Keywords

1 Introduction

Simulation has become a preferred tool in Operation Research for modelling complex systems [1]. Simulation is considered a decision support tool which has provided solutions to problems in industry since the early 1960s [2]. Studies in human behaviour modelling and simulation have received increased focus and attention from simulation research in the UK [3]. 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 [4]. 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 [5, 6]. 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, SD 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 proactive behaviour. Here, reactive behaviour is related to staff responses to the customer when something is being requested and is 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 [7]. 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 a combination of DES/ABS. This study compares both simulation techniques in term of simulation result using a real world case study: check-in services in the airport.

In some of our previous studies: the capabilities of DES and combined DES/ABS in representing the impact of reactive staff behaviour [8]; mixed reactive and proactive behaviour in a retail sector [9] and public sector [10]. In the present paper, we look at the capabilities of DES and a combination of 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 Sect. 2 we explore the characteristics of DES and ABS and discuss the existing literature on modelling human behaviour in service sector. In Sect. 3 we describe our case study and the simulation models development and implementation. In Sect. 4 we present our experimental setup, the results of our experiments, and a discussion of these results. Finally, in Sect. 5 we draw some conclusions and summarise our current progress.

2 Literature Review

2.1 Human Behaviour Modelling in DES and ABS

This section reviews the existing research studies on modelling human behaviour using DES and ABS techniques. As explained by Pew and Mavor [4], 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 [11]. This is because the diversity of human behaviours is more accurately depicted by the use of simulation \ [12].

In all the reviewed work, 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 [5, 6, 13, 14], On the other hand, [15–19], recommend ABS for modelling human behaviour. [5] claims 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 large-scale systems. This characteristic of entities in DES is agreed by [13], 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, [17, 18] have 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, [12] have investigated methods of estimating the impact of imperfect situational awareness of military vehicle operators. They opined that, it is possible to use the DES model to understand human behaviour by matching the results from the DES model with human subjects. [15] comments that, modelling consumer behaviour when grocery shopping is easier using ABS because, this model has the ability to integrate communication among individuals or consumers. [16] asserts 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 argued that ABS is more suitable than DES due to the characteristics of the ABS model; specifically, it allows to model proactive and autonomous entities 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 [4] who studied the operation of courier services in logistics, who investigated manufacturing systems, who studied human travel systems. Others are [3] who looked at the operation of coffee shop services, [21] who is using SIMKIT to model both simulation models and [20], investigated the modelling of earth-moving operations. 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 to represent the communication and autonomous decision-making.

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 has shown 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

The comparison made in this study uses DES and a combination of DES/ABS. As the focus of this research was to investigate a service oriented system in the public sector, which involves queuing for the different services. The use of ABS is not the choice for the investigations carried out in this research, this is because 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 combination of 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 Case Study Description

The operation at the check-in counters in an airport has been chosen as third case study [9, 10] 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 [1].

Figure 1 illustrates the operation at the airport check-in service, the numbering and red arrow represents 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 Fig. 1).

Fig. 1.
figure 1

The operation at the airport check-in service (Color figure online)

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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 Fig. 1). If counter staff are available, then travellers will move to the check-in counter (represented by arrow number 3 in Fig. 1). Once their check-in is completed, the travellers are free to go to their boarding gates (represented by arrow number 3 in Fig. 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 that have been modelled are the behaviours of another member of staff (supervisor) who is responsible for observing and controlling the check-in 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 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 the travellers to execute such proactive behaviour is generated from knowledge that they gathered by 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 the DES/ABS models, as well as conceptual modelling, is then taken to consideration.

4 Simulation Models

Both, the DES model and the combined DES/ABS model are based on the same conceptual model (Fig. 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 (Fig. 2). Two different implementation approaches are used for developing the DES/ABS model: the process-oriented approach is used for the DES modelling (Fig. 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).

Fig. 2.
figure 2

The implementation of DES model

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Fig. 3.
figure 3

The implementation of combined DES/ABS model

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Two simulation models have been developed from the conceptual models and both are implemented in the multi-paradigm simulation software AnyLogic™ [22]. These 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 a combination of DES/ABS model, they are all 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 [6].

A discussion follows on how objects in DES model or agents in DES/ABS model are set up:

Travellers Object/Agent.

The arrival rate of the simulation model is gathered from “Simulation with Arena” by [1]. In both DES and 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
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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.

Table 2. Counter staff service time
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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 check-in 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 h, 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:

Ho 1:

Modelling human reactive behaviour in DES model produce statistically the same simulation results with combined DES/ABS model

Ho 2 :

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
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In this experiment, the general idea of reactive (response to environment) and proactive behaviours as described in Sect. 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 . 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).

Table 4. Results of experiment 1 and experiment 2
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The hypotheses for Experiment 1 are:

Ho1_1

:

The travellers waiting time resulting from the reactive DES model is not significantly different from the reactive combined DES/ABS model

Ho1_2

:

The number of travellers served resulting from the reactive DES model is not significantly different from the reactive combined DES/ABS model

Ho1_3

:

The counter staff utilisation resulting from the reactive DES model is not significantly different from the combined reactive DES/ABS model

Ho1_4

:

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 DES/ABS model

Ho2_6

:

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

Ho2_7

:

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

Ho2_8

:

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

Results for the Experiment 1 and 2 are shown in Table 4 and the results of the T-test are shown in Tables 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, 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.

Table 5. Results of T-test in Experiment 1
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Table 6. Results of T-test in Experiment 2
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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 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 travellers searching for the shortest queue while queuing) have shown lower p-values than the chosen level of significant value (0.05). Therefore, the Ho2_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.

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 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. The study in particular focusses on finding out more about the benefits of implementing only reactive or mixed reactive and proactive behaviours. More precisely, our investigations have been able to answer 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 [8] and mixed reactive proactive behaviour modelling [9] in a first case study based in the retail sector and a second case study based in the public service sector [10]. We found that modelling 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 DES/ABS models.

Also 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. This is because modelling such detail has a significant impact on the overall system performance, as it reduces the customer waiting time and the number of customers not served. We found a combination of DES/ABS model to be suitable for modelling the levels of proactive behaviour that was investigated in the proposed 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 a combination of DES/ABS techniques.