristics of the ABS model; specifically, it
allows to model proactive and autonomous entities
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that can behave similar to humans in a real world
system.
Instead of choosing only one simulation
technique to model human behaviour, some
researchers tend to combine DES and ABS in order
to model a system which cannot be modelled by
either method independently. Examples are Page et
al. (1999) who studied the operation of courier
services in logistics, Kadar et al. (2005) who
investigated manufacturing systems, Dubiel and
Tsimhoni (2005) who studied human travel systems
and Robinson (2010) who looked at the operation of
coffee shop services. They all agree that DES and
ABS modelling can complement each other in
achieving their objectives. A combination of ABS
and DES modelling is useful when human behaviour
has to be modelled for representing communication
and autonomous decision-making.
In conclusion, the research into human behaviour
using DES and ABS that has been carried out so far
suggests that DES and ABS are able to model human
behaviour but take different approaches (dependent
entities vs. independent agents). The studies outlined
above indicate that DES is suitable for capturing
simple human behaviour, but is problematic when
applied to more complex behaviours as the next event
to occur in DES has to be determined. In contrast,
ABS offers straightforward solutions to modelling
complex human behaviour, i.e. free movement
patterns or employee proactive behaviour, as agents
can initiate an event themselves. However, for ABS
the resource requirements (computational power) are
much higher and the modelling and implementation
of the model is more complex.
2.2 The Simulation Choice
For our comparison exercise we have chosen to
use DES and combined DES/ABS. As we are
interested to investigate a service oriented system in
the public sector, which involves queuing for the
different services we cannot use pure ABS for our
investigations, as in pure ABS models the system
itself is not explicitly modelled but emerges from the
interaction of the many individual entities that make
up the system. However, as ABS seems to be a good
concept for representing human behaviour we use a
combined DES/ABS approach where we model the
system in a process-oriented manner while we
models the actors inside the system (the people) as
agents.
3
3
Case Study Description
The operation at the check-in counters in an
airport has been chosen as third case study (first case
study-Majid, 2010 and second case study - Majid,
2012) because it demonstrates a diversity of contact
between counter staff and travellers, which is essential
to this study of human behaviour. Information on this
third case study is chosen from “Simulation with
Arena” by Kelton (2007).
Figure 1 illustrates the operation at the airport
check-in service, the numbering and red arrows
representing the sequence of operation. The operation
at the airport check-in service of this case study starts
from the point at which travellers enter the main
entrance door of the airport and progress to the one
from the five check-in counters of an airline company
(represented by arrow number 1 in Figure 1). The
operation at the five check-in counters is from 8.00
am to 12.00 am every day. If members of staff at the
related check-in counters are busy, the travellers have
to wait in the counter queue (represented by arrow
number 2 in Figure 1). If counter staff are available,
then travellers will move to the check-in counter
(represented by arrow number 3 in Figure 1). Once
their check-in is completed, the travellers are free to
go to their boarding gates (represented by arrow
number 3 in Figure 1).
To model the human reactive and proactive
behaviours, information on real human behaviours at
the airport is gathered through secondary data sources
such as books and academic papers. The reactive
behaviour that has been investigated relates to
counter staff reactions to travellers in processing their
check-in requests and their response to travellers
waiting in queues during busy periods. The proactive
behaviours have been modelled are the behaviours of
another member of staff (supervisor) who is
responsible for observing and controlling the checkin services. The first proactive behaviour of a
supervisor is a request to the counter staff to work
faster in order to reduce the number of travellers
waiting in queues. The decision to execute such
proactive behaviour is based on their working
experience. Identifying and removing any suspicious
travellers from queues is the supervisor’s second
proactive behaviour to be modelled, their decision
again based on observation and working experience.
Suspicious travellers include those with overweight
hand or cabin luggage, drunken travellers and
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unauthorised pregnant women. The proactive
behaviour of travellers is related to their search for
the shortest queue in order to be served more quickly.
The decision by travellers to execute such proactive
behaviour is generated from knowledge that they
gather through observing other queues while
checking-in.
After analysing the operation at the check-in
counter, the level of detail to be modelled in the DES
and combined DES/ABS models, also known
conceptual modelling, is then considered.
