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Abstract

Airports face challenges due to the increasing volume of air traffic and tighter environmental restrictions which result in a need to actively integrate speed profiles into conventional routing and scheduling procedure. However, only until very recently, the research on airport ground movement has started to take into account such a speed profile optimisation problem actively so that not only time efficiency but also fuel saving and decrease in airport emissions can be achieved at the same time. It is envisioned that the realism of planning could also be improved through speed profiles. However, due to the multi-objective nature of the problem and complexity of the investigated models (objective functions), the existing speed profile optimisation approach features high computational demand and is not suitable for an on-line application. In order to make this approach more competitive for real-world application and to meet limits imposed by International Civil Aviation Organization for on-line decision time, this paper introduces a pre-computed database acting as a middleware to effectively separate the planning (routing and scheduling) module and the speed profile generation module. Employing a database not only circumvents duplicative optimisation for the same taxiway segments, but also completely avoids the computation of speed profiles during the on-line decision support owing a great deal to newly proposed database initialization procedures. Moreover, the added layer of database facilitates, in the future, more complex and realistic models to be considered in the speed profile generation module, without sacrificing on-line decision time. The experimental results carried out using data from a major European hub show that the proposed approach is promising in speeding up the search process.

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... As a result, OR has transformed from an efficiency-oriented application to an environment-oriented one in ATFMPs. The past two decades have witnessed the extensive utilization of OR in addressing environmental concerns related to different ATFMPs (Liang et al., 2020;Simaiakis and Balakrishnan, 2016;Weiszer et al., 2015b;Baaren and Roling, 2019;Sölveling et al., 2011;Ho-Huu et al., 2020;Bertsimas and Frankovich, 2015). Two review papers have investigated the literature addressing environmental considerations in ATFMPs (Singh and Sharma, 2015;Guimarans et al., 2019). ...
... The 4DTs guidance system requires approaches that can produce an efficient route, schedule, and precise speed profile to guide the control procedure. Therefore, numerous studies have attempted to optimize the speed profile (Chen et al., 2015a,b;Zhang et al., 2015Zhang et al., , 2018Zhang et al., , 2019 or solve the coupled optimization problem of taxi routing, scheduling, and speed profile (Ravizza et al., 2013(Ravizza et al., , 2012Chen et al., 2016;Evertse and Visser, 2017;Obajemu et al., 2021;Weiszer et al., 2014Weiszer et al., , 2015bWeiszer et al., , 2020Zhang et al., 2022b). In this stream, reducing fuel consumption was a general objective function, and the multi-objective optimization model aiming to enhance efficiency and mitigate environmental hazards simultaneously was the mainstream (Chen et al., 2015b,a;Zhang et al., 2019;Ravizza et al., 2013Ravizza et al., , 2012Chen et al., 2016;Evertse and Visser, 2017;Weiszer et al., 2014Weiszer et al., , 2015bWeiszer et al., , 2020Zhang et al., 2022b). ...
... Therefore, numerous studies have attempted to optimize the speed profile (Chen et al., 2015a,b;Zhang et al., 2015Zhang et al., , 2018Zhang et al., , 2019 or solve the coupled optimization problem of taxi routing, scheduling, and speed profile (Ravizza et al., 2013(Ravizza et al., , 2012Chen et al., 2016;Evertse and Visser, 2017;Obajemu et al., 2021;Weiszer et al., 2014Weiszer et al., , 2015bWeiszer et al., , 2020Zhang et al., 2022b). In this stream, reducing fuel consumption was a general objective function, and the multi-objective optimization model aiming to enhance efficiency and mitigate environmental hazards simultaneously was the mainstream (Chen et al., 2015b,a;Zhang et al., 2019;Ravizza et al., 2013Ravizza et al., , 2012Chen et al., 2016;Evertse and Visser, 2017;Weiszer et al., 2014Weiszer et al., , 2015bWeiszer et al., , 2020Zhang et al., 2022b). ...
