Airside Optimization Framework Covering Multiple Operations in Civil Airport Systems with a Variety of Aircraft: A Simulation-Based Digital Twin

Citation: Attar, A.; Babaee, M.; Raissi, S.; Nojavan, M. Airside Optimization Framework Covering Multiple Operations in Civil Airport Systems with a Variety of Aircraft: A Simulation-Based Digital Twin. Systems 2024,12, 394. https:// doi.org/10.3390/systems12100394 Academic Editors: Philippe Mathieu, Juan M. Corchado, Fernando De la Prieta Pintado and Alfonso González-Briones Received: 28 August 2024 Revised: 20 September 2024 Accepted: 24 September 2024 Published: 26 September 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). systems Article Airside Optimization Framework Covering Multiple Operations in Civil Airport Systems with a Variety of Aircraft: A Simulation-Based Digital Twin Ahmad Attar 1,2,* , Mahdi Babaee 3, Sadigh Raissi 3and Majid Nojavan 3 1Department of Engineering, University of Exeter, Exeter EX4 4QF, UK 2Exeter Digital Enterprise Systems (ExDES) Laboratory, University of Exeter, Exeter EX4 4QF, UK 3Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran 11518-63411, Iran; [email protected] (M.B.); [email protected] (S.R.); [email protected] (M.N.) *Correspondence: [email protected]; Tel.: +44-1392-661000 Abstract: The airside is a principal subsystem in the intricate airport systems. This study focuses on introducing a digital twin framework for analyzing the delays and capacity of airports. This framework encompasses a diverse array of authentic features pertaining to a civil airport for a mixture of both landing and departing flights. Being a decision support for the management of international airports, all sizes and weight categories of aircraft are considered permissible, each with their own unique service time and speed requirements in accordance with the global aviation regulations. The proposed discrete event simulation digital twin provides a real-time demonstration of the system performance with the possibility of predicting the future outcomes of managerial decisions. Additionally, this twin is equipped with an advanced and realistic 3D visualization that facilitates a more comprehensive understanding of the ongoing operations. To assess its efficiency in practice, the framework was implemented at an international airport. The statistical tests revealed the superior similarity between the proposed twin and the real system. Using this twin, we further optimized the studied system by analyzing its projected future performance under a set of scenarios. This resulted in a nearly 30% upgrade in the capacity of this airport while decreasing the expected delays by over 18% annually. Keywords: digital twin; airport modeling; discrete event simulation; airfield optimization; runway capacity; airside mixed operations; 3D twin; Enterprise Dynamics 1. Introduction Optimizing complex systems and monitoring their performance pose convoluted challenges for system managers [ 1 , 2 ]. These tasks involve trade-offs, ensuring reliability, and adapting to changing conditions. Whether it is fine-tuning algorithms, managing resources, or analyzing data, system managers require advanced and up-to-date decision support tools to play their critical role in maintaining efficient and robust systems. Airports are one of such complex systems that are the most strategic component in the aviation industry. Several intricate subsystems with a multitude of events and operations are present in an airport that can be categorized in two major groups: landside and airside [3–5] . Here, the landside is mainly the segment of this system that deals with the passengers’ interactions with the airport facilities [ 6 ], while the airside concentrates on the operations pertaining to the aircraft on the airport’s ground as well as their movement within the airport’s airspace [3–5,7]. A comprehensive, real-time information base, known as a digital twin, offers more precise insights for strategic decision making and enhances the design, planning, and management processes of airport infrastructure [ 8 ]. In general, computer simulation Systems 2024,12, 394. https://doi.org/10.3390/systems12100394 https://www.mdpi.com/journal/systems Systems 2024,12, 394 2 of 18 models have long been interesting decision-support tools for airport managers for both airside and landside. For instance, Pérez et al. [ 9 ], Scozzaro et al. [ 10 ], and Derek et al. [ 11 ] utilized simulation for optimizing the staff schedules and allocations in the landside of the airport. Here, Pérez et al. [ 9 ] offered a discrete event simulation model for the terminal checkpoints to improve the shift allocation of security screening resources. They concluded that to improve the passenger cycle time of the studied airport, additional security officers are required, for which they offered two improvement strategies, namely reactive and proactive. Manataki and Zografos [ 12 ] is another study that explored the use of simulation methods in the landside of airport systems. They applied the system dynamics simulation method to model the performance of the terminals, considering multiple stakeholders and a variety of services, including ticketing, check-in, boarding pass control, passport control, security screening, customs control, baggage claim, and ancillary services. Their model was illustrated using some sample data from Athens International Airport. Very recently, in a theoretical attempt, Uyar and Gürsel [ 13 ] discussed a discrete event simulation (DES) model using SimPy for the landside to address the passenger flow and waiting time at the security checkpoints, check-in stage, and boarding points. On the other hand, focusing on the strategic planning of the airside, Chen et al. [ 14 ] proposed a simulation model for Shanghai Hongqiao International Airport (SHA). They built their simulation model on the ProModel platform and used it to analyze different scheduling scenarios to find the bottleneck in the system. The results presented in [ 14 ] suggest that if the demand increases from its baseline scenario, a drastic increment in the arrival and departure queue lengths can be anticipated at SHA airport. Özdemir et al. [ 15 ] studied the airside from the taxiway perspective. They proposed a simulation model for ˙ Istanbul Atatürk Airport using SIMMOD and explored the effects of adding a set of new taxiways. Their proposed changes in the infrastructure included various combinations of end-around and rapid-exit taxiways. The researchers concluded that switching to end-around and rapid-exit taxiways can decrease the taxi time by around 52% and 