Both, the DES model and the combined DES/ABS
model are based on the same conceptual model (Figure
2) but the strategy of implementation in both cases is
very different. DES modelling uses a process-oriented
approach, i.e. the development begins by modelling the
basic process flow of the check-in services operations
as a queuing system. Then, the investigated human
behaviours, reactive and proactive are added to the
basic process flow (Figure 2). Two different
implementation approaches are used for developing the
combined DES/ABS model: the process-oriented
Travellers travel to
boarding gate
Travellers travel to
boarding gate
3
Counter 1
3
Boarding gates
Counter 2
Counter 3
Counter 4
Counter 5
Travellers being serve by counter staffs
2
2
2
2
Travellers in queues
2
Observing supervisor
1
1
1
1
1
Arriving travellers
Main Entrance
Figure 1: The illustration of the check-in services in an airport
4
Simulation Models
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Queuing system and Human Behaviours
in DES Model
Start
Reactive behaviour
Travellers arrive at
the airport’s main
entrance
Travel to the
check-in counters
Counter staffs busy?
Counter staff
processes checkin request by
travellers
No
Travellers travel to
the boarding gate
End
Yes
A2
Proactive
behaviour
Travellers queuing
Proactive
behaviour
Supervisor
managing the
check-in area
A1
Proactive
behaviour
A
B
Travellers’ proactive
behaviours
Supervisor’s proactive
behaviours
Jump queue while
queuing to the
shortest queue
Proactive
B2
Request counter staff that
has long queue to work
faster
Removing the suspicious
travellers from queueing
Figure 2 : The implementation of DES model
Individual Behaviour by ABS Model
Travellers
State Chart Diagram
counter staff
available
waiting to be served
being served
travel to check-in counter
being idle
idle
serving
travellers
Supervisor
State Chart Diagram
observing
Message passing
finish serving
travellers
busy
travellers enter
the airport’s main
entrance
Message passing
Counter Staff
State Chart Diagram
Message passing
travellers leave
to boarding gate
Message passing
Jump queue upon
arrival to the
shortest queue
Proactive
B1
Proactive
A2
Message passing
Proactive
A1
B2
send
message to
counter staff
return to
observe
busy
Figure 3: The implementation of combined DES/ABS model
Proactive
behaviour
B1
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approach is used for the DES modelling (Figure 2) and
the individual-centric approach is used to model the
agents. Figure 3 shows some state charts that represent
the different types of agents in the model (travellers,
counter staff and supervisor).
Table 2 : Counter staff service time
Service Time Parameters
Value
Counter staff service time Weibull (7.78,3.91)
Two simulation models have been developed
from the conceptual models and have been
implemented in the multi-paradigm simulation
software AnyLogic™ (XJTechnologies, 2013). Both
simulation models consist of one arrival process
(travellers), five single queues, and resources (five
counters staff). Travellers, counter staff and supervisor
are all passive objects in the DES model while in the
combined DES/ABS all of them are active objects.
Passive objects are entities that are affected by the
simulation‘s elements as they move through the
system, while active objects are entities acting
themselves by initiating actions (Siebers et al., 2010).
iii.
A discussion follows on how objects in DES model or
agents in combined DES/ABS model are set up:
i.
Travellers object/agent
The arrival rate of the simulation model is
gathered from “Simulation with Arena” by Kelton
(2007). In both DES and combined DES/ABS
models, the arrival process is modelled using an
exponential distribution with the arrival rate shown in
Table 1. The arrival rate is equivalent to an
exponentially distributed inter-arrival time with mean
= 1/rate. The travel time as stated in Table 1 is the
delay time for travellers moving from the airport
entrance to the check-in counters.
Table 1 : Travellers arrival rates
Arrival Type
Time
Rate
Travellers arrival 8.00 –
Approximately 30
time
24.00 am
people per hour
Travellers travel upon
Uniform (1,2)time
arriving
minimum 1 minute ,
maximum 2 minutes
ii.
Counter staff object/Agent
In both simulation models, five members of
counter staff have been modelled performing the task
of processing travellers’ check-in requests. Task
priority is allocated on a first in first out basis
according to the service time stated in Table 2 below:
Supervisor Agent (only in combined DES/ABS
model)
The supervisor agent is modelled in the
combined DES/ABS model while in DES model the
supervisor is imitated by a set of selection rules
(programming function). This is because in the DES
model the communication between the entities is not
capable of being modelled. In both simulation models,
the supervisor is not directly involved with the checkin process. He/she is there only to observe the situation
at the check-in counter, so no service time is defined
for the supervisor for both simulation models (DES
and combined DES/ABS).