Article
The escalating demand for air travel has significant environmental implications, such as air pollution and noise nuisance, compelling airports to mitigate their negative impacts on the environment. Operations Research (OR) has played a crucial role in optimizing environment-oriented airport traffic flow management problems, including gate assignment, pushback control, taxi planning, tow vehicles operations, runway scheduling, and aircraft trajectory optimization, to align with airports’ environmental objectives. Despite the thriving body of papers, there is a lack of an inclusive review framework for papers in this field. Therefore, this paper introduces a comprehensive classification framework and conducts a thorough review of the application of OR in addressing environmental concerns. The review identifies prevailing trends, summarizes environmental concerns and methodologies, and presents research agendas. Future studies should prioritize efforts to enhance existing research in terms of modeling and algorithms while also exploring the interdisciplinary incorporation of OR with reinforcement learning and big data analytics, as well as its application in emerging scenarios.
... In a multigraph, there can be more than one edge between two nodes, corresponding to different candidate speed profiles. Desirable candidate speed profiles can be generated offline and saved in a database for more efficient computation at runtime [12]. This also enables time consuming calculation of the Pareto front with respect to multiple conflicting objectives. ...
... A group of consecutive edges in the layout model forms a straight segment, if the angle between two neighbouring edges is small enough (<30 degrees [12]). If the angle of an edge is larger than 30 degrees with respect to the predecessor edge, it will form a turning segment. ...
... For each segment, multiple Pareto optimal speed profiles are precomputed depending on the weight category of aircraft with respect to the conflicting objectives [4], [12]. The speed profile contains acceleration, constant speed, deceleration and rapid deceleration phases where the duration of each phase determines the taxiing time and fuel cost of the speed profile. ...
Conference Paper
Full-text available
Routing and scheduling of airport ground movements poses a critical issue for efficient surface operations. For real-world applications, multiple objectives should be considered, leading to a multigraph representation of the search space. Meanwhile, intermediate holding is often needed to a) release availability of scarce resources such as runways and gates for more cost-effective routing and scheduling, b) provide additional solutions in the speed profile database that can be used during routing and scheduling, and c) keep airport ground movements functional during disruptive events that may paralyse part of the taxiway network. This paper presents an extended multiobjective memetic algorithm upon the multigraph model to search for desirable solutions with intermediate holding. The performance of the proposed algorithm is examined with problem instances of different airport layouts. The results demonstrate prominent savings in both time and fuel costs compared with solutions without intermediate holding.
... The conflict-induced delay of aircraft under each conflict type is accurately calculated. The calculation accuracy of conflict-induced delay higher than those of previous studies on route planning [18][19][20][21][22], and it provides references for refined surface route planning schemes. ...
... Some studies about multi-objective surface route planning assure the accuracy and timely optimization results by improving the algorithms. Weiszer et al. [19] introduced a precomputed database as the middleware to meet the online decision time limit of ICAO and effectively separated the planning and speed profile generation modules. With regard to the coupled runway sequencing (arrival and departure) and taxiway routing problems, Benlic et al. [43] proposed a local search algorithm to optimize the taxiing route of aircraft and decrease the taxiing waiting time and fuel consumption. ...
... Weiszer et al. [22] set up a multi-objective evolutionary optimization framework to improve the overall surface operating efficiency of airports and achieve fast convergence and potentially good solutions through the evolutionary search algorithm based on modified crowding distance. However, these studies [19,22,43,44] provide few considerations to the influences of environmental differences on the multiobjective surface operation optimization of aircraft. ...