10%, respectively. Offering a similar DES model in SIMMOD for the taxiway system of the airside, Dönmez et al. [ 16 ] investigated the influence of the International Civil Aviation Organization’s (ICAO) taxiway system development stages on the delays and runway capacity in a single-runway airport. Their case was Turkey’s Samsun Çar¸samba Airport (LTFH), which reportedly has a basic level of taxiway layout. In their simulation model, however, they completely disregarded the capacities of other components of the airside (e.g., holding queue, apron). A DES model was also proposed by Feng and Johnson [ 17 ] using the ARENA simulation platform, explicitly for analyzing the end-around taxiways. Their configurations were partially inspired by the Dallas/Fort Worth International Airport. Lai et al. [ 18 ] proposed a conceptual DES model in Simul8 simulation software for some parts of the airside in New York City’s John F. Kennedy Airport (JFK) to analyze the potential bottlenecks and congestions in the taxiway utilization. In general, existing studies either modeled the airside partially or put their main focus on scheduling of arrivals and departure events. This keeps them far from what managers would consider a practical decision support tool [ 19 ]. To address this gap, a few conceptual frameworks were proposed by Zografos and Madas [ 19 ] and Oliveira [ 8 ] to help managers analyze the system using various performance measures. In the most recent attempt in 2020, Oliveira [ 8 ] investigated the benefits of developing a digital twin for the airports. Being an experienced airport project manager and infrastructure expansion consultant herself, the author sees today’s airports as cyber-physical systems for which building information modeling (BIM) technology and 3D visualizations are essential for facilitating proper decision making. Thus, in this study, we aim to introduce a practical digital twin that embraces all elements of the airside (e.g., holding stack, glide path, runway, taxiway, apron) in a realistic manner. This twin mimics the real behavior of the system by applying the rules and Systems 2024,12, 394 3 of 18 standards normally applied by airport crew and operation teams in accordance with the global authorities’ mandatory regulations. Such a digital twin can not only be used to assess the performance of the system under given scheduling scenarios, but it can also predict the effect of strategic decisions on the nominal capacity of the airside. The nominal capacity is the upper bound for the airside infrastructures, and thus having a realistic estimation of that is mandatory for the organization’s stakeholders to avoid overselling capacity to airlines [ 20 ]. However, such realistic estimations are generally overlooked, and the dominant approaches are the rough analytical methods [21,22]. Therefore, another contribution of the current study is to investigate the implementation of the proposed framework in an international airport case study seeking the optimization of its nominal capacity under sequential landings and departures. Furthermore, to address the above-mentioned need for a visual representation of the system, a realistic 3D visualization is also proposed for this novel framework. Hence, the contributions of the current study are five-fold: (i) presenting a holistic picture of the airside encompassing every component of this sector of the airport, (ii) proposing a fully functional digital twin framework in addition to the conceptual representation of the process, (iii) examining the prospective nominal capacity of the system apart from any shortcomings in flight schedules for strategic planning of facilities, (iv) providing both a 2D and 3D representation of the system for monitoring purposes, and (v) implementing the framework in a real airport to assess its performance in practice. The rest of this paper is organized as follows: The system under study is described in Section 2. The proposed simulation method and digital twin is presented in Section 3. This section also includes the used aviation rules and assumptions. Section 4presents the results of this study applied to an international airport as a practical case study, both for the validation phase and the optimized scenarios. In Section 5, the results are discussed using the defined performance indicators. Some concluding remarks and future research directions are provided in the last section. 2. System Description In this section, we provide a brief description of the studied airport system. Here, first, we explore the parts and components of the airside, and then the necessary performance indicators and terms such as airport capacity, capacity of the apron area, and what is considered a delay for the visiting aircraft are defined. Table 1lists the important symbols, notations, and abbreviations used in this paper. Table 1. Notations and symbols. Symbol Description Symbol Description ϕLength of the common approach path, ROTiRunway occupancy time of aircraft i, δij Minimum permissible distance separation between two arriving aircraft (leading aircraft iand trailing aircraft j) anywhere along the common glide path, MTSij Minimum time separation between leading aircraft iand trailing aircraft j, TBAHi Time between arrival to holding stack for aircraft i , ViApproach speed of the leading aircraft i,AOTiApron occupancy time of aircraft i, VjApproach speed of the trailing aircraft j,TOTiTaxiway occupancy time of aircraft i, µMean of normal distribution, βScale parameter of the distribution, σStd. deviation of Normal distribution, θThreshold parameter of the distribution, αShape parameter of the distribution, γLocation parameter of the distribution. 