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Abertis de investigación sobre gestión de infraestructuras del transporte y seguridad vial

14- ESPAÑA -

Analytical approach to landside system dynamics at airport passenger terminals

Martí Montesinos Ferré

Pórtico

La red internacional de Cátedra Abertis convoca un año más, junto a prestigiosas universidades, los premios que reconocen a los mejores trabajos de final de carrera, tesinas o tesis doctorales relacionadas con la gestión de infraestructuras de transporte, desarrollados por universitarios de los distintos países en los que opera el Grupo Abertis.

A partir de la creación en el año 2003 de la primera Cátedra Abertis, su presencia internacional ha ido creciendo y constatando el compromiso de la compañía con el mundo académico y contribuyendo a la investigación sobre la repercusión de las grandes obras en el territorio, a la vez que esto permite una mejora en la calidad de vida de sus habitantes.

La Red Internacional de Cátedras Abertis está presente en España, Francia, Puerto Rico, Chile y Brasil, en colaboración con las siguientes universidades: Universitat Politècnica de Catalunya-BarcelonaTech (Barcelona, España); IFSTTAR, École des Ponts–ParisTech, Fondation des Ponts (París, Francia); Universidad de Puerto Rico (San Juan, Puerto Rico); Pontificia Universidad Católica de Chile (Santiago, Chile); y, Universidad de São Paulo (São Paulo, Brasil).

Este modelo de gestión del conocimiento tiene su origen en la firme voluntad de Abertis de colaborar con las universidades, los centros de excelencia y los expertos más destacados en cada materia con el fin de ayudar a generar y a divulgar el conocimiento, poniéndolo al servicio de la investigación y de toda la sociedad. El trabajo distinguido por los Premios Abertis de investigación que ahora tiene en sus manos, quiere ser una muestra más de esta vocación de servicio a los investigadores, a la comunidad educativa y de los profesionales con responsabilidades en el campo dela gestión de las infraestructuras.

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PresentaciónLa Cátedra Abertis de la Universidad Politécnica de Cataluña (UPC) promueve la realización de seminarios y conferencias y la investigación sobre la gestión de infraestructuras del transporte estructurada en los ejes de actividad de la corporación: carreteras y autopistas, tráfico, seguridad vial y sistemas de transporte inteligentes.

Asimismo, con objeto de potenciar el interés de los universitarios españoles, la Cátedra Abertis establece anualmente el Premio Abertis, al mejor trabajo de investigación inédito en gestión del transporte realizado por estudiantes en España. Existen otras Cátedras Abertis similares en otros países como Francia, Puerto Rico, Chile y Brasil.

En la catorceava convocatoria de 2016, se presentaron veintitrés candidatos, todos ellos de elevada calidad. En la categoría de tesinas se presentaron doce contribuciones relacionadas con el diseño de concesiones de autopistas maduras “brownfields” después de vencer el periodo de concesión (caso Abertis), simulación de un sistema público de bicicletas eléctricas, un análisis comparativo de viabilidad entre Hyperloop y AVE en el corredor Madrid-Barcelona, nuevas tecnologías para el transporte de mercancías en España, una herramienta de simulación de tráfico para explotación de datos, la nueva directiva europea de contratación pública y cambios en legislación vigente en empresas de conservación y explotación de carreteras, evaluación y fiabilidad de una red de autobuses, un enfoque analítico y dinámico de las terminales de pasajeros aeroportuarias mediante una visión holística y su departamentalización, una metodología para la reducción de la afección al tráfico en conservación de carreteras mediante el uso de incentivos y penalizaciones aplicado a carreteras de Euskadi, un estudio de la funcionalidad del tráfico en carreteras 2+1 mediante microsimulación aplicado a carreteras del TM de Montserrat en Valencia, eficiencia persistente y transitoria en la producción estocástica y las fronteras de costes aplicado al sector de autopistas, un análisis de la evolución de las trayectorias de ciclistas en puertos de montaña para la mejora de la señalización de tramos curvos conflictivos en las poblaciones de Torres Torres, Serra y Algimina de Alfara en Valencia

El trabajo final de grado “Analytical approach to landside system dynamics at airport passenger terminals: departmentation and holistic view” del Sr. Martí Montesinos Ferrer, Ingeniero de Caminos, Canales y Puertos por la Universitat Politècnica de Catalunya, BarcelonaTech. Ha resultado merecedora de un Áccésit como finalista XIV Premio Abertis 2016.

En este trabajo se muestran las interrelaciones de las complejas actividades de tierra en un aeropuerto además de las diferentes políticas de gestión de todo el sistema. Un sistema de políticas basados en un enfoque holístico prioriza enfoque departamental que además va en la línea de la iniciativa Europea que fomenta el A-CDM (Collaborative Decision Making). Este estudio incorpora características de otros sectores. Los resultados se derivan del enfoque analítico basado en teoría de colas que permite investigar la política de distribución de recursos. La metodología para construir el modelo de abajo hacia arriba pretende mostrar la complejidad del sistema aeroportuario y proponer enfoques a múltiples aspectos que intervienen en él.

Prof. Francesc RobustéDirector de la Cátedra Abertis-UPC

Martí Montesinos Ferrer Analytical approach to landside system dynamics at airport

passenger terminals: departmentalization and holistic view

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ABSTRACT

Landside operations at passenger terminals have increased in importance in the operation of many airports, due to the ongoing growth in passengers and peak-hour demand. However, currently, each landside processing facility is managed locally without a systemic view.

Airport landside systems are complex, with multiple interrelations. Therefore, the impact of different resource management policies on the overall system performance (embarking direction) is analysed. Policies based on a holistic view outperform policies based on departmentalization, without coordination. It is aligned with the European initiative A-CDM (Collaborative Decision Making), which promotes a similar philosophy at a higher scale than the scope of this thesis.

This study incorporates insights from other sectors, such as supply chain management and the bullwhip effect. The results are derived from an analytical approach, based on queueing theory, which allows investigating different time-varying resource allocation policies at each processing facility and their impact on system dynamics.

The bottom-up methodology to build the model aims to show the complexity of airport system and to propose approaches to multiple aspects that intervene in it (departing passenger arrival patterns, lead time to get an additional resource at a facility and passenger flow between facilities, amongst others).



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ACKNOWLEDGEMENTS - AGRAÏMENTS

En primer lloc, vull agrair enormement l’assessorament i consell continu a en César Trapote, així com la seva predisposició. Sense cap mena de dubte, les converses sobre transports que hem mantingut han estat d’allò més enriquidores, no només en l’àmbit d’aquest treball.

També vull agrair a en Francesc Soriguera, que va creure en la meva proposta inicial i em va fer veure la importància d’escollir un tema que m’apassionés (i em segueix apassionant!); tot i que, potser, se sortia de l’habitual per un TFG. Gràcies per involucrar en César!

Vull agrair als que m’han acompanyat durant unes últimes setmanes intenses de redacció. (Those Greeks writing their Master thesis, from whom I did not understand a word…Thanks guys!)

Gràcies a en Marc, Narcís i Patricia pels comentaris per millorar el contingut de la tesina. I a en Lluís per aguantar(-me) les meves llargues explicacions sobre com anava avançant la tesina, durant la nostra estada a DTU.

I com no, gràcies família! Carme, Joan, Humi i Anton, mil gràcies! Per tot



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INDEX

INDEX i

LIST OF TABLES iii

LIST OF FIGURES iv

1. INTRODUCTION AND OBJECTIVES 1

2. PROFESSIONAL PRACTICE 2

2.1 Level of Service 3

3. STATE OF THE ART 4

3.1. Review of models and its applications for an airport system 4

3.1.1. Formal applications of classical, steady-state queuing theory 4

3.1.2 Graphical analyses using cumulative diagrams 4

3.1.3. Detailed computer simulations 5

3.1.4. Characteristics of the models’ wrap up and its applicability 6

3.2. Industry manuals: A-CDM 8

3.3. Supply chain management and bullwhip effect 9

3.4. Review of some particular issues about airport terminal analysis 10

3.4.1. Arrival patterns 10

3.4.1.1. Underlying factors affecting arrival patterns 11

3.4.1.2. Review of models of arrivals patterns 11

3.4.2. Resources’ availability at a processing facility 12

4. PROBLEM STATEMENT 13

4.1. Basic assumptions 13

4.2. Objective function 14

4.3. Bullwhip effect indicator W 15

4.4. Arrivals rate of departing passengers 15

4.4.1. Arrivals rate for a single flight 15

4.4.2. Passenger presentation function 16

4.4.3. Superposition of passenger arrival rates 18

4.5. Resources’ availability model at a processing facility 18

5. MODEL 21



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5.1. Parameters of the model 21

5.2. Queuing theory concepts 21

5.3. Flow between processing facilities 24

5.3.1. Translation vs Reshaping 24

5.3.2. Choice and application 24

5.4. Resource allocation policies 25

5.4.1. Local policy 26

5.4.2. Holistic policy 27

6. NUMERICAL EXPERIMENTATION 29

6.1. Landside operational characteristics of the airport terminal 29

6.2. Base Case Scenario 29

6.2.1. System dynamics applying a local policy 30

6.2.2. Impact of the constraints of one processing facility on the performance of

the overall system 32

6.3. Peak Scenario 34

6.3.1. Results applying a local policy 36

6.3.2. Results applying a holistic policy 36

6.3.2.1. Parameters interpretation 38

6.3.3. Comparison between local and holistic policies and scenario conclusions 39

7. CONCLUSIONS AND FUTURE RESEARCH 40

7.1. Conclusions 40

7.2. Future lines of research 40

REFERENCES 42

APPENDICES

APPENDIX A. Flight Schedules A1

APPENDIX B.Additional data analysis for Peak Scenario A3



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LIST OF TABLES

Table 1. Parameters of cumulative arrivals distribution formula adjusted to British and

Vueling flights ................................................................................................................. 12

Table 2. Type of flight and arrival pattern ...................................................................... 17

Table 3. Values to calibrate 𝑡𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒 function ........................................................... 20

Table 4. Values of the thresholds in local policies to manage the number of filters at a

processing facility ........................................................................................................... 27

Table 5. Operational characteristics for a medium size airport ..................................... 29

Table 6. System load ratio for Base Case Scenario ......................................................... 30

Table 7. Operational performance indicators when changing the managing policy at the

first processing facility .................................................................................................... 33

Table 8. System load ratio for Peak Scenario ................................................................. 35

Table 9. Operational performance indicators when changing the managing policy at the

first processing facility .................................................................................................... 36

Table 10. Total cost of different local policies. Peak Scenario ....................................... 36

Table 11. Parameter ranges for holistic policy ............................................................... 37

Table 12. Total cost of best holistic policies. Peak Scenario .......................................... 37

Table 13. Operational performance indicators for different holistic policies ................ 38

Table 14. Parameter values of best holistic policies ...................................................... 38



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LIST OF FIGURES

Figure 1. Airport passenger terminal flow chart .............................................................. 2

Figure 2.Airport stakeholders, information sharing and its objectives. A-CDM .............. 9

Figure 3. Bullwhip effect in supply chain ........................................................................ 10

Figure 4. Landside flow chart ......................................................................................... 14

Figure 5. Types of presentation distribution for departing passengers ......................... 17

Figure 6. Arrivals for a single flight ................................................................................. 18

Figure 7. 𝑡𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒 as a function of the utilization ratio .............................................. 20

Figure 8. Cumulative diagram for a flight at a certain porcessing facility. ..................... 22

Figure 9. Cumulative diagram and arrival-departure rates diagram ............................. 23

Figure 10. Translation between consecutive facilities ................................................... 25

Figure 11. Estimated arrivals at the check-in for Base Case Scenario ............................ 30

Figure 12. Arrivals at the different facilities for Base Scenario.. Local policy,

with QMAX (check-in)=12pax ............................................................................................ 31

Figure 13. Operation of the check-in facility. Primary y-axis: number of active counters.