Article
Aircraft taxiing in large airports is often delayed by traffic conflicts. The increased fuel consumption and pollutant emission lower the efficiency of airport surface operation and bring potential safety hazards. Previous studies on route planning for surface taxiing seldom involve refined delay analysis under different traffic conflict types and discussions on route planning at airports with various environmental parameters and under diverse aircraft types. In this study, dynamic models of crossing, head-on, and trailing conflicts during aircraft taxiing were constructed, and delays of these three conflict types were determined. The shortest route set of restricted routing was determined by the Yen algorithm according to the topological network, conflict-induced delay, and spatial distributions of taxiing at airports. Considering the three optimization objects, the aircraft route planning was optimized and determined from the shortest route set according to the differences in the environmental parameters and aircraft types. Experiment results show that the proposed method achieves 9.6–12.1% higher route planning precision in all traffic periods compared with previous route planning with considerations to taxiing conflict. This method also provides support to the dynamic decision of airport operation and control departments according to the environment and performance of aircraft.
... In this paper we found nonadditivity cases were rare in practice as it is possible only if (1) a node is an end of both straight and turning segment at the same time, and (2) all objectives are dominated in order to be eliminated from the search. For the airport instance from Hong Kong International Airport investigated in this paper, the number of such cases has been counted maximum 1.72% of all pairs of parallel segments similar as in Fig. 3. Furthermore, adopting the same modelling approach as QPPTW and -QPPTW algorithms facilitates fair comparison of these algorithms with the proposed algorithm and enables to employ the same database of speed profiles (Weiszer et al., 2015b) regardless of the routing and scheduling algorithm. ...
... Speed profiles, with the corresponding nondominated costs for each objective are stored in a database. The database of speed profiles is created in the preprocessing step using , as described in Weiszer et al. (2015b), such that every possible segment for a specific airport is stored in the database. Each straight segment is further categorised as breakaway, intermediate and holding determining their start/end speed. ...
... -QPPTW and QPPTW algorithms used in this paper follow Ravizza et al. (2013b). These two algorithms have been adapted to use the database of speed profiles (Weiszer et al., 2015b). ...
Article
Full-text available
Recent research on airport ground movement introduced an Active Routing framework to support multi-objective trajectory-based operations. This results in edges in the airport taxiway graph having multiple costs such as taxi time, fuel consumption and emissions. In such a graph, multiple edges exist between two nodes reflecting different trade-offs among the multiple costs. Aircraft will have to choose the most efficient edge from multiple edges in order to traverse from one node to another respecting various operational constraints. In this paper, we introduce a multi-objective routing and scheduling algorithm based on the enumerative approach that can be used to solve such a multi-objective multi-graph problem. Results using the proposed algorithm for a range of international airports are presented. Compared with other routing and scheduling algorithms, the proposed algorithm can find a representative set of optimal or near optimal solutions in a single run when the sequence of aircraft is fixed. In order to accelerate the search, heuristic functions and a preference-based approach are introduced. We analyse the performance of different approaches and discuss how the structure of the multi-graph affects computational complexity and quality of solutions.
... The computational results with data from major international hub airports show the efficiency of the proposed approach. Recently, the Active Routing (AR) approach for airport ground movement has been introduced (Chen et al., 2016a,b;Weiszer et al., 2015a;Weiszer et al., 2015b) with the aim of providing near-optimal nondominated speed profiles and routes for taxiing aircraft. AR enables the routing and scheduling of taxiing aircraft, which was previously based on distance, emphasising time efficiency, to be optimised with regard to richer information embedded within speed profiles. ...
... The real-time application of the AR framework can be achieved using a pre-computed database of nondominated speed profiles for key building blocks (i.e. straight taxiway segments) of the airport layout (Weiszer et al., 2015a). The database acts as a middleware to effectively separate the speed profile generation module from the routing and scheduling module. ...
... In Line 1, building blocks are identified from a graph representation of airport taxiway layout (Weiszer et al., 2015a). The building blocks include all straight segments of taxiways, separated by turning segments. ...