2.1. Airside Components The principal components of the airside consist of runway, taxiway system, and apron area/gates. These components are illustrated in a simplified sketch of the surveyed airside in Figure 1. In general, according to [ 21 – 23 ], the airside system may be divided into the following seven main parts that will all be considered in our digital twin: Systems 2024,12, 394 4 of 18 (1) Holding stack: Holding (or flying a hold), in aviation, is a maneuver designed to delay an aircraft already in flight while keeping it within a specified airspace. A standard holding pattern can be seen in Figure 1. (2) Approach gate: It is an entry part beyond which the pilot cannot alter speed and direction (without a command or permission from the control tower). (3) Glide path: it is where all aircraft are sequenced individually under rules of minimum distance separation. (4) Final approach: A zone on the approach and in a specified distance from the runway threshold. Once an aircraft enters this zone on its landing operation, no other airplanes are allowed to enter the runway from the departure queue. In other words, despite the runway being empty, it is considered to be locked/occupied by the landing airplane until after its landing operation is complete. (5) Runway: A runway is a “distinct rectangular area on a land aerodrome prepared for the landing and takeoff of aircraft” [ 21 ]. Runways may be a man-made surface (often asphalt, concrete, or a mixture of both) or a natural one (grass, dirt, gravel, ice, or salt). As a dominant rule in this industry, each runway may be occupied only by one aircraft at a time. (6) Taxiway: A walkway made by asphalt, concrete, gravel, or grass that links the runways to the terminals, ramps, and other airport infrastructure assets [ 24 ]. Design of this part of the airport and proper management of the taxiways significantly influence the overall capacity of the runway [15,16,25,26]. (7) Apron: This area is where aircraft are parked. It also hosts multiple operations such as unloading, loading, etc. [ 27 , 28 ] In order to facilitate cooperation amongst users, an apron management service or advisory may exert control over the apron [28,29]. Systems 2024, 12, x FOR PEER REVIEW 4 of 18 2.1. Airside Components The principal components of the airside consist of runway, taxiway system, and apron area/gates. These components are illustrated in a simplified sketch of the surveyed airside in Figure 1. In general, according to [21–23], the airside system may be divided into the following seven main parts that will all be considered in our digital twin: (1) Holding stack: Holding (or flying a hold), in aviation, is a maneuver designed to delay an aircraft already in flight while keeping it within a specified airspace. A standard holding pattern can be seen in Figure 1. (2) Approach gate: It is an entry part beyond which the pilot cannot alter speed and direction (without a command or permission from the control tower). (3) Glide path: it is where all aircraft are sequenced individually under rules of minimum distance separation. (4) Final approach: A zone on the approach and in a specified distance from the runway threshold. Once an aircraft enters this zone on its landing operation, no other airplanes are allowed to enter the runway from the departure queue. In other words, despite the runway being empty, it is considered to be locked/occupied by the landing airplane until after its landing operation is complete. (5) Runway: A runway is a “distinct rectangular area on a land aerodrome prepared for the landing and takeoff of aircraft” [21]. Runways may be a man-made surface (often asphalt, concrete, or a mixture of both) or a natural one (grass, dirt, gravel, ice, or salt). As a dominant rule in this industry, each runway may be occupied only by one aircraft at a time. (6) Taxiway: A walkway made by asphalt, concrete, gravel, or grass that links the runways to the terminals, ramps, and other airport infrastructure assets [24]. Design of this part of the airport and proper management of the taxiways significantly influence the overall capacity of the runway [15,16,25,26]. (7) Apron: This area is where aircraft are parked. It also hosts multiple operations such as unloading, loading, etc. [27,28] In order to facilitate cooperation amongst users, an apron management service or advisory may exert control over the apron [28,29]. Figure 1. A schematic sketch of airside components along with a graph of landing separation distance calculation in the common glide path. Here, 𝑉  and 𝑉  denote the aircraft approach speed, Figure 1. A schematic sketch of airside components along with a graph of landing separation distance calculation in the common glide path. Here, Vi and Vj denote the aircraft approach speed, Tij is the minimum time separation at the entry gate, and δij is the minimum admissible distance separation for two arriving aircraft, i.e., a leading aircraft i(red line) and a following aircraft j(green line). 2.2. Airport Capacity In an airport, the term capacity is the physical capability of the airport system to handle and accommodate multiple or mixed operations (arriving and departing of air- Systems 2024,12, 394 5 of 18 craft) for a specific period of time. This performance indicator also includes the ability to accommodate passengers on the landside [ 21 , 22 , 30 ]. Airport capacity estimations and analyses are generally performed separately for its landside and airside segments. The airport’s landside includes the terminal buildings in order to provide various services to passengers and ground access systems. Airside facilities, on the other hand, consist of the runways, taxiways, apron area, and the parking positions. Although the runway is the most important component in many airport systems to handle the mixed operations (i.e., landing and takeoff), there are also other factors that affect the ability of airports to carry out activities. These factors include weather conditions, air traffic control, specifications and condition of taxiway systems, apron area, and gate facilities [31]. 2.3. Apron Capacity The capacity of the apron area can be defined as the ability to accept and serve aircraft during a specified interval of time. Different factors may affect the gate/apron capacity— e.g., the number and type of stands, gate occupancy time for various aircraft (i.e., the mix of aircraft types), and the strategies and restrictions in place for apron gates [21,22]. 2.4. Delay In the airfield, delays and congestion happen when the demand is greater than the capacity. Based on the existing studies in air traffic flow management (ATFM), the delays that occur at the airport can be divided into two main categories: (i) airborne delays and (ii) ground delays [ 32 – 34 ]. There are intricate relationships among these delays, and a few studies looked at the possibility of transforming airborne delays into their ground counterparts (e.g., [ 34 ]). Arriving aircraft might also be delayed, ensuring the completion of the departure operation for another plane. For more details on airport delays, one may refer to [ 32 , 33 ]. Our proposed digital twin incorporates all of these delays and their interactions so as to provide a more comprehensive viewpoint of the airside system. 