Secondary y-axis: Queue (pax per filter), lead time for the additional counter ordered

in the previous node (min), N Supply line (units) and thresholds (pax). ....................... 32

Figure 14. Arrivals at the different facilities for Base Scenario. Local policy, with QMAX

(check-in)=6pax .............................................................................................................. 33

Figure 15. Comparison of resources spent with QMAX (check-in)=6pax and QMAX (check-

in)=12pax ........................................................................................................................ 34

Figure 16. Estimated arrivals at the check-in for Peak Scenario .................................... 35



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1. INTRODUCTION AND OBJECTIVES

Landside operations at passenger terminals have increased in importance in the operation of many airports, due to the ongoing growth in passengers and peak-hour demand. However, there is a lack of systemic view of that issue. Airport system is complex, composed by multiple subsystems and many interrelations. The scope of this study is landside system in embarking direction. The main objectives of this thesis are to contribute to better understanding of system dynamics and to evaluate the impact of processing facilities managing policies on overall system performance, using an analytical approach. This work is organised as follows. Section 4 describes the metrics to evaluate system performance and how to model two inputs that are crucial for landside operations: departing passenger arrivals and lead time between the placement of an order of an additional resource and the delivery at a processing facility. Section 5 focus on resource allocation policies modelling. It also explains the methodology applied to calculate queue management indicators (waiting time and queue length). Finally, numerical experimentation is carried out in Section 6. Base case scenario should clarify concepts about the model and its operation, while Peak scenario aims to test and compare the performance of local and holistic resource allocation policies. This study incorporates insights from other sectors, such as supply chain management and bullwhip effect. It is also aligned with air transport sector initiatives. At a higher scale, A-CDM aims to improve the overall operational efficiency at airports, based on collaboration and information sharing between stakeholders. In this line, resource management policies with a holistic view are expected to outperform policies based on departmentalization, without effective coordination.



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2. PROFESSIONAL PRACTICE Airport field is divided into airside and landside operations. Traditionally, more efforts have been focused into airside (runway capacity, total coordinated take-offs and landings hourly capacity, ATM…) and it has been the key factor when planning and designing airports. Likewise, airside operations used to be the main cause of delay, and user’s satisfaction. However, the increasing passengers demand has led to more complex landside planning, becoming critical to the smooth operation and expansion of many airports, from 80s and on (Gosling, 1989). The following chart shows the different processes in the landside, those that passengers go through.

Figure 1. Airport passenger terminal flow chart (Ashford et al, 2011)

It is clear that the airport system is composed by several subsystems that are interrelated. However, in practice, each subsystem has a different manager which aims the optimal performance of its subsystem, without a systemic view of the whole system. Regarding to our study, for each subsystem analysed (check-in, security control and boarding) the resources allocated at every moment are an independent decision of each manager. Therefore, generally speaking, there is a departmentalization of the terminal processes, without effective coordination. Nonetheless, it is important to be aware that there are different airport management models and airport authorities have a different role depending on the country. It affects the level of freedom to allocate the resources available according to the needs of whole system. In Europe, it is usual that few handling companies provide services (e.g. outgoing passenger acceptance for flight and baggage processes or safe acceptance of outgoing passengers to the aircraft) to several airlines. Thus, focusing on check-in allocation, the procedure works as follows: the handling company requires to the airport



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authority a certain number of counters, according to the demand of the airlines it serves. Then, the handling company manages how many of the pre-assigned counters they assign to a certain flight and how much time. However, another scenario is that the airport authority assigns the check-in counters in a centralized manner. It is typical from Asia, where the airport authority is also responsible for the operational management. On the contrary, in US airports, all the operational decisions in the landside are taken directly by the airline that operates each flight. As mentioned above, although the interdependence between subsystems, in practice, management decisions are taken in a local manner. However, the industry trend is to remark the importance of a holistic approach to manage airport operations, considering each subsystem as a link in a chain of events. This idea is developed in section State of the art, reviewing the new manual of one of the most important operational improvement initiatives: the Airport Collaborative Decision Making (A-CDM). It aims to improve the overall operational efficiency at airports.

2.1 Level of Service

In order to evaluate the performance of the airport system, it is needed a metric. Level of Service (LOS) has been typically measured according to waiting time and space per occupant in a particular part of the terminal. IATA-type LOS ranges from A (better) to F (worst). It is a way to assess the quality of the service at a processing facility or a holding area, independently from the others. But there is no holistic view about the passenger perception. Therefore, our study line aims to incorporate other performance indicators to capture the overall user perceived level of service (e.g. flow continuity or smoothness).



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3. STATE OF THE ART The main aim of this section is to describe the research done in our field of interest. First, reviewing different approaches to airport passenger terminal operations analysis; identifying an aspect of the issue that needs further exploration, according to the research carried out previously. Then, it is demonstrated that the finding is aligned with recent insights of the industry (ACI Europe, IATA, EUROCONTROL initiatives). Additionally, it is shown how research in other fields can be applied to address that problem (supply chain management and bullwhip effect). Finally, some other relevant topics needed to build a complete model are reviewed.

3.1. Review of models and its applications for an airport system

First, as seen in the airport passenger terminal flow chart, airport systems are complex systems due to the links between different subsystems and the amount of traffic characteristics that intervene. However, traditionally, the standard design procedures were based on handbook formulas (e.g. “FAA standards”, see FAA (1988)). From a forecasted number of peak hour passengers (e.g. 30th hour), size of the terminal building and size of the different facilities were calculated using conversion factors between traffic and floor surface, depending on the desired level of service. Generally, these formulas were insensitively applied without taking into account the particularities of each situation: traffic characteristics and operational characteristics of each terminal (Odoni & Neufville, 1992). Therefore, due to the need of improving airport terminal planning, some other approaches have been applied. Odoni & Neufville (1992) proposes a classification for different types of methods used to analyse the flows in an airport terminal. It is the following one, complemented with examples of each type. The examples are aimed to show strong and weak points of each approach and, then, for which applications they are more adequate.

3.1.1. Formal applications of classical, steady-state queuing theory They are not adequate when significant fluctuations (e.g. when the rate of arrivals varies markedly along a day). Thus, steady state approach is not applicable to our line of research.

3.1.2 Graphical analyses using cumulative diagrams Graphical analyses using cumulative diagrams need as input the pattern of arrivals and the service rate. This method has been especially used for analysing single facilities (as an independent unit): Horonjeff (1975) recommended it for check-in counters and, previously, Lee (1966) elaborated a stochastic model to analyse them; Tanner (1966) used a deterministic queueing model for departure baggage handling. Therefore, this method has been applied since many years ago.



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However, due to the difficulties to know the pattern of arrivals in a facility given the departures in the previous one, it has been applied much less frequently to analyse passenger flow through a sequence of facilities. Nonetheless, Dunlay & Park (1978) proposed two models in this line, aiming to relate the cumulative arrivals curves at two successive sets of airport processing subsystem. But no application was presented in the paper. The first model applies tandem queuing theory, with a deterministic approach. Passengers are considered disaggregated by flight, but aggregated service is proposed to be used when all servers of a facility can be used by all passengers. The outputs at each facility are delays and queue lengths and passenger arrivals in the following facility, in which they are used as input. The second model adds complexity to the first one. Instead of considering the simple case where all passengers go directly from one facility to the next one (only compulsory activities), in this case ancillary activities are taken in account. Therefore, the time spent between two consecutive facilities is a deterministic walking time plus an extra time which depends on the probability of using ancillary activities.

3.1.3. Detailed computer simulations Detailed computer simulations aim to analyse the flow through the whole terminal (or at least more than one activity) instead of only through one facility or area. This method provides more detailed information but it is the most complex, needing more detailed inputs and being more difficult to understand. Moreover, lack of generality is a common issue and they usually need reprogramming to fit each terminal. 3.1.3.1. First models of the entire terminal: holistic approach and complexity One of the first models of the whole terminal building using simulation technique was created by Baron and Hennig (1974). The main goal was to calculate the number of servers needed in each facility not to exceed an aimed maximum waiting time, both for departures and arrivals, with a holistic approach. The model generated a flow of passengers entering the terminal, given the departure flight schedule. It was used as input to the processor that generated a departure flow. The processor was modelled as a stochastic black-box (e.g. without taking into account the functional building structure). It is a lumped model.

Airport Landside Simulation Model (ALSIM), developed by the Transportation System Center, is another proof of the complexity of this type of approaches, as it is remarked in Tosic (1992). The set of inputs of this model is based on a very detailed description of an airline schedule, passenger characteristics, terminal geometry and facilities service characteristics. Output consists of very detailed statistics about the number of users, queue and waiting time characteristics or occupancy counts for all facilities, considering that it is a model of the entire terminal building.



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3.1.3.2. New models: object-oriented simulation Simulation models have evolved towards object-oriented simulation, as Tosic (1992) anticipated in the 90s describing it as the most promising modelling technique for airport terminal analysis.

An illustrative example of these new generation terminal simulation models is described in Fonseca i Casas et al (2014), detailing the simulation model for the New Barcelona Airport International Terminal (NAT). It uses Specification and Description Language (SDL), a format object oriented language, for the model definition. It is especially useful when the goal is to obtain a disaggregated or microscopic model (i.e. each passenger has her own behaviour) and there are many concurrent activities. The NAT model aims to cover all processes in the airport. For this purpose, there are three interconnected microscopic submodels: for multimodal access to the terminal, for the terminal building and for the platform. In order to model the passenger flow through the terminal each passenger is implemented as an intelligent agent. Each passenger entity decides how to spend their time in the terminal building based on its set of attributes (flight type, luggage, group size…) and the availability of exceeding time to reach its destination. Moreover, each terminal area is modelled as an element with limited capacity. Thus, when there is a queue at a facility, the incoming passengers cannot be served immediately because the neighbour element they want to go to has no free space and they cannot advance. Then, according to their attributes they decide to wait, increasing the queue length, or to go somewhere else. In the description of the model it is stated that in the terminal building main common facilities are considered (check in area, security control facilities, shopping areas, boarding gates area, baggage claim…). But there is no reference about how to take in account the capacity of service of each facility and its resources managing policy (e.g. number of open check-in counters and its maximum rate of service). Fonseca i Casas et al (2014) highlights that previously developed terminal simulation models have often had a lack of systemic view. They represented only a subsystem of an airport (e.g. check in area) and although done in detail it is not sufficient to analyse the interactions between subsystems.