Article
Full-text available
In addition to time efficiency, minimisation of fuel consumption and related emissions has started to be considered by research on optimisation of airport surface operations as more airports face severe congestion and tightening environmental regulations. Objectives are related to economic cost which can be used as preferences to search for a region of cost efficient and Pareto optimal solutions. A multi-objective evolutionary optimisation framework with preferences is proposed in this paper to solve a complex optimisation problem integrating runway scheduling and airport ground movement problem. The evolutionary search algorithm uses modified crowding distance in the replacement procedure to take into account cost of delay and fuel price. Furthermore, uncertainty inherent in prices is reflected by expressing preferences as an interval. Preference information is used to control the extent of region of interest, which has a beneficial effect on algorithm performance. As a result, the search algorithm can achieve faster convergence and potentially better solutions. A filtering procedure is further proposed to select an evenly distributed subset of Pareto optimal solutions in order to reduce its size and help the decision maker. The computational results with data from major international hub airports show the efficiency of the proposed approach.
... Sub-optimal utilization of airport capacity and resources leads to excessive runway queuing, aircraft conflicts, and passenger delays. Existing studies have focused on the optimization of various airside operations such as off-block control , runway sequencing (Chandrasekar and Hwang, 2015), gate/stand allocation (Urszula and Atkin, 2013;Benlic et al., 2017), taxi routing (Marin and Codina, 2008;Balakrishnan and Jung, 2007), node sequencing (Smeltink and Soomer, 2004;Rathinam et al., 2008), and taxiing speed control (Chen and Stewart, 2011;Weiszer et al., 2015). A major weakness in these studies is that they are all constrained within a network configuration with fixed and known origin-destination of aircraft movement. ...
... Taxiing management aims to improve time and cost effectiveness on taxiway networks, by focusing on taxiing time prediction (Pujet et al., 1998;Shumsky, 1995;Idris et al., 2002;Ravizza et al., 2013), taxiing speed control Weiszer et al., 2015), and taxiing sequencing (Smeltink and Soomer, 2004;Rathinam et al., 2008). Off-block (or pushback) control is a congestion mitigation strategy by metering the off-block operations to reduce taxiing time and delay during the departure process. ...
Article
Full-text available
Airport surface operations, such as off-block control, taxi routing, and runway sequencing, are typically confined to a fixed network topology with given origin–destination (O-D) pairs for departures and arrivals. Reconfiguring the O-D distribution of flights by actively assigning their aprons and runways is a potentially effective measure to maximize the utilization of network capacity. To date this has not been investigated in the literature due to its complexity involving operational constraints, assignment rules, and different stakeholders. This paper demonstrates the significant potential of O-D reconfiguration in improving surface network efficiency by proposing a joint apron-runway assignment framework for pre-tactical operations. This is underpinned by a comprehensive review of apron and runway assignment rules, including constraints and preferences, and an elaborated optimization scheme that encompasses lexicographic and iterative approaches along with temporal buffers to absorb uncertainties in pre-tactical operations. The proposed apron-runway assignment is implemented and assessed in a case study at Beijing Capital International Airport. An airport cellular automata simulator is employed for quantitative evaluation, and qualitative assessment is based on interviews with subject matter experts. Compared to the current operations, the proposed apron-runway assignment is very promising, with reductions in total taxiing distance, average taxiing time, taxiing conflicts, runway queuing time, and fuel consumption by 15.5% (Table 3), 6.2%, 19.8%, 17.6% (Table 4) and 6.6% (Table 5) respectively; gated assignment is increased by 11.8% (Table 4). Importantly, these benefits remain robust to unforeseen flight delays, as demonstrated in a sensitivity analysis.
... In 2014, Ravizza et al. [3] and other researchers studied the path planning of aircraft at the airport surface considering time and fuel consumption and introduced a sequence diagram-based algorithm to solve the problem. In 2015, Weiszer et al. [4] proposed an aircraft at the airport surface motion database for the high calculation time requirements of existing speed configuration optimization methods and effectively separated path planning (routing and scheduling) and speed profile generation modules through precalculation to avoid the same repeated optimization of taxiway sections. In 2015, Weiszer et al. [5] used a multiobjective optimization method to solve the comprehensive optimization problem combining runway scheduling and ground motion problems. ...