3. Simulation-Based Digital Twin The proposed simulation model covers all aforementioned components of the airside, from holding stacks and common glide path to runway, taxiway, apron area, and parking positions. The model also supports arrival and departure queues, mixed operations, and the safety rules used by the control tower to guarantee the minimum time separation for the safety of these operations in a space-time framework. In this section, we illustrate all aspects of the simulation-based digital twin and declare the inherent rules and assumptions in modeling such systems. Then, we propose a comprehensive discrete event simulation model and explain different steps of the aircraft’s journey in the digital model to demonstrate the level of similarity between the real system and the twin. 3.1. Attributes of the Aircraft The characteristics of the aircraft, as the main entity in this system, are among the most influential inputs to any digital twin in the aviation industry [ 35 ]. The separation rules of the Federal Aviation Administration (FAA) of the United States categorize aircraft as heavy, large, medium, and small, depending on their maximum takeoff weights [21,22,30] . These rules declare the minimum separation space rule required between aircraft in vertical, longitudinal, and lateral directions. This space depends on the aircraft type, speed, availability of radar facilities, navigational aids, and a few other factors such as the severity of wake vortices [ 21 ]. These separations are given in Table 2—both in the visual flight rule (VFR) and for the instrumental flight rule (IFR). For further information on the calculation basis of these values for each of these aircraft combinations, one may refer to [21,22,31]. Systems 2024,12, 394 6 of 18 Table 2. Minimum separation rule for consecutive arrivals and departures in VFR and IFR circumstances [21,22,31]. Sequence Trailing Aircraft Type Arrival–Arrival (Nautical Miles) Departure–Departure (Seconds) Maximum Takeoff Weight (in Tons) Leading Aircraft Type D C A&B D C A&B IFR VFR IFR VFR IFR VFR IFR VFR IFR VFR IFR VFR >300000 Heavy (D) 4 2.7 5 3.6 6 4.5 120 90 120 120 120 120 [12500, 300000] Large (C) 3 1.9 3 1.9 4 2.7 60 60 60 60 60 50 <12500 Small and Medium (A&B) 3 1.9 3 1.9 3 1.9 60 50 60 45 60 35 3.2. Control Tower Rules and Modeling Assumptions We aggregate the fundamental decision rules and theoretical assumptions from the literature (refer to [ 21 – 23 , 30 , 31 ]) with the findings of our field study in several international airports to provide a comprehensive and accurate analysis of landing and takeoff activities. The following assumptions and rules are considered in this paper: •The system operates 24 h a day and 365 days a year. • Landing events have preemption over departure operations. For all other cases (arrival– arrival or departure–departure events), only a FIFO (first-in, first-out) sequencing strategy is put in place with no preemptions. • Only one runway is in operation, and at any point in time, one aircraft can occupy the runway. • A departure operation may not be initiated if the subsequent arrival is less than a specified distance from the runway threshold, usually 2 nautical miles (NMI) in IFR conditions. • Successive departures are spaced at a minimum time separation equal to their departure service time from Table 2. As an assumption, the freedom of the tower controller to reorder the immediate parts of the departure sequence is disregarded. • To estimate the arrival–arrival minimum time separation over the approach path (i.e., MTS ) for each pair (i,j) of leading aircraft i and trailing aircraft j , the following rule from [21] is applied: MTSij =max(δij Vi +ϕ−δij·max 1 Vi −1 Vj!, 0!,ROTi)+Buffer Time, (1) where δij is the minimum permissible distance separation between the two arriving aircraft anywhere along the common glide path from Table 2, ϕ is the length of the common approach path, and ROTi is the runway occupancy time of the leading aircraft. The relatively short buffer time in this formula acts as a safety period applied by the control tower based on their expertise and the airport conditions. The speeds of leading and trailing planes are also symbolized by Viand Vj, respectively. • All planes that enter the airport have a two-way visit with a departure already scheduled for them, and no aircraft is expected to stay at the airport indefinitely. 3.3. Simulation Modeling The simulation model is developed using the discrete event method in Enterprise Dynamics ™ (ED) software (version 9), which was proven to possess the versatility and reliability necessary to produce a model of sufficient detail and sensitivity, while offering acceptable 3D features for industry presentation [ 36 – 41 ]. The simulation starts with the main entities (i.e., aircraft) entering the holding stack. Once the control tower grants an air- Systems 2024,12, 394 7 of 18 craft permission to exit the stack, it heads to the approach zone and glides to the dedicated runway. Here, it is the responsibility of the model’s control tower to maintain the required distance between aircraft by holding the planes in the stack sufficiently. After completing the landing procedure on the runway, the aircraft enters the apron area through a given taxiway. Subsequently, apron operations (unboarding, unloading, refueling, loading, and boarding) are performed on the airplane. As we assume that the airplanes that enter the airport do not stay there permanently, the apron operations for each plane should be followed by a departure request. After receiving the departure permission, the plane takes a prescribed taxiway and runway for their takeoff operation and leaves the system. Here, as explained earlier, if the runway is occupied (or locked for another landing plane), the plane has to queue in the taxiway for the final permission to use the runway. This process and the corresponding sequence of events are summarized in Figure 2. Systems 2024, 12, x FOR PEER REVIEW 7 of 18 • All planes that enter the airport have a two-way visit with a departure already scheduled for them, and no aircraft is expected to stay at the airport indefinitely. 