3.1.4. Characteristics of the models’ wrap up and its applicability 3.1.4.1. Analytical approach and simulations To wrap up, once dismissed the handbook formulas method and classical queueing theory, it is time to decide which method is the most suitable for our analysis. On one hand, queueing graphical analysis is preferred for allowing a continuous understanding of the processes in the system. But the simplicity of the analytical approach is twofold because when modelling the complexity of real-life problems many authors decide to



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use simulations models, considering queueing graphical analysis too simple. Consequently, most of the models with a holistic view of the terminal are simulations. However, they have drawbacks as the need of very detailed data or the difficulties to interpret the operation of the system. That is not as important when designing a new terminal as when analysing the operation of a current system. Simulation models need calibration and not necessarily all the parameters have a physical meaning. Model validation is paramount and the reviewed models tend to use expert validation of modelled processes and comparison between predicted and recorded traffic patterns (e.g. NAT model). 3.1.4.2. Deterministic and stochastic methods On the other hand, methods can be classified according to how they deal with the information used as input: -Deterministic methods. The inputs are assumed as known. Thus, each input has a single value for each instant. -Stochastic methods. Each input is associated with a probability distribution function. Thus, as inputs are an ensemble, random samples of inputs can be obtained using Monte Carlo method, for instance, and the model can be run several times. Then, a distribution of outputs is given as result. In other words, different scenarios and their probabilities of realization are calculated. When analysing passengers flow in a new airport (before construction) the high degree of uncertainty leads to a stochastic approach. While when analysing the flow in an existing terminal, in order to redesign a facility or to plan a building expansion, it is reasonable to use a deterministic approach. In the second case the patterns of loads of the system are pretty well known while in the first one it is difficult to guess the distribution of passenger arrivals. 3.1.4.3. Fields of applicability In this line, De Neufville and Grillot (1982) state that deterministic queueing model (cumulative diagram method) is very appealing for its transparency that allows continuous understanding of the processes underlying the model, in contrast with either stochastic queueing or computer simulation models. Odoni and De Neufville (1992) show their preferences for using cumulative diagram method for the redesign of a particular space within an existing structure; considering that, this approach presumes that the pattern of arrivals is known. On the contrary, simulation microscopic models are better suited to evaluate the layout of a new terminal. The reason is that a microscopic model simulates individual passengers’ behaviour and their interactions with physical barriers, facilities and other passengers. Whereas analytical models usually use predetermined paths and walking speed distributions to determine travel time between facilities (mesoscopic level) or even constant walking speed.



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3.1.4.4. Model choice Therefore, analytical queueing model will be the basis to analyse our system (an existing terminal, with focus on operational performance). Although this approach is more complicated when applied to a complete terminal, in our case, comprehensibility of the method overcomes the drawbacks. It allows investigating different time-varying resource allocation policies at each processing facility and its impact on system dynamics.

3.2. Industry manuals: A-CDM With a holistic approach to airport processes, one can benefit from a better understanding of the interdependence between them and from an integrated management. There are several initiatives supporting this idea, such as Airport Colaborative Decision Making (A-CDM), promoted by ACI Europe, Eurocontrol and IATA, amongst others. It has a broader spectrum than our analysis, which is focused on passengers’ flow and processing facilities, but it gives a good overall vision of airport management needs. A-CDM is mainly based on the benefits from sharing information between the elements of a system or chain and avoiding departmentalization when taking decision. As it is described in Airport CDM Implementation Manual (version 4, March 2012), A-CDM aims to improve the operational efficiency of all airport stakeholders (aircraft operators, ground handlers, airport operators, network operations and air traffic control). Sharing information and achieving common situational awareness (e.g. by tracking the progress of a flight), the predictability of events is increased. It leads to lower delays and better utilization of the resources.



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Figure 2.Airport stakeholders, information sharing and its objectives. A-CDM (2012)

The manual gives very detailed guidelines to implement A-CDM, with a particular focus on the aircraft turnaround and pre-departure sequencing process. For our line of research, it is important to highlight two additional objectives: tracking and downstream estimates updating.

3.3. Supply chain management and bullwhip effect A-CDM is quite recent but information sharing and resources optimization in a system is an issue analysed in other fields for a long time. For instance, in supply chain management. In single-echelon chains, the lack of information sharing causes extra operational cost. It is typically explained using the Beer Game, created by MIT professors to demonstrate key principles of supply chain management and to explain the bullwhip effect. Through this example of the operation of beer supply chain (with 4 elements: retailer, wholesaler, distributor and brewer), it is shown the trend of larger fluctuations in inventory when going upstream the supply chain (i.e. the brewer experiments the



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largest inventory swings). In this case, the extra cost is having a larger stock than the objective (no optimal resources allocation).

Figure 3. Bullwhip effect in supply chain

Variations in demand at the retail end tend to dramatically amplify upstream the supply chain (Figure 3). This is the so-called bullwhip effect and it occurs because each element works as an “island” (no information shared and no systemic thinking to hold a common safety stock). Then, each element is forced to keep its own safety stock (with respect to the demand of the previous element), due to uncertainties in demand and lead time. Therefore, it has been shown that sharing information reduces bullwhip effect, as uncertainty decreases and demand is forecasted better. Ouyang & Daganzo (2008) propose an indicator to evaluate bullwhip effect. This bullwhip effect metric compares orders fluctuation versus demand fluctuation. If orders fluctuation is higher than demand fluctuation (assessed with RMSE), then there is bullwhip effect. It is called worst-case metric because it is evaluated in all customer demand conditions and it only shows no bullwhip effect if there is no RMSE amplification upstream under any condition.

3.4. Review of some particular issues about airport terminal analysis

3.4.1. Arrival patterns The passengers’ arrival distribution at the check-in is a critical input for the model, in the first step of the chain. Forecasting the passengers’ arrival distribution in an airport has been one of the most challenging issues in airport planning and operations, due to many factors are involved.



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3.4.1.1. Underlying factors affecting arrival patterns For example, Odoni & De Neufville (1992) show that time of arrival at the terminal before the SDT is affected by passenger perceptions (the perceived amount of time needed from arriving to the terminal until boarding and its uncertainty):

𝑡0 ≅ �̂�𝑤 + �̂�𝑝 + 𝑐1�̂�(𝑇𝑤) + 𝑐2�̂�(𝑇𝑝) (3.1)

Where 𝑡0 is the time a passenger arrives at the terminal before the scheduled boarding

time; �̂�𝑤 is the total amount of time a passenger perceives to spend at the processing

facilities; and �̂�𝑝 is the total amount of time a passenger perceives to spend traveling

through the terminal. �̂�(𝑋) is the standard deviation of X and 𝑐𝑥 coefficients capture the importance of “making the flight” for the passenger, amongst others. For example, a domestic regular traveller will arrive later to the terminal because the lower estimation she does about the time she needs to make the flight and the lower uncertainty she assigns to its estimation. On the contrary, an international occasional traveller, for whom losing the flight has a higher cost, will arrive sooner. From this approach, it can be concluded that reducing the variability of processing times (waiting and service times) and travel times within the terminal reduce the time passengers arrive at the terminal before the SDT. In a similar way, arrival patterns are affected by the infrastructure to access to the airport: a more reliable system of transport with less variability of ground access time to the airport leads to passengers that arrive later to the airport. Therefore, the arrivals curve might experience significant variations for the same type of flight from one year to another, due to changes in the airport system (including the access).

3.4.1.2. Review of models of arrivals patterns IATA (2014, Airport Development Reference Manual) explains that different check-in

arrival patterns may apply to different type of flights (regular/charter,

domestic/international). It proposes to define the arrival patterns by the percentage of

passengers per flight arriving in 10min periods, based on historical data. Airline specific

Departures Control Systems (DCSs) are the best-recommended source of historical data.

The first arrivals usually start around 150min prior to the flight departure.

Different arrival distribution functions have been widely used in the literature such as

Normal, Erlang (a form of Gamma distribution), Gauss or Poisson.

Tosic (1992) reviews about the departure passenger flow: Baron and Hennig (1974) found the time distribution of departing passengers to have Gamma distribution, with S-shaped cumulative curve, for German airports. While Tosic et al (1983) also found S-



12

shaped cumulative arrival curves at Belgrado Airport. Then, although it is shown that arrivals depend on the type of flight, S-shape is consistently observed for cumulative arrival curve. Llorens (2005) identifies two standard cumulative distribution functions for two type of flights at Terminal 2B of Barcelona Airport. The following formula is used to model the arrivals cumulative curve for a single flight:

𝐴𝑃(𝑡) = 𝐶𝑇 ·𝑎

𝑎 + 𝑒𝑏·(100−

100𝑡𝑂𝑝

)

(3.2)

CT is the theoretical passenger occupation of the flight, Op is the opening check-in time before the scheduled departure time (SDT) and a and b are parameters to calibrate by least square method (b/a<<1).

Good fit to arrivals cumulative arrivals curve of British and Vueling flights at Barcelona

Airport is observed, with the parameters of Table 1 (Op=180min). Both resultant

cumulative curves are clearly S-shaped.

Table 1. Parameters of cumulative arrivals distribution formula adjusted to British and Vueling flights. Llorens (2005)

3.4.2. Resources’ availability at a processing facility In our model, it is needed to estimate the lead time from the moment an order to open an extra filter (e.g. a check-in counter) is placed to the moment the resources are ready at the facility. In taxi services, it can be found an analogy to the aforementioned issue. Several models have been used to evaluate taxi services in terms of waiting time for users, as it is reviewed in Salanova et al (2014). Taxis have three operational modes (hailing, stand and dispatching). For this case, dispatching mode is very adequate, because it includes idle, assigned and servicing taxis. Zamora (1996) proposes the following formulation for the average waiting time in the dispatching market:

𝑇𝑤 =0.4𝑟

�̅�√𝜆𝑑 − 𝜆𝑢 ·𝑟𝐴0.5

2�̅� · 𝜖

(3.3)



13

Where 𝑇𝑤is the waiting time of costumers (min). 𝜆𝑑is the taxi hourly supply (vehicles per hour and area of service) while 𝜆𝑢, the hourly demand for taxi trips (trips per hour and area of service). �̅�: average speed of the trip (km/h); 𝐴: area of the region; 𝜖: relation between travel time with and without passenger in the dispatching market ; 𝑟: area and network parameter. Waiting time 𝑇𝑤 is the sum of reaction time (considered negligible by Zamora) and taxi access time (taxi travel time between the customer and the nearest taxi). In the formula (Eq 4.3), it is captured the influence of the density of free taxis in the area.