We conducted 100 replications for each set of
parameters. Both simulation models use the same
model input parameter values. Therefore, if we see any
differences in the model outputs they will be due to the
impact of the differences between the modelling
techniques. The run length for this case study is 16
hours, imitating the normal operation of the check-in
counter at an airport. The verification and validation
process are performed simultaneously with the
development of the basic simulation models (DES and
combined DES/ABS).
5
Experiment and Results
The purpose of the experiment is to compare the
simulation results between DES and combined
DES/ABS models when modelling human reactive
and proactive behaviours. The experiment sought to
find out what could be learnt from the simulation
results when modelling complex proactive
behaviours for the realistic representation of the real–
life system using two different simulation techniques.
The comparison of simulation results for DES and
combined DES/ABS models is conducted statistically
by performing a T-test. For our comparison we use
the following hypotheses:
Ho1 :
Modelling human reactive behaviour in
DES model produce statistically the same
simulation results with combined DES/ABS
model
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Ho2 :
Modelling human mixed reactive and
proactive behaviour in DES model produce
statistically the same simulation results with
combined DES/ABS model
Four simulation models have been developed: 1.
Reactive DES model 2. Reactive Combined DES/ABS
model 3. Mixed Reactive and Proactive DES model
and 4. Mixed Reactive and Proactive DES/ABS
models. Two types of experiments have been
conducted as shown in Table 3.
Table 3: Experiment Description
Experiment
Description
Experiment 1
Experiment 2
Reactive DES versus Reactive
Combined DES/ABS
Mixed Reactive and Proactive DES
versus Mixed Reactive and
Proactive DES/ABS
In this experiment, the general idea of reactive
(response to environment) and proactive behaviours
as described in section 4 were imitated and modelled.
The first proactive behaviour that was modelled in
this experiment is related to the behaviour of the
supervisor, who is responsible for ensuring that the
check-in process is under control. The supervisor’s
proactive behaviour is demonstrated by requesting
counter staff to work faster in order to serve travellers
who have been waiting a long time. The decision of
requesting counter staff to work faster is based on the
supervisor’s awareness that some travellers would
not move to another shorter queue. The second
proactive behaviour is demonstrated by the
behaviours of travellers who require faster service.
Finding the shortest queue on arrival at the check-in
services and moving from one queue to another
shorter queue while queuing have exemplifies the
proactive behaviours of travellers. Finally, the third
proactive behaviour is exhibited in identifying
suspicious travellers by the supervisor, based on their
own experience and observation at the check-in
counters
To investigate the impact of modelling
variation of proactive behaviours against reactive
behaviours in DES and combined DES/ABS models,
“customer waiting time” and “number of customers
served” were used as our main performance
measures. We have selected these measures as the
literature recommends them as important measures to
increase productivity in the service-oriented systems
(Robert and Peter, 2004). In addition, we have used
“counter staff utilisation” and “number of customers
not served” as the additional performance measures.
To execute the Experiment 2, the additional
performance measures were used based on the
investigated proactive behaviours (number of
requests to work faster, number of travellers
searching for shortest queue (upon arriving), number
of travellers searching for shortest queue (while
queuing) and number of travellers moved to the
office). It is assumed that investigating these
measures will provide sufficient evidence in
understanding the impact of the simulation outputs
for different behaviours in one simulation technique.
The sub-hypotheses were built for each
performance measure in DES and combined
DES/ABS according to the list of experiments to be
compared. Finally, the results of the performance
measures in the Experiment 1 and 2 were gathered
and compared for both simulation models (Table 4).
The hypotheses for Experiment 1 are:
Ho1_1 :
Ho1_2 :
Ho1_3 :
Ho1_4 :
The travellers waiting time resulting from the
reactive DES model is not significantly
different from the reactive combined DES/ABS
model.
The number of travellers served resulting from
the reactive DES model is not significantly
different from the reactive combined DES/ABS
model.
The counter staff utilisation resulting from the
reactive DES model is not significantly
different from the combined reactive DES/ABS
model.
The number of travellers not served resulting
from the reactive DES model is not
significantly different from the reactive
combined DES/ABS model.