3.3. Simulation Modeling The simulation model is developed using the discrete event method in Enterprise Dynamics™ (ED) software (version 9), which was proven to possess the versatility and reliability necessary to produce a model of sufficient detail and sensitivity, while offering acceptable 3D features for industry presentation [36–41]. The simulation starts with the main entities (i.e., aircraft) entering the holding stack. Once the control tower grants an aircraft permission to exit the stack, it heads to the approach zone and glides to the dedicated runway. Here, it is the responsibility of the model’s control tower to maintain the required distance between aircraft by holding the planes in the stack sufficiently. After completing the landing procedure on the runway, the aircraft enters the apron area through a given taxiway. Subsequently, apron operations (unboarding, unloading, refueling, loading, and boarding) are performed on the airplane. As we assume that the airplanes that enter the airport do not stay there permanently, the apron operations for each plane should be followed by a departure request. After receiving the departure permission, the plane takes a prescribed taxiway and runway for their takeoff operation and leaves the system. Here, as explained earlier, if the runway is occupied (or locked for another landing plane), the plane has to queue in the taxiway for the final permission to use the runway. This process and the corresponding sequence of events are summarized in Figure 2. Figure 2. A schematic view of the main processes and events’ sequence in an airside of a typical civil airport with both landing and departure flights. The core of the proposed model can be found in the 2D layout of the digital twin presented in Figure 3. For the three aircraft sizes considered in this system, we utilize three distinct “Source” atoms (i.e., objects) that help model the inter-arrival time of each individual type of aircraft conveniently. A “Queue” atom models the holding stack simultaneous operations. In this model, three unique “Multiservice” atoms represent the approach gate, taxiway, and apron operations. Furthermore, to model the single runway assumed in this paper, we use a “Server” atom. The service time of this server and the mentioned multiservice atoms depend on the type of aircraft. The capacity of the multiservice object is fully adjustable, which empowers this model to flexibly adopt the specifications of the real system. Another advantage of the used objects is their capability to implement the possible failures in the system (one may refer to [36,37,39] for further information about this feature). According to our problem definition and the process sequence demonstrated in Figure 2, the apron is visited only once by each aircraft, while the taxiways and runway are expected to be used twice: once in the landing process and another time on or before the departure operations. Here, a few atoms together emulate the role of the control tower in regulating the takeoff operations in this digital twin. A “Server” and a “Queue” (placed in the bottom part of the model in Figure 3) manage the outgoing flights and lock the runway in case an incoming flight is about to land. Departure flights leave the system through a “Sink” atom (shown by a teal object in the top part of the layout). Another duty Figure 2. A schematic view of the main processes and events’ sequence in an airside of a typical civil airport with both landing and departure flights. The core of the proposed model can be found in the 2D layout of the digital twin presented in Figure 3. For the three aircraft sizes considered in this system, we utilize three distinct “Source” atoms (i.e., objects) that help model the inter-arrival time of each individual type of aircraft conveniently. A “Queue” atom models the holding stack simultaneous operations. In this model, three unique “Multiservice” atoms represent the approach gate, taxiway, and apron operations. Furthermore, to model the single runway assumed in this paper, we use a “Server” atom. The service time of this server and the mentioned multiservice atoms depend on the type of aircraft. The capacity of the multiservice object is fully adjustable, which empowers this model to flexibly adopt the specifications of the real system. Another advantage of the used objects is their capability to implement the possible failures in the system (one may refer to [ 36 , 37 , 39 ] for further information about this feature). Systems 2024, 12, x FOR PEER REVIEW 8 of 18 of the control tower is performed by the “Server” atom preceding this sink, which imposes the required departure–departure distance rules between successive outgoing flights. Figure 3. A snapshot of the 2D layout of the proposed simulation model of the studied system developed in Enterprise Dynamics software. In addition to the presented 2D layout of the model, a 3D animated view is designed for the proposed digital twin, which graphically illustrates the sequence of operations. This feature enhances the ease of interpreting the results and behavior of our twin, enabling real-time system monitoring or focused observation of specific details from desired angles. Figure 4 depicts an example snapshot of the mentioned 3D with multiple planes using the airside. Figure 4. A glance at our 3D digital twin designed in ED software for the airside system. 4. Implementation in an International Airport In this section, we explain the implementation of the proposed digital twin in an international airport in Iran with one operating runway. In order for the proposed Figure 3. A snapshot of the 2D layout of the proposed simulation model of the studied system developed in Enterprise Dynamics software. Systems 2024,12, 394 8 of 18 According to our problem definition and the process sequence demonstrated in Figure 2 , the apron is visited only once by each aircraft, while the taxiways and runway are expected to be used twice: once in the landing process and another time on or before the departure operations. Here, a few atoms together emulate the role of the control tower in regulating the takeoff operations in this digital twin. A “Server” and a “Queue” (placed in the bottom part of the model in Figure 3) manage the outgoing flights and lock the runway in case an incoming flight is about to land. Departure flights leave the system through a “Sink” atom (shown by a teal object in the top part of the layout). Another duty of the control tower is performed by the “Server” atom preceding this sink, which imposes the required departure–departure distance rules between successive outgoing flights. In addition to the presented 2D layout of the model, a 3D animated view is designed for the proposed digital twin, which graphically illustrates the sequence of operations. This feature enhances the ease of interpreting the results and behavior of our twin, enabling real-time system monitoring or focused observation of specific details from desired angles. Figure 4depicts an example snapshot of the mentioned 3D with multiple planes using the airside. Systems 2024, 12, x FOR PEER REVIEW 8 of 18 of the control tower is performed by the “Server” atom preceding this sink, which imposes the required departure–departure distance rules between successive outgoing flights. Figure 3. A snapshot of the 2D layout of the proposed simulation model of the studied system developed in Enterprise Dynamics software. In addition to the presented 2D layout of the model, a 3D animated view is designed for the proposed digital twin, which graphically illustrates the sequence of operations. This feature enhances the ease of interpreting the results and behavior of our twin, enabling real-time system monitoring or focused observation of specific details from desired angles. Figure 4 depicts an example snapshot of the mentioned 3D with multiple planes using the airside. Figure 4. A glance at our 3D digital twin designed in ED software for the airside system. 