4. PROBLEM STATEMENT The processes a passenger goes through at the airport before departing form a system. The landside system (embarking direction) is mainly governed by three subprocesses, which affect the overall performance of the system. Currently, these three subprocesses or subsystems (check-in, security control and boarding) are managed independently, although they are interrelated. Therefore, it will be compared the impact of a local policy to allocate resources on the overall performance to a holistic policy, which take into account the effect of each subsystem both downstream and upstream.

4.1. Basic assumptions The analytical model is developed with a set of assumptions. As explained before, the airport system is quite complex, with many interrelations. Therefore, it is necessary to simplify in order to an easier comprehension of the system dynamics. The simplified landside system (embarking direction) includes only the main subsystems

for our purpose (i.e. passport control is omitted). The model is based on tandem queue

theory. It is assumed no intermediate reservoir between two consecutive processing

facilities.

Therefore, the complex flowchart of Figure 1 is simplified as follows:



14

Figure 4. Landside flow chart. Modified from Solak et al (2009)

The reservoirs are omitted: no departure hall (before the security control) neither

departure room. Additionally, distant check-in is not considered. The implications of this

assumptions will also be discussed later.

Finally, in order to analyse the system a macroscopic approach is used. It is a reasonable

assumption that during high demand periods, passenger flow is distributed optimally

amongst parallel filters (parallel queues with the same length). Then, it is used an

aggregated rate of service at each processing facility (the sum of the rates of service of

each filter). Aggregated queuing theory does not aim for following individuals in a micro

level and labelling them with their flight. In other words, it can be seen as one single

flight (superposition of all the flights) that allows the managers of each processing

facility to share all the parallel filters as a common resource.

This aggregated approach does not allow to impose constraints such as that all the passengers check their luggage earlier than 40min prior to the SDT or that the boarding closes 15min prior to the SDT. Notice that there may be small variations in these values, from one airport to another. However, in our model the fulfilment of these conditions is controlled with other indicators of the model, such as the waiting time and the maximum queue at each processing facility.

4.2. Objective function Measuring the system performance with only one indicator seems a difficult tasks. The

aim is to evaluate the amount of resources spent but also the passenger level of service.

However, in order to compare the different resource allocation policies, it is interesting

Check-inSecurity control

Boarding



15

to do this exercise of simplification. It is proposed the following aggregated objective

function (C):

𝐶 = 𝐶𝑝 + 𝐶𝑟

(4.1)

Total cost (C) equals to total passenger cost (𝐶𝑝) plus total resources cost (𝐶𝑟).

Total passenger cost is integrated by cost of waiting times (𝑤𝑡) at each processing

facility j weighted by value of time (𝑐𝑝), considering all passengers processed.

𝐶𝑝 = 𝑐𝑝 ∑ 𝑤𝑡𝑗 𝑇𝑂𝑇𝐴𝐿

(4.2)

Passenger value of time is estimated as EUR 60 per hour (Eurocontrol, 2013).

Total resources cost is calculated estimating a cost of EUR 15 per hour and worker (𝑐𝑤):

𝐶𝑟 = 𝑐𝑤 ∑ 𝑁ℎ𝑜𝑢𝑟 𝑥 𝑤𝑜𝑟𝑘𝑒𝑟𝑗 𝑇𝑂𝑇𝐴𝐿

(4.3)

For the sake of simplicity, it is assumed the same cost per hour worked for all processing facilities.

4.3. Bullwhip effect indicator W In order to have a complimentary indicator to compare local policies and holistic policies, it is adapted bullwhip effect metric from Ouyang & Daganzo (2008):

𝑊𝑗 = [∑ 𝑐�̅�

2(𝑡)𝑡𝑓

𝑡𝑖

∑ �̅�𝑗2(𝑡)

𝑡𝑓

𝑡𝑖

]

12

(4.4)

Where 𝑐�̅� is the variation of capacity at processing facility 𝑗 with respect its average and

�̅�𝑗 is the variation of arrival rate at processing facility 𝑗 with respect its average.

4.4. Arrivals rate of departing passengers In order to generate the aggregated arrival rate for our model an Excel tool is developed.

4.4.1. Arrivals rate for a single flight It is based on the following formulation, for the arrivals rate at the check-in for a single

flight:

𝑃𝐴𝑋𝑖 (∆𝑡𝑛) = 𝛼𝑖 · 𝐶𝑖 · (𝐴𝑖(𝑡𝑛+1) − 𝐴𝑖(𝑡𝑛))

(4.5)

Where 𝑃𝐴𝑋𝑖 are the departing passengers that arrive at the check-in to take the flight

𝑖 during the time interval ∆𝑡𝑛 (pax). 𝛼𝑖: airplane occupation ratio for flight 𝑖; 𝐶𝑖: the



16

airplane capacity for flight 𝑖 (pax); 𝐴𝑖(𝑡𝑛+1) − 𝐴𝑖(𝑡𝑛): percentage (expressed as a

decimal) of departing passengers of flight 𝑖 that arrive at the check-in during the interval

∆𝑡𝑛 with respect to the total number of passengers of flight 𝑖 that do the check-in on-

site.

It is assumed that all the passengers do the check-in on-site (i.e. no distant check-in

assumption). Thus, the arrivals curve at the check-in facility is the same as the arrivals

curve at the airport entrance, with a lead time (translation). If not, an additional ratio

should be added in the formula: the number of passengers that use the check-in facility

(to get their boarding card, to check their luggage or both) with respect the total

numbers of passengers of flight 𝑖.

Finally, the occupation ratio of the airplane is one of the KPIs in the management of airlines, which tend to publish it only when it is positive. Thus, it is not that simple to obtain a reliable indicator. It is assumed an average occupation ratio for each type of flight. Differentiating the occupation ratio between types of flight improves the model clarity (e.g. a charter flight, typically with higher occupation, puts more pressure on the system, for the same type of airplane).

4.4.2. Passenger presentation function 𝑨𝒊(𝒕) The passenger presentation function for a single flight 𝐴𝑖(𝑡) represents the percentage

of passengers that have arrived to the check-in until the instant t, with respect to the

total number of passenger that do the check-in.

According to the literature, several distribution functions are candidates. After testing

several Erlang, Normal and Gauss distributions, together with Llorens´ distributions for

Vueling and British flights, Figure 5 shows the final choice:



17

Figure 5. Types of presentation distribution for departing passengers

Figure 5 aims for representing different arrival patterns, according to the type of flight.

Table 1 summarizes the presentation distribution assigned to each type of flight.

Table 2. Type of flight and arrival pattern

SHORT HAUL LONG HAUL

SCHEDULED Vueling-type British-type NON SCHEDULED (CHARTER) Erlang distribution

Considering only three different arrivals patterns improves simplicity and it is consistent

from a conceptual point of view. The aim is to test the landside system given different

patterns of load, but not for a specific airline, destination and departure time.

Generally, the longer the trip the greatest importance for the passenger of making the

flight (i.e. higher perceived penalty for losing it due to tardiness). Thus, following Odoni

& De Neufville formulation (Eq. 4.1), the time a passenger arrives at the terminal before

the SDT is greater for a long distance flight than for a short distance one. It is observed

comparing Vueling and British distribution. For the long haul flight (Bristish-type), almost

50% of the passengers have arrived 90min before SDT, while only around 25% for the

short haul flight (Vueling-type).

On the other hand, the steepest curve is the Erlang. Meaning that it represents a more

homogeneous population. From Eq. 4.1, it can be seen that if most of the passengers

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

-200 -150 -100 -50 0

A(t)

SDT (min)

Presentation distributions

Vueling

British

Erlang



18

are similar (e.g. family groups of a cruise tour) it is more likely to have small variations

in their perceived necessary time to make the flight. Moreover, charter flights are

characterised by accessing the airport by buses exclusive for charter flight passengers.

Therefore, arrivals will be concentrated in a smaller gap.

It is important for the operations of landside system because concentrated arrivals

increase the pressure on the system. This is clear in Figure 6.

Figure 6. Arrivals for a single flight

In Figure 6, it is assumed 𝛼 = 80% for scheduled flights and 𝛼 = 90% for charters;

𝐶 = 180 𝑝𝑎𝑥.

4.4.3. Superposition of passenger arrival rates Once the rates of arrivals at each time interval are generated for each single flight, it is just needed to aggregate them according to SDT. Then, it will be obtained the aggregated rate of arrivals to the check-in facility, considering all flights.

4.5. Resources’ availability model at a processing facility The resource’s availability model aims to estimate the lead time from the moment an order to open an extra filter (e.g. a scanner at the security control) is placed to the moment the necessary resources to do so are ready at the facility.

0

5

10

15

20

25

-200 -150 -100 -50 0

pax

SDT (min)

Arrivals each 5 min

Vueling

British

Charter



19

The model should capture two different phases in the airport landside operations:

1st Phase: When there are few resources allocated in a facility, the time to get an extra resource is minimum because there are on hold resources. When the utilization of the resources is low, it is possible to have some resources in a waiting room to quickly respond to the request of opening an extra filter. They are called backup resources. 2nd Phase: When the utilization ratio is high (e.g. >60%), there are no more available backup resources. Regarding to the resources needed to open an extra security scanner, the security staff may be free somewhere in the terminal after finishing a task, on patrol or assigned. Assigned guards include those that have been called to open an extra security scanner and they are going to the security control.

Therefore, the time to open an extra filter is a function of the number of resources available and the distance to the processing facility. The time increases as the utilization ratio increases. The utilization ratio is defined as the number of active resources at the processing divided by the total number of resources.

According to the previous reasoning, the following function describes the time between

an order for an extra resource is placed and the resource starts running at the facility.

𝑡𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒 = 𝑡𝑠𝑒𝑡𝑢𝑝 + 𝑡𝑎𝑐𝑐𝑒𝑠𝑠

(4.6)

𝑡𝑎𝑐𝑐𝑒𝑠𝑠 = {𝑡𝑎𝑐𝑐𝑒𝑠𝑀𝐼𝑁 ± 𝑡𝑎𝑙1 < 𝑢𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑

𝑡𝑡𝑎𝑥𝑖 ± 𝑡𝑎𝑙2 ≥ 𝑢𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑

(4.7)

For the clarity of the model, 𝑡𝑠𝑒𝑡𝑢𝑝 is included. In some cases, the boot time of the new

active filter system may be significant or even the reaction time of the manager to take

a decision. With the same spirit, 𝑡𝑎𝑙1 and 𝑡𝑎𝑙2 aim to highlight the uncertainty in the lead

times.