... and v 3 can be determined by a4 , v 4 , and d 4 .erefore, there are 4 variables a 1 , d 1 , d 2 , d 4 for each link (N i , N j ). ...
Article
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Most of the traditional taxi path planning studies assume that the aircraft is in uniform speed, and the model is optimized based on the shortest taxi time. Although it is easy to solve, it does not consider the change of the speed profile when the aircraft turns, and the optimal taxiing time of the aircraft does not necessarily bring the optimal taxiing fuel consumption. In this paper, the aircraft’s taxi distance and the number of turns in the taxi are considered. The aircraft path planning model with the shortest total distance of the airport surface is established. The improved A∗ algorithm is used to obtain the shortest path P. Based on this, the shortest path P is established. Considering the multitarget velocity profile model of time and fuel consumption, a heuristic search is used to generate an accurate velocity profile for each path to obtain a 4D trajectory of the aircraft and then quantitative analysis of the impact of aircraft pollutant emissions on the airport environment based on 4D trajectory taxi time. The experimental results show that, compared with the traditional optimization method without considering the turning times, the total taxiing distance and turning times of the aircraft are greatly reduced. By balancing the taxiing time and fuel consumption, a set of Pareto-optimal velocity profiles is generated for the aircraft taxiing path; at the same time, it will help the airport save energy and reduce emissions and improve the quality of the airport environment. It has a high practical application value and is expected to be applied in the real-time air traffic control decision of aircraft surface in the future.
... TBTO aims to realise more precise control of airport ground movements via the assigned 4-dimensional trajectories (4DTs). In addition to the taxiing route, a 4DT also contains the information of the required arrival times at intermediate waypoints, and even the detailed speed profiles for taxiing along the route when the full 4DT is used (Weiszer et al., 2015b). The introduction of these temporal requirements makes it possible to reduce the uncertainty during taxiing (Brownlee et al., 2018) and achieve more efficient surface operations (Weiszer et al., 2015a). ...
... To investigate the performance of FtG guidance, a testing dataset for Pudong airport is generated, which includes 1234 conflictfree 4DTs, corresponding to a typical day's traffic volume of Pudong airport in 2014. These are the most fuel-efficient 4DTs under specific time constraints generated using the existing methods (Weiszer et al., 2015b;Zhang et al., 2018aZhang et al., , 2018b. Note that for generating the 4DTs, artificial waypoints are set along the taxiways for safe separation between aircraft during routing and scheduling, which are referred to as control points (Zhang et al., 2018b). ...
Article
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Safer and more efficient airport ground movements can be planned by routing and scheduling systems based on the 4-dimensional trajectory (4DT). In order to achieve the benefits envisioned in the planning stage, an effective taxiing guidance system is indispensable. The Follow-the-Greens (FtG) guidance concept provides an augmented means for 4DT based taxiing with pilot in the loop, which is expected to guide the piloted aircraft by dynamically adjusting the lit position of green ground navigation lamps according to the assigned 4DTs. This paper presents a simulation study to investigate the feasibility of FtG based on a control theoretic modeling of the taxiing system. The 4DT conformance errors with different navigation lamp control strategies are investigated. The key performance indices, including temporal constraint violation and fuel consumption, are analysed. The results demonstrate that it is feasible to follow conflict-free 4DTs through FtG if an appropriate lamp controller is available. The results also highlight the need to proactively handle the potential conformance errors in the routing and scheduling stage.
... Several studies have been carried out for the aircraft taxi path optimization problem. Weiszer [10] introduced the speed curve to optimize the taxiing speeds of aircraft and constructed a model with taxiing time and fuel consumption as the optimization objective to construct the model. The experimental results showed that this method could further reduce the taxiing cost without increasing the computation time. ...