4. Implementation in an International Airport In this section, we explain the implementation of the proposed digital twin in an international airport in Iran with one operating runway. In order for the proposed Figure 4. A glance at our 3D digital twin designed in ED software for the airside system. 4. Implementation in an International Airport In this section, we explain the implementation of the proposed digital twin in an international airport in Iran with one operating runway. In order for the proposed framework to be used as a decision support tool for this airport, the first step is collecting data from the existing state of the system. Based on our survey of historical data in this airport and by consulting the staff and engineers of the airport, we consider the following specifications for this case: • The common approach/glide path of this airport is 15 km in length, and the safety buffer time is 20 s. • Based on the observed restriction on the operational teams, apron capacity is assumed to be independent of the type of aircraft. Apron capacity is 5 aircraft, and it is also independent of their type. • Due to the prevailing weather conditions in the area where the airport is located, runway processes of both arrivals and departures are under the IFR conditions. Systems 2024,12, 394 9 of 18 • Equipment and aircraft maintenance operations are assumed to be completely effective. Thus, no failure is expected during the analysis horizon. • Changes in the direction and magnitude of wind are disregarded, i.e., the arrival and departure operations are performed in one direction only. The used simulation software offers a wide range of options available in the “Experiment Wizard” for collecting results from the digital twin. For this study, the observation is replicated 20 times, each for a period of 365 days. Here, to capture the performance of the system in a more realistic and steadier situation, the first 60 days of each observation are considered as a warm-up period. The logged key performance indicators (KPIs) are: (i) the number of airplanes served from each type and (ii) the average waiting time of each aircraft type for permissions in different stages (e.g., in holding stack and the departure waiting times). Inadequate or biased data may result in untrustworthy simulations, and the exactitude of the digital twin is contingent upon the quality and comprehensiveness of the supplied data. Therefore, as a fundamental phase of the implementation, the data gathering over the initial three months of our engagement with this airport involves conducting 15 interviews and meetings with the airport personnel and management, as well as dedicating 20 h to exclusive observations and collaboration with the control tower team. This helped us comprehend the natural behavior and decision-making routines of this airport. Furthermore, there was continuous and ongoing cooperation with a key consultant of the airport throughout the project. In order to gather the validation data from the system’s steady and normal operation, we utilized the performance data of a one-year period prior to the onset of the COVID-19 outbreak in 2019. That was to mitigate any prejudiced findings arising from the impact of COVID-related lockdowns on the aviation sector reported by other studies such as Suau-Sanchez et al. [ 42 ]. Using the mentioned records, we obtained the required data for estimating the following parameters: (i) the aircraft approach speed (in meters per second), (ii) occupancy time of the runway both in landing and takeoff (in seconds), (iii) taxiway occupancy time (TOT) in seconds, (iv) time between arrivals to the holding stack (TBAH) (in hours), and (v) gate (apron) occupancy time (AOT) in seconds. For each of these parameters, the data is examined statistically to identify the best statistical distribution. These goodness-of-fit tests were performed on more than 150 random samples derived from the data, and the best-fit distributions were identified with a 95% confidence interval. Figure 5demonstrates this test for the landing ROT of aircraft type C, for which the 200 data points randomly fall around (and close to) the fitted normal distribution line. As seen in this figure, the chosen distribution type (here, normal) fits the data with a very high p-value (>0.250) and the Anderson–Darling (AD) values of 0.780, which indicate no significant type-I error for this fit. The estimated mean and standard deviation of this parameter are approximately 60 and 8 s, respectively. Systems 2024, 12, x FOR PEER REVIEW 10 of 18 respectively. The results of these fit tests and the estimated distribution and their associated parameters are presented in Table 3. As a next step of implementing this framework in this airport, we run the twin for one year to see if its performance and KPIs in this state comply with the available records of served aircraft by the real system. This step ensures that the simulation model is an accurate representation of the system and can be used as a digital decision support tool. We ran the model for a few replications and then presented the results to our industry experts, consulting about the level of similarity between our model’s behavior with that of the real system. We also asked the experts to observe the 2D and 3D of the twin to confirm that the recognized logics and sequencing rules for the visiting aircraft are reasonable and realistic. The satisfactory feedback received from the experts in this stage encouraged us to further examine the model by applying a statistical hypothesis testing method. Table 3. Statistical distributions and the relevant fit test results for the input parameters in the chosen airport. Parameter Aircraft Type A & B C D Distribution Fit Results (p-Value|AD **) Distribution Fit Results (p-Value|AD) Distribution Fit Results (p-Value|AD) Approach Speed (m/s) N (52, 3) * 0.175 | 1.52 N (68, 5) >0.250 | 0.66 N (75, 7) >0.250 | 1.18 Landing ROT (s) N (54, 10) >0.250 | 0.55 