𝑡𝑡𝑎𝑥𝑖 to model the access time in the 2nd phase is based on the Zamora’s formulation (Eq. 3.3) for the taxi access time in the dispatching market. In the dispatching market, the customers call for an immediate taxi service, as the processing facility manager does to request an extra filter. In the taxi model, there are also three states: idle (free staff somewhere in the terminal but not in the backup room), servicing (on patrol) and assigned taxis. The 1st phase is not based on this formulation because it is assumed that when the utilization ratio is lower than a certain value, all the extra resources come from the backup room. Then the distance from the backup room to the facility is known and constant, unlike the distance between a random customer and the nearest idling taxi.



20

For the sake of simplicity, Zamora’s formulation is modified as follows:

𝑡𝑡𝑎𝑥𝑖 =𝑅𝑅

�̅� √1 − 𝑢 · 𝐵

(4.8)

Where 𝑢 is the utilization ratio. �̅�: average speed of the trip (km/h); 𝑅𝑅: area parameter;

𝐵: utilization sensitivity parameter

After calibration, according to empirical practical criteria:

Table 3. Values to calibrate 𝑡𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒 function

𝒕𝒔𝒆𝒕𝒖𝒑 𝟎. 𝟓 𝒎𝒊𝒏

𝒕𝒂𝒄𝒄𝒆𝒔𝑴𝑰𝑵 2 𝑚𝑖𝑛

𝒕𝒂𝒍𝟏 1 𝑚𝑖𝑛 𝒖𝒕𝒉𝒓𝒆𝒔𝒉𝒐𝒍𝒅 0.6

𝒕𝒕𝒂𝒙𝒊 �̅� 5 𝑘𝑚/ℎ 𝑅𝑅 22.09380 𝐵 1.01695

𝑡𝑡𝑎𝑥𝑖𝑚𝑎𝑥 35𝑚𝑖𝑛

𝑡𝑎𝑙2 2 𝑚𝑖𝑛

In Figure 7, it is plotted 𝑡𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒 function.

Figure 7. 𝑡𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒 as a function of the utilization ratio

With this simplified model, the two main characteristics in the lead time when an additional resource is ordered are captured: increasing lead time when there are less resources available and its randomness. They both have impact on the managing policies to allocate the resources and on bullwhip effect.

0

10

20

30

40

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Tim

e (m

in)

Utilisation ratio



21

5. MODEL The main aim of this section is to describe the processing facility resources allocating model and the methodology applied to calculate queue management indicators. Waiting times and queue lengths, computed using queueing theory concepts, are used as inputs for managing the resources allocation. Two types of different policies and the logic they follow are described.

5.1. Parameters of the model The parameters that intervene in the model are described below: 𝑄: queue length [pax]

𝑤𝑡: waiting time [min]

𝜆: arrival rate [pax/min]

𝜇: service rate [pax/min]

𝑐: maximum service rate or capacity [pax/min]

𝑡𝑠: service time [min]

𝑆: offset between to cumulative curves due to a translation [min]

𝑁𝑎𝑐𝑡𝑖𝑣𝑒: number of active filters/resources (e.g. in the check-in facility: number of open

counters)

𝑁𝑑𝑒𝑠𝑖𝑟𝑒𝑑: number of active filters wanted at a processing facility

𝑁𝑒𝑥𝑡𝑟𝑎𝑜𝑟𝑑𝑒𝑟: number of extra resources ordered at a processing facility

𝑁𝑒𝑥𝑡𝑟𝑎𝑎𝑟𝑟𝑖𝑣𝑎𝑙: number of extra resources that arrive at a processing facility

𝑁𝑠𝑢𝑝𝑝𝑙𝑦 𝑙𝑖𝑛𝑒: difference between cumulative 𝑁𝑒𝑥𝑡𝑟𝑎𝑜𝑟𝑑𝑒𝑟 and cumulative 𝑁𝑒𝑥𝑡𝑟𝑎𝑎𝑟𝑟𝑖𝑣𝑎𝑙

∆𝑡: time interval [min]

It is used discrete modelling. Thus, the parameters may have indexes, as follows:

𝜇𝑗 𝑡𝑜𝑡𝑎𝑙(𝑖): aggregated service rate of the processing facility 𝑗 at time interval 𝑖.

5.2. Queuing theory concepts Cumulative diagrams are the key element in queuing theory. It allows calculating average and maximum queue length and waiting time, with a simple graphical analysis. A cumulative arrival diagram, A(t), indicates how many users have entered the system

while a cumulative departure diagram, D(t), indicates how many of them have left it.



22

The queue length at a given time t is the difference between the cumulative arrivals and

the cumulative departures:

𝑄(𝑡) = 𝐴(𝑡) − 𝐷(𝑡)

(5.1)

The waiting time for the passenger n is the difference between the time she arrives and

the time she leaves (in FIFO system):

𝑤𝑡(𝑛) = 𝑡|𝐷(𝑡)=𝑛 − 𝑡|𝐴(𝑡)=𝑛

(5.2)

This is shown in Figure 8, for a processing facility, with the input of only one flight.

In the example shown above the individual customers (passengers) are represented as

a continuously flow rather than discrete entities. However, as the processing facility is

opened considerably late after the first arrival, it is only possible to distinguish two

different phases (growth and decline of the queue). From Figure 9, it is clear that there

is another important phase. This stagnant phase occurs when there is no queue and

n

Figure 8. Cumulative diagram for a flight at a certain porcessing facility. Modified from de Neufville and Odoni (2003)



23

passengers are served as they arrive (at a lower rate than the maximum service

capacity).

Figure 9. Cumulative diagram and arrival-departure rates diagram. MOPTMA (1995)

Queueing phases in Figure 9:

a) From 0 to t0: Stagnant Phase

𝑄(𝑡) = 0 𝑎𝑛𝑑 𝜆(𝑡) ≤ 𝑐

𝑇ℎ𝑒𝑛 𝜇(𝑡) = 𝜆(𝑡) 𝑎𝑛𝑑 𝐷(𝑡) = 𝐴(𝑡) where 𝜆 is the arrival rate at the processing facility,

𝜇 is the departure rate (rate at which customers are served)

and 𝑐 is the maximum service rate or capacity.

A passenger is served as she arrives. There is no queue.

b) From t0 to t2: Queue growth Phase

𝜆(𝑡) > 𝑐

𝑇ℎ𝑒𝑛 𝜇(𝑡) = 𝑐 𝑎𝑛𝑑 𝐷(𝑡) < 𝐴(𝑡)

Arrival rate exceeds the maximum service rate. Thus, queue grows.

c) From t2 to t3: Queue decline Phase

𝑄(𝑡) > 0 𝑎𝑛𝑑 𝜆(𝑡) ≤ 𝑐

𝑇ℎ𝑒𝑛 𝜇(𝑡) = 𝑐 𝑢𝑛𝑡𝑖𝑙 𝐷(𝑡) = 𝐴(𝑡) 𝑎𝑛𝑑 𝑄(𝑡) = 0

When arrival rate decreases and becomes smaller than the capacity c, the queue

begins to decrease. The processing facility works at its maximum service rate

until vanishing the queue.

At t=t2 the queue reaches its maximum 𝑄(𝑡2). The waiting time for the person

arriving at t=t2 is equal to 𝑄(𝑡2)

𝑐.

d) From t3: Stagnant Phase



24

In our model, a discrete approach is used. However, the same concepts are applicable.

Arrivals, departures an queue length are computed at the end of each interval 𝑖

(equivalent to node 𝐼 + 1).

It is important to notice that in the abovementioned cumulative analysis, service time is not included. Then, assuming a deterministic service time 𝑡𝑠, the cumulative departure diagram needs to be shifted to the right this 𝑡𝑠 gap.

5.3. Flow between processing facilities There are different approaches to estimate the time spent between consecutive processing facilities.

5.3.1. Translation vs Reshaping The simplest one is to consider that there is no significant intermediate reservoir between two consecutive processing facilities. Then, the shape of the cumulative arrival curve for the later facility is the same as the shape of the cumulative departure curve for the previous facility in the sequence. Therefore, it is a translation with a constant gap between the two curves (i.e. the time spent between consecutive processing facility only depends on the distance). A more sophisticated approach considers the ancillary activities between processing facilities. Thus, the propagation process from the departure curve to the arrival curve in the later facility is not a direct translation. Instead of that, the curve is reshaped, due to different times to get from facility A to facility B, for each type of passenger (reshaping). Some examples were reviewed. For example, in the NAT model, each passenger is a unique entity and spends a certain time between A and B, according to its attributes and the ancillary facilities along its way. In other words, when no intermediate reservoir, passenger flow between processing facilities is modelled as a first out first in discipline (FOFI). While, when considering that ancillary facilities are relevant, a passenger X that comes out later from the check-in facility than a passenger Y could arrive earlier at the security control, depending on its attributes or preferences (e.g. Y is more likely to go to the coffee shop when travelling in a big group with children).

5.3.2. Choice and application Tosic (1992) remarks that even rather elaborate models do not pay attention to intermediate waiting areas or ancillary facilities, connecting directly (space-wise and time-wise) check-in output flow to the next terminal network node (e.g. security check). For the sake of simplicity, this is the approach used in the model. Moreover, translation is very appropriate approximation in airports where the travel time from check-in counter to security check is similar for each counter. It is a bit more debatable when applied to the flow from security check to boarding gates.



25

In short, in our model it is used translation. It implies that the departure curve of one facility is used as the arrival curve for the following one, with an offset due to walking (S). This can be seen in Figure 10.

Figure 10. Translation between consecutive facilities

Besides the ancillary activities, there are other sources of distortion in the propagation process of the cumulative curves. For instance, bottle necks (e.g. escalators), where lamination occurs. Additionally, when considering walking speed not constant, cumulative arrivals shape change from cumulative departures curve. Variable walking speed is typically modelled as lower speed when higher passenger density. In this line, Solak et al (2009) use a linear relationship between walking speed and density (passengers/m2) (i.e. walking speed has a maximum of 4.82 km/h and decreases linearly with increasing density). However, in both cases FOFI is mantained.

5.4. Resource allocation policies The objective is to generate functions that simulate the two different types of policies to allocate resources, described in the Problem Statement. The processes a passenger passes through at the airport before departing form a system.

The landside system (embarking direction) is mainly governed by three subprocesses,

which affect the overall performance of the system. These three subprocesses or

subsystems (check-in, security control and boarding) are managed independently,

although they are interrelated.