The hypotheses for T-test in Experiment 2 are
the same with the four hypotheses in Experiment A1
but these hypotheses are tested with a name link to
Experiment 2 as follows:
Ho2_1, Ho2_2, Ho2_3, and
Ho2_4, for the travellers waiting time, the counter staff
utilisation, the number of travellers not served and
the number of travellers served, respectively. To
complete the Experiment 2, the following additional
sub-hypotheses are needed:
Ho2_5
:
The number of requests to work faster resulting
from the mixed reactive and proactive DES
model is not significantly different from the
mixed reactive and proactive combined
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Ho2_6
:
Ho2_7
:
Ho2_8
:
hypotheses are failed to be rejected. Results in this
case study have revealed a similar impact on both
simulation models when modelling similar reactive
behaviour. Hence, the simulation result for the reactive
DES and combined DES/ABS models is statistically
show no differences and the Ho1 hypothesis is failed to
be rejected.
Similarities and dissimilarities of results
between DES and combined DES/ABS were found in
this combined-proactive experiment, as shown in
Table 4. Significantly, the combined DES/ABS model
has produced a shorter waiting time, a lower number of
requests to work faster and a higher number of
travellers searching for the shortest queue while
queuing compared to the DES model. This impact is
significant, probably due to the extra individual
DES/ABS model.
The number of travellers searching for the
shortest queue (upon arrival) resulting from the
mixed reactive and proactive DES model is not
significantly different from the mixed reactive
and proactive combined DES/ABS model.
The number of travellers searching for the
shortest queue (while queuing) resulting from
the mixed reactive and proactive DES model is
not significantly different from the mixed
reactive and proactive combined DES/ABS
model.
The number of travellers moved to the office
resulting from the mixed reactive and proactive
DES model is not significantly different from
the mixed reactive and proactive combined
DES/ABS model.
Table 4 : Results of Experiment 1 and Experiment 2
Experiment 1
Experiment 2
DES
Combined
DES/ABS
DES
Combined
DES/ABS
18.46
24.84
65
20.59
473
22.81
4
4.71
-
6.44
10.18
69
18.21
462
24.19
0
0
1
1.25
3.12
8.17
70
18.7
459
25.1
0
0
0
0.19
Mean
18.64
23.91
64
18.58
473
22.35
4
2.25
-
SD
-
-
477
25.18
478
50.89
Number of travellers searching
for the shortest queue (while
queuing) (people)
Mean
-
-
n/a
223
-
-
Number of travellers moved to
the office (people)
Mean
-
-
SD
-
-
Performance
measures
Travellers waiting times
(minute)
Counter staff utilisation (%)
Number of travellers served
(people)
Number of travellers not served
(people)
Number of requests to work
faster
Number of travellers searching
for the shortest queue (upon
arriving) (people)
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
SD
Results for the Experiment 1 and 2 are shown
in Table 4 and the results of the T-test are shown in
Table 5 and 6. Referring to Table 4, Experiment 1 has
revealed similarities in results between the DES and
combined DES/ABS models. The test results in Table
5 show that the p-values for each performance measure
are higher than the chosen level of significant value
(0.05). Thus the Ho1_1, Ho1_2, Ho1_3, and Ho1_4,
n/a
42.18
15
17
10.15
11.83
behaviour that is modelled in the combined DES/ABS
model. Such behaviour (travellers searching for
shortest queue while queuing) is frequent in the system
under study and has affected the travellers’ wish to be
served more quickly; therefore no queue is longer than
another. The statistical test (Table 6) has confirmed
these three performance measures (customer waiting
time, number of requests to work faster and number of
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travellers searching for the shortest queue while
queuing) have shown lower p-values than the chosen
level of significant value (0.05). Therefore, the Ho 2_1,
Ho2_5 and Ho2_7 hypotheses are rejected.
In addition, the statistical test has confirmed
that there are no significant differences in both
simulation models’ results between counter staff
utilisation, number of travellers served, number of
travellers not served, number of travellers searching
for shortest queue upon arrival and number of
travellers moved to the office, as their p-values are
higher than the level of significant value. The Ho2 _2,
Ho2_3, Ho2_4, Ho2_6 and Ho2_8 hypotheses are therefore
failed to be rejected. As an overall result, modelling
human combined-proactive behaviour for both DES
and combined DES/ABS models is statistically
different in their simulation results performance.
Therefore, the Ho2 hypothesis is rejected.