N (60, 8) >0.250 | 0.78 N (65, 5) 0130 | 1.73 Departure ROT (s) N (55, 3) >0.250 | 1.03 N (49, 5) 0.130 | 1.73 N (43, 7) 0.240 | 1.28 TOT (s) N (200, 12) 0.133 | 1.71 N (180, 13) >0.250 | 0.41 N (173, 10) >0.250 | 0.66 TBAH (h) W (24.07, 0.7, 0) 0.207 | 1.40 LN (3.91, 413, 0) >0.250 | 0.51 G (11.40, 1.20, 0) >0.250 | 0.70 AOT (s) N (2700, 80) >0.250 | 0.88 N (3300, 95) >0.250 | 0.83 N (4000, 122) 0.118 | 1.81 * 𝑁󰇛𝜇,𝜎󰇜: normal distribution; W󰇛𝛼,𝛽,𝜃󰇜: Weibull distribution; 𝐿𝑁󰇛𝛾,𝛽,𝜃󰇜: lognormal distribution; G 󰇛 𝛼,𝛽,𝜃 󰇜 : gamma distribution. ** Anderson–Darling statistic. Figure 5. Normal distribution goodness of fit plot for landing ROT of aircraft type C. For this purpose, a one-sample Wilcoxon nonparametric hypothesis test is applied on the known nominal capacity of the system for small and medium (A&B), large (C), and heavy (D) planes compared with the results of our model with a 95% confidence interval. Table 4 presents the results of the aforementioned hypothesis tests based on a 25-run sample of the simulation model under peak conditions where there were no restrictions on aircraft arrivals. From the calculated p-values of 0.421, 0.548, 0.574, and 0.830 for the Figure 5. Normal distribution goodness of fit plot for landing ROT of aircraft type C. Systems 2024,12, 394 16 of 18 This framework was implemented in an international airport to assess the validity of the twin in practice. Some statistical tests were performed, and the management of the airport was consulted about the performance of our twin. The positive feedback received from the experts, together with the exceptional test results reported for this airport, confirmed the usability of this framework in real airport systems. One of the major advantages of such a twin is the possibility of predicting the outcome of managerial decisions for such a complex system. Thus, as an extra step in this research, we compiled any possible changes in the settings to enhance the airport KPIs, forming a list of modification scenarios. These scenarios were all defined by close consultation of the airport experts and managers to guarantee the acceptability and application of the outcome. The investigated scenarios offered an increase of up to 28% in the overall nominal capacity, while reducing the average delay of aircraft by up to 18%. Given the significant similarities between international airports around the world, we anticipate that the proposed framework will be easily adaptable to match the characteristics of other airports. Hence, as an extension avenue for this work, one may investigate the performance of the proposed digital twin approach in other case studies. Our primary performance indicators were chosen in line with the needs and preferences of the managers of the studied airport. Nonetheless, with the core of this framework being a proper discrete event simulation model, other performance measures may also be logged to satisfy the individual requirements of future projects. Another extension to the current paper may involve fatal failures both in the facilities and in airplanes, together with the corresponding downtimes in different parts of the system. An important prerequisite for such an extension attempt, however, would be a sufficient amount of data on such failure and repair events as an extra input to the model. Author Contributions: Conceptualization, M.B. and S.R.; methodology, A.A., M.B. and S.R.; software, A.A. and M.B.; validation, A.A., M.B. and S.R.; formal analysis, A.A., M.B., S.R. and M.N.; investigation, A.A., M.B. and S.R.; resources, M.B.; data curation, A.A. and M.B.; writing—original draft preparation, A.A., M.B., S.R. and M.N.; writing—review and editing, A.A., M.B., S.R. and M.N.; visualization, A.A.; supervision, S.R. and M.N.; project administration, A.A., M.B. and S.R. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Conflicts of Interest: The authors declare no conflicts of interest. References 1. Qudrat-Ullah, H. Introduction: Managing Complex Tasks with Systems Thinking. In Managing Complex Tasks with Systems Thinking; Springer: Berlin, Germany, 2023; pp. 3–23. 2. Siddique, I. Systems Engineering in Complex Systems: Challenges and Strategies for Success. Eur. J. Adv. Eng. Technol. 2022 , 9, 61–66. [CrossRef] 3. Marquez, V. Landside| Airside: Why Airports Are the Way They Are; Springer: Berlin, Germany, 2019. 4. Skorupski, J. Airside and Landside Modelling for Determining Airport Capacity. Proc. MATH-MoD 2009,9, 271–282. 5. Yang, Z.; Yu, S.; Notteboom, T. Airport location in multiple airport regions (MARs): The role of land and airside accessibility. J. Transp. Geogr. 2016,52, 98–110. [CrossRef] 6. Di Mascio, P.; Moretti, L.; Piacitelli, M. Airport landside sustainable capacity and level of service of terminal functional subsystems. Sustainability 2020,12, 8784. [CrossRef] 7. Sadono, M.; Putra, D.; Harahap, S. Development of Aircraft Movement Simulation in Airport Airside Area. AVIA 2021 ,2, 9–19. [CrossRef] 8. Oliveira, P.P. Digital twin development for airport management. J. Airpt. Manag. 2020,14, 246–259. [CrossRef] 9. Pérez, E.; Taunton, L.; Sefair, J.A. A simulation-optimization approach to improve the allocation of security screening resources in airport terminal checkpoints. In Proceedings of the 2021 Winter Simulation Conference (WSC), Phoenix, AZ, USA, 15–17 December 2021; pp. 1–11. 10. Scozzaro, G.; Mota, M.M.; Delahaye, D.; Mancel, C. Simulation-Optimisation-based decision support system for managing airport security resources. In Proceedings of the EUROSIM 2023, Amsterdam, The Netherlands, 3–5 July 2023. Systems 2024,12, 394 17 of 18 11. Martinez, D.; Zertuche, D.; Pérez, E. Simulation Based Strategy for Dynamic Workforce Allocation and Checkpoint Configuration in Airport Screening Facilities. In Proceedings of the IIE Annual Conference, New Orleans, LO, USA, 21–23 May 2023; pp. 1–6. 12. Manataki, I.E.; Zografos, K.G. Assessing airport terminal performance using a system dynamics model. J. Air Transp. Manag. 2010,16, 86–93. [CrossRef] 13. Uyar, M.T.; Gürsel, G. Enhancing Airport Efficiency by Simulating Passenger Waiting Times. J. Appl. Eng. Agric. 2024,1, 47–51. 14. Chen, X.; Li, J.; Gao, Q. A simple process simulation model for strategic planning on the airside of an airport: A case study. J. Simul. 