Therefore, it will be compared the impact on the overall performance of a local policy to allocate resources and a holistic policy, which take into account the effect of each subsystem both downstream and upstream.

pax

time

Translation

Output from facility A Input to facility B



26

5.4.1. Local policy Traditionally, the managers of each processing facility have taken decisions only according to their subsystem (local decision making). One common way to decide if opening a new filter, or closing one of the active ones, is based on a visual criterion. If the queue length exceeds a certain point, the manager orders to open an extra filter. As the queue length is directly related to the waiting time, it is a way to control both time and space limitations. When it is measured the queue length per filter, imposing not to exceed X people means

that, when maximum waiting time exceeds 𝑋

𝑐𝑓𝑖𝑙𝑡𝑒𝑟 , a new server is opened to reduce it.

However, at the security control there is usually a shared line before splitting in small

ones in front of each conveyor belt to the x-ray machine. Then, the manager takes the

decision according to the total queue length.

Another important aspect to consider is what to do when a new filter was ordered but

it has not arrived yet and the queue continues growing. Actually, due to the uncertainty

of the arrival of the order and the anxiety it causes, some managers do not consider the

additional resources ordered that have not arrived yet (𝑁𝑠𝑢𝑝𝑝𝑙𝑦 𝑙𝑖𝑛𝑒); amplifying the

bullwhip effect. It is clearer with an example: according to the defined criteria, it is

needed only one extra resource, which is coming, but it has not arrived before the next

time-window to place an order. Then, the manager places a “just-in-case” order for

another additional resource (not needed if the previous order arrives).

The function used to describe the local or departmentalised policy is described below in

pseudocode:

If Queue Q(i) > QMAX: Nextraorder(i+1)= +1;

Else if Q (i) < QMIN & Q (i)< Q (i-1) & Nactive>1: Nextraorder(i+1) = -1;

Else: Nextraorder(i+1) = 0;

The decision is taken at the end of each time interval 𝑖 (i.e. at the node 𝐼 + 1).

The initial condition is set as Equilibrium condition:

Nj active(1) = [λj total

cj filter]

+

(5.3)

Then, the decision to open an extra filter or to close an active filter is taken at the end of each time interval 𝑖 (e.g. it is ordered an additional resource at node 𝐼 + 1, then 𝑁𝑒𝑥𝑡𝑟𝑎𝑜𝑟𝑑𝑒𝑟(𝑖 + 1)=1). However, as explained above, there is a lead time (𝑡𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒) between the order of an extra filter and the arrival of the necessary resources to do so, which is estimated with Eq. 4.6. Thus, the extra resource ordered at time 𝑡 will be arrive at the processing facility at time 𝑡 + 𝑡𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒. Using a discrete approach:



27

𝑇𝑇 = 𝑟𝑜𝑢𝑛𝑑 (𝑡𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒(𝐼 + 1)

∆𝑡)

(5.4)

𝑁𝑒𝑥𝑡𝑟𝑎𝑎𝑟𝑟𝑖𝑣𝑎𝑙(𝑖 + 1 + 𝑇𝑇) = 𝑁𝑒𝑥𝑡𝑟𝑎𝑎𝑟𝑟𝑖𝑣𝑎𝑙(𝑖 + 1 + 𝑇𝑇) + 𝑁𝑒𝑥𝑡𝑟𝑎𝑜𝑟𝑑𝑒𝑟(𝑖 + 1)

(5.5)

Therefore, the number of active resources at a processing facility:

𝑁𝑗 𝑎𝑐𝑡𝑖𝑣𝑒(𝑖 + 1) = 𝑁𝑗 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖) + 𝑁𝑒𝑥𝑡𝑟𝑎𝑎𝑟𝑟𝑖𝑣𝑎𝑙(𝑖 + 1) (5.6)

𝑗: check-in facility, security control facility

It is assumed that closing an active filter is instantaneous.

For the boarding gates, there is a small variation, as usually, the number of resources is

not based on the previous ones plus an increment. The number of preassigned gates

𝑁𝑝𝑟𝑒𝑎𝑠𝑠𝑖𝑔𝑛𝑒𝑑 is a function of the scheduled flights, the opening time and the closing time

of the boarding process.

𝑁𝑏𝑜𝑎𝑟𝑑𝑖𝑛𝑔 𝑎𝑐𝑡𝑖𝑣𝑒(𝑖 + 1) = 𝑁𝑏𝑜𝑎𝑟𝑑𝑖𝑛𝑔 𝑝𝑟𝑒𝑎𝑠𝑠𝑖𝑔𝑛𝑒𝑑 (𝑖 + 1) + 𝑁𝑒𝑥𝑡𝑟𝑎𝑎𝑟𝑟𝑖𝑣𝑎𝑙(𝑖 + 1)

(5.7)

For each flight, it is preassigned one gate with two agents. If needed, a support agent is

requested to speed up the boarding (i.e. to increase the service rate).

For the check-in, the system allows requesting two extra counters, instead of only one.

It happens when the queue exceeds the maximum and, additionally, is growing

consistently (e.g. for 15min).

Finally, the Table 4 shows a proposal of values of the maximum and minimum queue

length, within the range the manager does not order an extra resource.

Table 4. Values of the thresholds in local policies to manage the number of filters at a processing facility

The values are experience based and should be interpreted according to the capacity of the filters at each processing facility, described in the operational characteristics (section 7.1). As explained above, the security control policy is defined as function of total queue length.

5.4.2. Holistic policy A holistic policy takes into account the impact of the policy applied to a processing

facility on others. In order to analyse the performance of a holistic policy, it is proposed

𝑪𝒉𝒆𝒄𝒌 − 𝒊𝒏 𝑸𝑴𝑨𝑿 (per filter) 12pax

𝑄𝑀𝐼𝑁 (per filter) 4pax

𝑺𝒆𝒄𝒖𝒓𝒊𝒕𝒚 𝒄𝒐𝒏𝒕𝒓𝒐𝒍 𝑄𝑀𝐴𝑋 (total) 600pax

𝑄𝑀𝐼𝑁 (total) 200pax

𝑩𝒐𝒂𝒓𝒅𝒊𝒏𝒈 𝑄𝑀𝐴𝑋 (per filter) 180pax

𝑄𝑀𝐼𝑁 (per filter) 0pax



28

a simple formulation, trying to minimise the number of parameters that intervene,

described as follows:

𝑁𝑗 𝑑𝑒𝑠𝑖𝑟𝑒𝑑(𝑖 + 1) = (1) + (2)= (5.8)

= 𝑎𝑗[𝜆𝑗(𝑖 − 𝑑𝑗 + 1) + 𝜆𝑗(𝑖 − 𝑑𝑗) + 𝜆𝑗(𝑖 − 𝑑𝑗 − 1)] + 𝑏𝑗 (𝑄𝑗+1(𝑖))

If 𝑗=Check-in, the number of desired active check-in counters depends on the rate of

arrivals at the check-in facility, so-called arrivals term (1), and the queue at the security

control, so-called following queue term (2).

In order to consider the arrivals at the check-in, it is used the arrivals during three time

intervals (15min), prior to the interval of time for which the decision about the number

of counters is taken. The parameter 𝑑 could be interpreted as the degree of anticipation

and it is also influenced by the lead time to get an additional counter.

In short, if the rate of arrivals is high more active filters are desired. However, if the

following processing facility has a long queue, it is not convenient to add more load and

saturate it (due to the increase of the service rate in the early-in-the sequence facility).

Following this logic, 𝑏 parameter would be negative.

Parameters 𝑎𝑗 and 𝑏𝑗 weigh the importance of reducing the queue at the processing

facility 𝑗 (1) versus reducing the pressure to the facility 𝑗 + 1 (2). The parameters that

intervene in this policy need to be calibrated, according to the pattern of arrivals.

In this case, as opposed to the local policy, 𝑁𝑠𝑢𝑝𝑝𝑙𝑦 𝑙𝑖𝑛𝑒 are considered, in order to reduce

the bullwhip effect. In the local policy, it was not necessary because the amount of

additional resources at each time step was limited. Another way to reduce the

oscillations of the demand of additional resources could be including a restoring term in

the formulation. For the sake of simplicity, it is done as follows:

𝑁𝑗 𝑒𝑥𝑡𝑟𝑎𝑜𝑟𝑑𝑒𝑟(𝑖 + 1) = 𝑁𝑗 𝑑𝑒𝑠𝑖𝑟𝑒𝑑(𝑖 + 1) − (𝑁𝑗 𝑎𝑐𝑡𝑖𝑣𝑒(𝑖) + 𝑁𝑠𝑢𝑝𝑝𝑙𝑦 𝑙𝑖𝑛𝑒(𝑖))

(5.9)

Lead times are considered as described in Section 5.4.1.

To model the boarding facility, it is used the same approach as in Section 5.4.1, because it is mainly governed by airside constraints, more than in accordance to the landside operations.



29

6. NUMERICAL EXPERIMENTATION In this section, some experiments are carried out to illustrate the system dynamics and the impact of the constraints of one processing facility on the performance of the overall system. Then there is a comparison of the performance of the local and holistic policies.

6.1. Landside operational characteristics of the airport terminal The parameters in Table 5, apply to a medium-size airport:

Table 5. Operational characteristics for a medium size airport

*scanner: people-screening metal detector

For purposes of simplicity, it is assumed that check-in and boarding facilities do not share any worker.

6.2. Base Case Scenario The base case scenario aims to test the system for a medium load and to illustrate the system dynamics and the operation of the model. As input, the following pattern is used:

First, a low-medium load (18 operations/hour) and then a medium load (24

operations/hour), according to the above-mentioned operational characteristics. The

load pattern is uniform, due to all flights being short haul (Vueling-type). The flight

schedule is shown in Appendix A.

As a result, Figure 11 shows the estimated arrivals at the check-in, in 5min time intervals:

Operational characteristics

Check-in c per filter ( 𝜇𝑀𝐴𝑋 𝑖𝑛𝑑) 0.8pax/min

𝑡𝑠 75s

Number of filters 140 counter

Agents per filter 1 agent

Security control c per filter ( 𝜇𝑀𝐴𝑋 𝑖𝑛𝑑) 8pax/min

𝑡𝑠 7.5s

Number of filters 10 scanners*

Agents per filter 4 agents

Boarding c per filter ( 𝜇𝑀𝐴𝑋 𝑖𝑛𝑑) 9pax/min

𝑡𝑠 6.7s

Number of filters 20 gates (for departures)

Agents per filter 2 agents

Average walking time S

From Check-in to Security control 2.5min

From Security control to Boarding 10min



30

Figure 11. Estimated arrivals at the check-in for Base Case Scenario

The rate of arrivals at the airport has a flat peak of 286pax/5min, for the medium load,

and 213pax/5min, for the low load. In terms of the system capacity:

Table 6. System load ratio for Base Case Scenario

The time interval from 9:45 to 16:00, using 5min time steps, is simulated. The following data aim for a better understanding of the model dynamics. It is necessary to warm up the system before 9:45 (done between the time intervals 1 to 38). From now on, as the hour is arbitrary, we will refer to the interval from 9:45 to 9:50 as interval 39.