Table 5 : Results of T-test in Experiment 1
Performance
Measures
Travellers waiting
time
Counter staff
utilisation
Number of
travellers served
Number of
travellers not
served
DES vs. Combined
DES/ABS
P-value
Result
P = 0.801
Fail to reject
P = 0.422
Fail to reject
P = 0.763
P = 0.851
Fail to reject
Number of travellers
searching for shortest
queue (while queuing)
Number of travellers
moved to the office
Fail to reject
Fail to reject
Table 6: Results of T-test in Experiment 2
Travellers waiting
times
Counter staff
utilisation
Number of travellers
served
Number of travellers
not served
Number of requests to
work faster
Number of travellers
searching for shortest
queue (upon arriving)
P = 0.572
Modelling various proactive behaviours in the airport
check-in services has proved that the behaviour of
travellers who always seek faster service is the main
reason that has influenced the performance of both
simulation models. However, this is more noticeable in
combined DES/ABS as modelling travellers’
behaviours is more realistic than in the DES model.
The performance of the combined DES/ABS model in
modelling realistic human behaviours has a significant
impact on the simulation study.
From the comparison investigation, new
knowledge is obtained. The investigation has proven
that DES is capable of producing similar results to
those of combined DES/ABS when modelling the
reactive human behaviour, but further complex
proactive modelling produced different results.
Modelling detail human behaviours in combined
DES/ABS has demonstrated that modelling such
behaviours produce significance impact on the
simulation output performance. This study has shown
that it is useful to model human proactive behaviour as
detail as possible in the service industry in order to get
a good understanding on the service-oriented systems
performance. The best choice of simulation technique
is combined DES/ABS.
6
Performance
Measures
Statistical test is not
available
DES vs. Combined
DES/ABS
P-value
Result
P = 0.000
Reject
P = 0.486
Fail to reject
P = 0.612
Fail to reject
P = 0.218
Fail to reject
P = 0.000
Reject
P = 0.766
Fail to reject
Conclusions and Future Work
In this paper we have demonstrated the
application of simulation to study the impact of
human reactive and proactive behaviour service
systems. In particular we were interested in finding
out more about the benefits of implementing only
reactive or mixed reactive and proactive behaviours.
More precisely, our investigations have focused on
answering the question: Is it useful to model human
proactive behaviour in service industry and which
simulation techniques is the best choice for modelling
such behaviour?
Previously, we have dealt with the reactive
behaviour modelling (Majid et al, 2009) and mixed
reactive proactive behaviour modelling (Majid et al,
2010) in a first case study based in the retail sector
and a second case study based in the public service
sector (Majid et al, 2012). We found that modelling
10
Submitted to Elsevier Science
realistic proactive behaviours that habitually occur in
the real system are worth modelling in the modelled
situations as it has demonstrated a big impact to the
overall system performance in DES and combined
DES/ABS models.
In this paper, we have focused on modelling
different level of proactive behaviour for check-in
services in the airport. Modelling the service-oriented
system as realistically (proactive behaviour) as
possible is found important because modelling such
detail has a significant impact on the overall system
performance – reducing customer waiting time and
number of customers not served. We found combined
DES/ABS model is suitable for modelling the levels
of proactive behaviour investigated in this case study.
In the future we would like to involve with more
complex service-oriented systems, to test if we can
generalise our findings regarding the comparison of
modelling different level of proactive behaviours in
DES and combined DES/ABS techniques.
8.
9.
10.
11.
12.
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Submitted to Elsevier Science
Authors Biographies
MAZLINA ABDUL MAJID is a Senior Lecturer in
the Faculty of Computer Systems and Software
Engineering, Universiti Malaysia Pahang (UMP),
Malaysia. Her research interest is in discrete event
simulation and agent based simulation. Her email is
<mazlina@ump.edu.my>.
UWE AICKELIN is a Professor in the School of
Computer Science, University of Nottingham, UK.
His research interests include agent based simulation,
heuristics optimisation, artificial immune system. His
email is <uxa@cs.nott.ac.uk>.
PEER–OLAF SIEBERS is a Lecturer in the School
of Computer Science, University of Nottingham, UK.
The central theme in his work is the development of
human behaviour models which can be used to better
represent people and their behaviours in Operational
Research type simulation models. His email is
<pos@cs.nott.ac.uk>.
.