2015,9, 64–72. [CrossRef] 15. Özdemir, M.; Çetek, C.; Usanmaz, Ö. Airside capacity analysis and evaluation of Istanbul Ataturk airport using fast-time simulations. Anadolu Univ. J. Sci. Technol. A Appl. Sci. Eng. 2018,19, 153–164. [CrossRef] 16. Dönmez, K.; Aydo˘gan, E.; Çetek, C.; Mara¸s, E.E. The Impact of Taxiway System Development Stages on Runway Capacity and Delay under Demand Volatility. Aerospace 2023,10, 6. [CrossRef] 17. Feng, Y.; Johnson, M.E. Simulation Analyses of End-Around Taxiway Operations under Four Different Runway and Taxiway Choices. Transp. Res. Rec. 2021. [CrossRef] 18. Lai, J.; Che, L.; Kashef, R. Bottleneck Analysis in JFK Using Discrete Event Simulation: An Airport Queuing Model. In Proceedings of the 2021 IEEE International Smart Cities Conference (ISC2), Manchester, UK, 7–10 September 2021; pp. 1–7. 19. Zografos, K.G.; Madas, M.A. Development and demonstration of an integrated decision support system for airport performance analysis. Transp. Res. Part C Emerg. Technol. 2006,14, 1–17. [CrossRef] 20. Driver, C. Capacity utilisation and excess capacity: Theory, evidence, and policy. Rev. Ind. Organ. 2000,16, 69–87. [CrossRef] 21. Horonjeff, R.; McKelvey, F.X.; Sproule, W.J.; Young, S.B. Planning and Design of Airports, 5th ed.; McGraw-Hill: New York, NY, USA, 2010; Volume 4. 22. Ashford, N.J.; Mumayiz, S.; Wright, P.H. Airport Engineering: Planning, Design, and Development of 21st Century Airports; John Wiley & Sons: Hoboken, NJ, USA, 2011. 23. Ignaccolo, M. A simulation model for airport capacity and delay analysis. Transp. Plan. Technol. 2003,26, 135–170. [CrossRef] 24. Deng, W.; Zhang, L.; Zhou, X.; Zhou, Y.; Sun, Y.; Zhu, W.; Chen, H.; Deng, W.; Chen, H.; Zhao, H. Multi-strategy particle swarm and ant colony hybrid optimization for airport taxiway planning problem. Inf. Sci. 2022,612, 576–593. [CrossRef] 25. Sui, D.; Chen, H.; Zhou, T. A Conflict Resolution Strategy at a Taxiway Intersection by Combining a Monte Carlo Tree Search with Prior Knowledge. Aerospace 2023,10, 914. [CrossRef] 26. Jiang, Y.; Xue, Q.; Wang, Y.; Cai, M.; Zhang, H.; Li, Y. Traffic congestion mechanism in mega-airport surface. Phys. A Statist. Mech. Its Appl. 2021,577, 125966. [CrossRef] 27. Liu, Y.; Hu, M.; Yin, J.; Su, J.; Wang, S.; Zhao, Z. Optimization Design and Performance Evaluation of U-Shaped Area Operation Procedures in Complex Apron. Aerospace 2023,10, 161. [CrossRef] 28. IATA. IOSA Reference Manual for Audit Programs (IRM), 12th ed.; International Air Transport Association: Montreal, ON, Canada, 2022. 29. Sznajderman, L.; Ramírez-Díaz, G.; Di Bernardi, C.A. Influence of the apron parking stand management policy on aircraft and ground support equipment (gse) gaseous emissions at airports. Aerospace 2021,8, 87. [CrossRef] 30. Dixit, A.; Jakhar, S.K. Airport capacity management: A review and bibliometric analysis. J. Air Transp. Manag. 2021 ,91, 102010. [CrossRef] 31. Young, S.; Wells, A. Airport Planning and Management, 6th ed.; McGraw Hill: New York, NY, USA, 2011. 32. Andreeva-Mori, A.; Onji, M. Traffic-dependent Airborne Delay Buffer Optimization for Efficient Air Traffic Flow Management under Uncertainties. Trans. Jpn. Soc. Aeronaut. Space Sci. 2024,67, 145–153. [CrossRef] 33. Zhao, Z.; Yuan, J.; Chen, L. Air Traffic Flow Management Delay Prediction Based on Feature Extraction and an Optimization Algorithm. Aerospace 2024,11, 168. [CrossRef] 34. Liu, Y.; Hansen, M. Ground Delay Program decision-making using multiple criteria: A single airport case. In Proceedings of the USA/Europe Air Traffic Management Research & Development Seminar, Chicago, IL, USA, 10–13 June 2013. 35. Laukotka, F.N.; Krause, D. Supporting digital twins for the retrofit in aviation by a model-driven data handling. Systems 2023 , 11, 142. [CrossRef] 36. Amiri, M.; Taghavifard, M.T.; Azimi, P.; Aghaei, M. Multi-Objective Model for determining Optimal Buffer Size and RedundancyAvailability Allocation Simultaneously in Manufacturing Systems. Ind. Manag. J. 2019,11, 427–460. 37. Attar, A.; Raissi, S.; Khalili-Damghani, K. A simulation-based optimization approach for free distributed repairable multi-state availability-redundancy allocation problems. Reliab. Eng. Syst. Saf. 2017,157, 177–191. [CrossRef] 38. Attar, A.; Raissi, S.; Khalili-Damghani, K. Simulation-optimization approach for a continuous-review, base-stock inventory model with general compound demands, random lead times, and lost sales. Simulation 2016,92, 547–564. [CrossRef] 39. Attar, A.; Raissi, S.; Khalili-Damghani, K. Multi-Objective Reliability-Redundancy Allocation for Non-Exponential Multi-State Repairable Components. In Proceedings of the 11th International Conference of Industrial Engineering, IIEC2015, Tehran, Iran, 7–8 January 2015; pp. 1–8. [CrossRef] 40. Ghasemi, P.; Goodarzian, F.; Simic, V.; Tirkolaee, E.B. A DEA-based simulation-optimisation approach to design a resilience plasma supply chain network: A case study of the COVID-19 outbreak. Int. J. Syst. Sci. Oper. Logist. 2023 ,10, 2224105. [CrossRef] 41. Pourhassan, M.R.; Raissi, S.; Apornak, A. Modeling multi-state system reliability analysis in a power station under fatal and nonfatal shocks: A simulation approach. Int. J. Qual. Reliab. Manag. 2021,38, 2080–2094. [CrossRef] Systems 2024,12, 394 18 of 18 42. Suau-Sanchez, P.; Voltes-Dorta, A.; Cugueró-Escofet, N. An early assessment of the impact of COVID-19 on air transport: Just another crisis or the end of aviation as we know it? J. Transp. Geogr. 2020,86, 102749. [CrossRef] 43. Attar, A.; Jin, Y.; Luis, M.; Zhong, S.; Sucala, V.I. Simulation-Based Analyses and Improvements of the Smart Line Management System in Canned Beverage Industry: A Case Study in Europe. In Proceedings of the 2023 Winter Simulation Conference (WSC), San Antonio, TX, USA, 10–13 December 2023; pp. 2124–2135. [CrossRef] 44. Attar, A.; Raissi, S.; Tohidi, H.; Feizollahi, M.J. A Novel Perspective on Reliable System Design With Erlang Failures and Realistic Constraints for Incomplete Switching Mechanisms. IEEE Access 2023,11, 51900–51914. [CrossRef] 45. Attar, A.; Irawan, C.A.; Akbari, A.A.; Zhong, S.; Luis, M. Multi-disruption resilient hub location–allocation network design for less-than-truckload logistics. Transp. Res. Part A Policy Pract. 2024,190, 104260. [CrossRef] 46. Jerbi, A.; Ammar, A.; Krid, M.; Salah, B. Performance optimization of a flexible manufacturing system using simulation: The Taguchi method versus OptQuest. Simulation 2019,95, 1085–1096. [CrossRef] 47. Hussain, M.S.; Ali, M. Multi-agent-based dynamic scheduling of flexible manufacturing systems with routing flexibility. In Smart Systems; CRC Press: Boca Raton, FL, USA, 2024; pp. 212–229. 48. Megdouli, K.; Gholizadeh, T.; Tashtoush, B.; Cinnella, P.; Skorek-Osikowska, A. Optimization of carbon dioxide ejector expansion transcritical refrigeration system with ANOVA and NSGA-II. Int. J. Refrig. 2024,158, 173–189. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.