6.2.1. System dynamics applying a local policy The local managing policy described in Section 5.4.1 is applied. Below, the first graph explains why the arrival curve is not laminated as it advances through the system, from one processing facility to the following-in-the-sequence facility. The second graph illustrates the operation of one processing facility (managing policy criteria, supply line, etc).

200

210

220

230

240

250

260

270

280

290

30009:4

0

10:3

5

11:3

0

12:2

5

13:2

0

14:1

5

15:1

0

pax

System load Low load Medium load Max capacity

Check-in 38% 51% 560/5min

Security control 53% 72% 400pax/5min

Boarding 24% 32% 900pax/5min



31

Figure 12. Arrivals at the different facilities for Base Scenario.. Local policy, with QMAX (check-in)=12pax

Figure 12 shows the arrivals at each processing facility. In this case, the arrival curves is

not laminated by the previous processing facility. On the contrary, due to the criteria of

the policy, the previous filter increases the peak of arrivals for the following facility. It

happens because there is a flat peak of arrivals to the first filter, smaller than its

maximum capacity. Then, when the arrivals at the check-in increase, the manager waits

until reaching a certain queue per filter until deciding to open additional counters. As

the queue keeps growing, more additional counters are opened and they work at full

capacity, because there is queue. Therefore, at a certain point, the departure rate from

the check-in facility exceeds the maximum rate of the arrivals.

This phenomenon is repeated at the security control facility. Therefore, the arrival curve

at the boarding facility has a higher peak, with shorter time span, compared to the

security control facility.

In the first part of the graph, it is clear that applying the equilibrium condition (Eq. 5.3),

during the warm up interval, the filters do not constraint the flow of passengers. It leads

to zero queue at the beginning of the analysed time interval.

Finally, the arrival rates at security control and boarding tend to stabilize around the

arrival rate at the check-in, if it remains constant for enough time. The managing policies

work as expected.

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100

pax

/min

Time interval

Arrivals

Check-in

Security Control

Boarding



32

Figure 13. Operation of the check-in facility. Primary y-axis: number of active counters. Secondary y-axis: Queue (pax per filter), lead time for the additional counter ordered in the

previous node (min), N Supply line (units) and thresholds (pax).

Figure 13 illustrates the operation of the check-in facility. When the queue per filter exceeds the threshold set by the manager (e.g. 12 pax), additional counters are ordered. As the utilization rate is quite low, there are sufficient resources available and the lead time is low. It is quite reactive and the number of orders in the supply line is low. This figure shows one simulation, not the average of several simulations, in order to capture easily the randomness of the lead time. For example, at the interval 61 there are two additional resources in the “supply line”, because both additional counters arrive more than 2.5min later than when they ordered (at the end of the interval 60).

6.2.2. Impact of the constraints of one processing facility on the performance of the overall system Table 7 shows the effect of changing the managing policy at the first processing facility (modification of parameter QMAX).

0

2

4

6

8

10

12

14

16

0

10

20

30

40

50

60

70

80

90

100

1 21 41 61 81 101

Time interval

Active counters

N Supply line

Queue (per filter)

Threshold MAX

Threshold MIN

Lead time ExtraCounter 1

Lead time ExtraCounter 2



33

Table 7. Operational performance indicators when changing the managing policy at the first processing facility. A: check-in; B: security control; C: Boarding

Local policy with QMAX:

𝑤𝑡𝐴

(min) 𝑤𝑡𝐵

(min) 𝑤𝑡𝐶

(min)

Average active counters

Average active security scanners

12 9,1 min 4,3min 0,3min 67,7 6,76 10 7,5 4,0 0,4 67,9 6,84 8 6,0 3,6 0,4 67,8 6,85 6 4,9 3,1 0,4 67,8 6,87

The reduction in waiting times at the check-in facility is a direct consequence of

decreasing the value chosen for the QMAX parameter. At the check-in, one could interpret

it as the queue non-desirable to exceed or, in terms used in supply chain management,

as the stock of people aimed. A queue of 6 people per filter means a waiting time of

7.5min (service rate of 0.8pax/min). However, as the system load is not high, the average

waiting time is lower.

Secondly, it is important to notice that modifying the operation of the check-in facility

has an impact on the following facility (security control). The waiting times at security

control are reduced too, although there are no changes in its managing policy. However,

the decrease of the waiting time at security control is due to the change in the arrival

curve at the facility and a small increase in resources used. This is shown in Figure 12.

Figure 14. Arrivals at the different facilities for Base Scenario. Local policy, with QMAX (check-in)=6pax

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100

pax

/min

Time interval

Arrivals

Check-in

Security Control

Boarding



34

Comparing Figure 12 and Figure 14, it can be seen that with a faster or more reactive

managing policy at check-in (QMAX smaller), the peak of arrivals at security check is

shifted to the left and lasts less time. Additionally, the system becomes stationary

earlier.

According to Table 7, the amount of resources spent (number of filters opened at each

facility) does not vary significantly. Thus, for medium load, the whole system benefits

from a more reactive policy at the first processing facility.

In Figure 15, it can be noted how the policy with QMAX (check-in)=6pax anticipates the

policy with QMAX (check-in)=12pax, but the area below each graph is almost equal. The

area is proportional to the resources spent.

Figure 15. Comparison of resources spent with QMAX (check-in)=6pax and QMAX (check-in)=12pax

The boarding facility is less interesting to analyse because the managing policies are less sensitive. The managing decisions in the early-in-the-sequence processing facility also affect the boarding facility. However, the capacity of the boarding facility is quite high compared to the previous ones and, thus, for medium load, there is no variations for resources allocated.

6.3. Peak Scenario The peak scenario aims to test the system under more demanding conditions. The charter flights cause a peak, due to their arrival pattern and departure density. The flight pattern used as input for Base Case Scenario is modified adding 12 charter flights in a

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100

pax

/min

Time interval

Resources allocation (processing capacity)

Check-in capacity Q12

Security control capacity Q12





35

15min interval, replacing 6 short haul flights. The flight schedule used as input is shown in Appendix B. As a result, Figure 16 shows the estimated arrivals at the check-in, in 5min time intervals:

Figure 16. Estimated arrivals at the check-in for Peak Scenario

The rate of arrivals at the airport has a maximum of 531pax/5min. In terms of the system

capacity:

Table 8. System load ratio for Peak Scenario

Operational performance indicators are based on 375min of simulation, from 9:45 to 16:00, using 5min time steps. From now on, as the hour is arbitrary, we will refer to the interval from 9:45 to 9:50 as interval 39.

200

250

300

350

400

450

500

550

09:4

0

10:3

5

11:3

0

12:2

5

13:2

0

14:1

5

15:1

0

pax

System load Peak Max capacity

Check-in 95% 560pax/5min

Security control 133% 400pax/5min

Boarding 59% 900pax/5min



36

6.3.1. Results applying a local policy Table 9 summarizes the operation of the system, applying a local policy.

Table 9. Operational performance indicators when changing the managing policy at the first processing facility. A: check-in; B: security control; C: Boarding

Local policy with QMAX:

𝑤𝑡𝐴

(min) 𝑤𝑡𝐵

(min) 𝑤𝑡𝐶

(min)

Average active

counters

Average active security scanners

WA WB

12 12,6 5,1 1,2 75,8 7,36 1,01 1,10 10 11,3 5,2 1,0 77,0 7,37 1,04 1,10 8 9,7 5,0 0,6 78,4 7,32 1,06 1,09 6 7,8 4,8 0,5 80,3 7,31 1,09 1,10

In the Peak Scenario, there is lamination of the arrivals from processing facility A to

processing facility B, as opposed to Base scenario (explanative graphs in Appendix B).

When increasing the QMAX parameter (more restrictive managing policy at check-in), the

operation of the security control benefits from it. The waiting time and the resources

spent at the security control facility are reduced. Again, the impact of the managing

policy at the early-in-the-sequence facility on the operation of the following processing

facility is clearly observed.

Cost of applying above mentioned policies is calculated according to the objective

function describe in section 4.2. The results are shown in Table 10.

Table 10. Total cost of different local policies. Peak Scenario

Local policy with QMAX: 𝐶𝑝 (EUR) 𝐶𝑟 (EUR) 𝐶 (EUR)

12 411188 9246 420435 10 380730 9360 390090 8 332867 9486 342353 6 285004 9663 294667

6.3.2. Results applying a holistic policy In order to evaluate the performance of a holistic policy, several trials are done varying the parameters that intervene in it. They are summarized as follows:



37

Table 11. Parameter ranges for holistic policy

Parameter Min Max

𝑎𝐴 0,3 0,7 𝑏𝐴 -0,18 0,02 𝑑𝐴 1 5 𝑎𝐵 0,03 0,07 𝑏𝐵 -0,018 0,002 𝑑𝐵 1 5

The performance is evaluated using the objective function C. However, some constraints

are also applied when optimising. The results are summarized in Table 12, for an

operation time of 375min.

Table 12. Total cost of best holistic policies. Peak Scenario

Scenario Constraint Best performing parameter set

𝐶𝑝

(EUR)

𝐶𝑟 (EUR)

𝐶 (EUR)

1st None

Holistic 1 40423 12396 52819

2nd Max number of aggregated resources*(1)

Holistic 2 61328 11245 72573

3rd Max number of resources at each processing facility*(2)

Holistic 3 75280 11178 86457

*(1)maximum number of aggregated resources used equal to resources used applying QMAX=12

(see table 9).*(2)maximum number of check-in resources/security control resources/boarding

resources used equal to resources used applying QMAX=12 (see table 9).*(3)standarized number of

passengers processed is used to calculate total passenger cost in order to compare policies

The best system managing policy is a trade-off between resources spent and passenger

waiting times. With no restrictions, it tends to spend more on resources than in the

other two scenarios, as the savings on total passenger cost are higher.

The second scenario shows that, the system performs better if the system is managed

as a whole rather than if resource restrictions are imposed at local level (third scenario);

although total expenditure of resources is the same. In this case, it is positive to invest

slightly more on security control resources and slightly less on check-in resources.

In Appendix B, data about system operation and resources distribution over time are

shown. Several takeaways may be extracted from these graphs. For instance, using the

best holistic policy with no constraints, processing capacity at check-in is adjusted faster

to arrival variations. Thus, the gap between the check-in arrivals curve and the security

control arrivals curve is smaller; likewise, the queue length. Table 13 summarizes the

system operation.



38

Table 13. Operational performance indicators for different holistic policies

Parameter set

Average waiting time A (min)

Average waiting time B (min)

Average waiting time C (min)

Average active

counters

Average active

security scanners

WA WB

Holistic 1 1,1 0,3 0,4 84,6 8,04 0,83 0,69 Holistic 2 1,8 0,7 0,3 73,8 7,68 0,85 0,73 Holistic 3 1,5 1,8 0,2 74,5 7,31 0,86 0,73

6.3.2.1. Parameters interpretation In this section, parameter sets resulting from optimisation are discussed, according to the managing policy described in section 5.4.2.

Table 14. Parameter values of best holistic policies

Parameter set

aA bA dA aB bB dB

Holistic 1 0,542 -0,168 4 0,052 0,000 2 Holistic 2 0,443 -0,026 0 0,049 0,002 0 Holistic 3 0,477 -0,051 2 0,046 0,001 1

In order to make the coefficients more self-explanatory, they are scaled as follows:

𝜇𝑀𝐴𝑋 𝑗 𝑑𝑒𝑠𝑖𝑟𝑒𝑑(𝑖 + 1) = (1) + (2) = (6.1)

= 𝑎𝑗′ [

𝜆𝑗(𝑖 − 𝑑𝑗 + 1) + 𝜆𝑗(𝑖 − 𝑑𝑗) + 𝜆𝑗(𝑖 − 𝑑𝑗 − 1)

3] + 𝑏𝑗

′ (𝑄𝑗+1(𝑖)

5)

With this transformation, if 𝑎𝑗′ is equal to 1, it means that a processing capacity

(pax/min) equal to average arrival rate is desired. When the second term is included

(negative), 𝑎𝑗′ should increase. The queue length is divided by interval duration, trying

to standardise the weight of both addends in terms of pax/min. Therefore, a rough initial

guess for 𝑎𝑗′ and 𝑏𝑗

′ could be done in the following way:

𝑎𝑗′ + 𝑏𝑗

′~1 and arrivals term (1) is more important than following queue term (2) at

the next step (e.g. 𝑎𝑗′=1.3 and 𝑏𝑗

′=-0.3).

Comparing Eq. 6.1 and Eq. 5.8 and according to 𝜇𝑀𝐴𝑋 𝑖𝑛𝑑 𝐴 = 0.8𝑝𝑎𝑥/𝑚𝑖𝑛 and

𝜇𝑀𝐴𝑋 𝑖𝑛𝑑 𝐴 = 8𝑝𝑎𝑥/𝑚𝑖𝑛:

𝑎𝐴 =1

3 · 0.8𝑎𝐴

′; 𝑏𝐴 =1

5 · 0.8𝑏𝐴

′

(6.2)

𝑎𝐵 =1

3 · 8𝑎𝐵

′; 𝑏𝐵 =1

5 · 8𝑏𝐵

′

(6.3)



39

From Table 14, the bA parameter is different from zero (and negative as expected). Then, the

queue length at the following in the sequence facility is an important indicator to get the

best overall system performance. Notice that, since processing capacity at boarding

facility and it is far from saturation, its queue does not have an impact on managing

policy for security control. Thus, bB is very small compared to aB (after trying several different

patterns of load, bB should be very close to zero and negative).

The policy that gives more importance to the queue at security filter, for deciding the resources at check-in facility, is Holistic 1. All Holistic 1, Holistic 2 and Holistic 3 have 𝑎𝐵

′>1 and 𝑏𝐵′0. Meaning that the policy orders a higher processing capacity (pax/min)

than the arrival rate observed a certain amount of time before (given by dB).

6.3.3. Comparison between local and holistic policies and scenario conclusions Resource allocation policies for a processing facility that take into account the impact

on the said facility but also on the following in the sequence facility perform better than

local managing policies. It has been proven that in best performing policies the so-called

following queue term is important. When queue in the following in the sequence facility

grows, this term inhibits the increase of processing capacity, in order not to add more

pressure on the following subsystem.

The total cost (C) of the system is reduced more than 80% using a holistic policy with no

restrictions, compared to local managing policies. Due to the nature of the aggregated

objective function (C), with high value of passenger waiting time, the optimal operation

of the system is achieved with a higher resource expenditure than in the local policies

analysed. Additionally, 2nd and 3rd scenarios could be interpreted as a sensitivity

analysis. With exactly the same expenditure of resources, passenger waiting times are

significantly lower than using a local policy. Then, the total cost (C) applying these

holistic policies is also considerably lower than using the related local policy,

independently of monetisation parameters (𝑐𝑝and 𝑐𝑟).

It is important to note that these cost reductions are the potential improvements for an

airport system where the airport authority assigns the check-in counters in a centralized

manner, with shared check-in for all passengers. In European airports, current managing

constraints would significantly reduce the room for improvement.

Moreover, the goodness of fit of processing facilities capacity to its demand (arrivals rate) is better when applying a holistic view. The bullwhip effect indicator W is lower for holistic policies than local ones. While the bullwhip effect is increased in the following facility, with a local, departmentalised policy. On the contrary, a holistic or systemic management reduces the fluctuations of the processing capacity at the following facility compared to its arrivals rate. This last point, together with the impact on it of considering the resources in the supply line, needs further investigation under different conditions.



40

7. CONCLUSIONS AND FUTURE RESEARCH

7.1. Conclusions Firstly, it is important to be aware of the need of analytical approaches to airport landside system; in order to bridge the gap between handbook formulas and object –oriented simulations, usually applied to address this issue. Analytical models are very useful to understand system dynamics. In this line, the model presented in this work is relevant in the way that it provides a comprehensible formulation to evaluate system performance for different resource management policies. Additionally, the bottom-up methodology allows understanding of multiple aspects that intervene in the system. Secondly, results show the impact of service rate of a facility on the following-in-the-sequence facility. Overall performance of landside system is better when processing facilities are managed with a holistic view, rather than using policies based on departmentalization. A holistic policy for resource management of a facility that takes into account the queue in the following one improves overall resource allocation. Results are in accordance to supply chain management previous experience and the bullwhip effect indicator W results also point in the direction that holistic management is beneficial; in this case, reducing fluctuation of resources along the chain. However, it should be further analysed with a security check with a less restrictive maximum processing capacity. The reduction in landside system total cost applying a holistic policy is high; even when resource expenditure is higher than for local policies. It highlights the high value that air transport organisations (e.g. Eurocontrol) give to passenger waiting time. Moreover, it is good to note that these cost reductions are the potential improvements for an airport system where all processing facilities operate in a centralized manner (e.g. shared check-in for all passengers). In European airports, constraints due to handling companies operation would significantly reduce the room for improvement. To summarize, this analytical model of the whole landside system (embarking direction) is valuable to highlight the benefits of a coordinated management of processing facilities, especially check-in and security control facilities. It encourages implementing a systemic and collaborative view to landside operation, with the same spirit as A-CDM.

7.2. Future lines of research Analytical models are very useful in reaching a better understanding of airport landside system dynamics. Modular approach to model the system allows clear differentiation of new lines of research. First, no distant check-in is one of the main assumptions. The influence of a mix of passengers that use conventional counters and passengers that do distant check-in should be investigated. Distant check-in passengers put more pressure on security control facility, as these passengers arrive in a more concentrated way (peak).



41

On the other hand, ancillary facilities’ impact on passenger flow between processing facilities should be further analysed. It is expected that they will reshape passenger arrival curves; generally, laminating it. Another important point is evaluation of savings thank to holistic resource allocation policies. The model aims to show the impact of holistic policies on system performance rather than a precise cost estimation. According to that, the system models a standard medium-size airport. However, its application to a real airport could be a next step. It would be interesting to organise more workshops with practitioners to adjust the submodels to estimate departing passenger arrivals, resources lead time to get and passenger flow between facilities. In this line, further research may be done about value of time. Some studies propose a higher penalization for the waiting time that exceeds passenger expectations (Frank, 2001). Finally, related to the previous points, testing holistic policies under multiple real conditions would allow finding a robust resource management policy suitable for a specific airport and its load patterns.



42

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A1

APPENDICES

APPENDIX A. Flight Schedules

Base Case Scenario. Flight schedule pattern. All the flights are Vueling-type (occupancy

of 80%).

Type of flight

SDT

VLG 10:00:00

VLG 10:00:00

VLG 10:05:00

VLG 10:10:00

VLG 10:10:00

VLG 10:15:00

… …

VLG 12:00:00

VLG 12:00:00

VLG 12:05:00

VLG 12:05:00

VLG 12:10:00

VLG 12:10:00

… …



A2

Peak Scenario. Flight schedule pattern. Vueling-type fligths (occupancy of 80%) and

chárter-type flights (occupancy of 90%)

Type of flight

SDT

VLG 10:00:00

VLG 10:00:00

VLG 10:05:00

VLG 10:10:00

VLG 10:10:00

VLG 10:15:00

… …

VLG 12:00:00

VLG 12:00:00

VLG 12:05:00

VLG 12:05:00

… …

VLG 14:25:00

VLG 14:25:00

Charter 14:30:00

Charter 14:30:00

Charter 14:30:00

Charter 14:30:00

Charter 14:35:00

Charter 14:35:00

Charter 14:35:00

Charter 14:35:00

Charter 14:40:00

Charter 14:40:00

Charter 14:40:00

Charter 14:40:00

VLG 14:45:00

VLG 14:45:00

… …



A3

APPENDIX B.Additional data analysis for Peak Scenario

Lamination of the arrival peak from check-in to security control facility. Arrivals at the different facilities. Local policy, with QMAX (check-in)=6pax

0

20

40

60

80

100

120

0 20 40 60 80 100

pax

/min

Time interval

Arrivals

Check-in

Security Control

Boarding



A4

Lamination of the arrival peak from check-in to security control facility. Arrivals at the different facilities. Local policy, with QMAX (check-in)=12pax

0

20

40

60

80

100

120

0 20 40 60 80 100

pax

/min

Time interval

Arrivals

Check-in

Security Control

Boarding



A5

Comparison of resources spent with QMAX (check-in)=6pax and QMAX (check-in)=12pax

Arrivals at consecutive facilities, according to the chosen managing policy. Policy: Holistic 1

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100

pax

/min

Time interval






0

20

40

60

80

100

120

0 20 40 60 80 100

pax

/min

Time interval

Arrivals

Check-in

Security Control

Boarding



A6


0

20

40

60

80

100

120

0 20 40 60 80 100

pax

/min

Time interval

Arrivals

Check-in

Security Control

Boarding



A7


0

20

40

60

80

100

120

0 20 40 60 80 100

pax

/min

Time interval

Arrivals

Check-in

Security Control

Boarding



A8

Comparison of resources spent with QMAX (check-in)=6pax and QMAX (check-in)=12pax

0

20

40

60

80

100

120

0 20 40 60 80 100

pax

/min

Time interval


Check-in Set 2

Security control Set 2

Check-in Set 1

Security control Set 1

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