Article
Full-text available
Aircraft taxiing emissions are the main source of carbon dioxide and other pollutant gas emissions during airport ground operations. It is crucial to optimize aircraft taxiing from both spatial and temporal perspectives to improve airport operation efficiency and reduce aviation emissions. In this paper, a bilevel spatial and temporal optimization model of aircraft taxiing is constructed. The upper-level model optimizes the aircraft taxiing path, and the lower-level model optimizes the taxiing start time of the aircraft. By the iterative optimization of the upper- and lower-level interactions, the aviation fuel consumption, flight waiting time, and number of taxiing conflicts are reduced. To improve the calculation accuracy, the depth-first search algorithm is utilized to generate the set of available paths for aircraft during the model solution process, and a model solution method based on the genetic algorithm is constructed. Simulation experiments using Tianjin Binhai International Airport as the research object show that adopting the waiting taxiing strategy can effectively avoid taxiing conflicts and reduce aviation fuel consumption by 753.18 kg and 188.84 kg compared to the available path sets generated using Dijkstra’s algorithm and those created manually based on experience, respectively. Conversely, adopting an immediate taxi-out strategy caused 54 taxiing conflicts and increased aviation fuel consumption by 49.44 kg. These results can provide safe and environmentally friendly taxiing strategies for the sustainable development of the air transportation industry.
... As an important location for the outfow and infow of air trafc, the AS operation has various constraints, such as release time, available apron, and runway assignment, which cause trafc congestion. Studies on the trafc congestion on ASs have mainly focused on the queuing problem connecting the apron, taxiway, and runway [1][2][3]; fight delays [4][5][6][7]; and taxiing management [8][9][10]. Yang et al. discussed the congestion of departing trafc fow but ignored the infuence of arriving trafc fow [11]. ...
Article
Full-text available
A multilayer network approach to model and analyze air traffic networks is proposed. These networks are viewed as complex systems with interactions between airports, airspaces, procedures, and air traffic flows (ATFs). A topology-based airport-airspace network and a flight trajectory network are developed to represent critical physical and operational characteristics. A multilayer traffic flow network and an interrelated traffic congestion propagation network are also formulated to represent the ATF connection and congestion propagation dynamics, respectively. Furthermore, a set of analytical metrics, including those of airport surface (AS), terminal controlled airspace (TCA), and area-controlled airspace (ACA), is introduced and applied to a case study in central and south-eastern China. The empirical results show the existence of a fundamental diagram of the airport, terminal, and intersections of air routes. Moreover, the dynamics and underlying mechanisms of congestion propagation through the AS-TCA-ACA network are revealed and interpreted using the classical susceptible-infectious-removed model in a hierarchical network. Finally, a high propagation probability among adjacent terminals and a high recovery probability are identified at the network system level. This study provides analytical tools for comprehending the complex interactions among air traffic systems and identifies future developments and automation of layered coupled air traffic management systems.
... The mainly terms of relevant theoretical research are as followed: the airport operation scheduling capability is improved through the flight area scheduling optimization [4][5][6][7][8][9], the aircraft taxiing route optimization [10][11][12][13], reducing emissions [14][15][16][17][18]and the conflict detection method optimization [19][20][21][22][23][24]. Zhang et al. [25] provided a consistent method to improve the methods of determining unimpeded taxiing time, which is the reference time used for estimating taxiing delay. ...
Preprint
Aircraft taxiing conflict is a threat to the safety of airport operations, mainly due to the human error in control command infor-mation. In order to solve the problem, The aircraft taxiing deduction and conflict early warning method based on control order information is proposed. This method does not need additional equipment and operating costs, and is completely based on his-torical data and control command information. When the aircraft taxiing command is given, the future route information will be deduced, and the probability of conflict with other taxiing aircraft will be calculated to achieve conflict detection and early warning of different levels. The method is validated by the aircraft taxi data from real airports. The results show that the method can effectively predict the aircraft taxiing process, and can provide early warning of possible conflicts. Due to the advantages of low cost and high accuracy, this method has the potential to be applied to airport operation decision support system.
... baggage unloading, freight unloading, etc) which reduced the waiting time for passengers and also reduced the cost of ground handlers due to idle capacity (37). Furthermore, the data collected in the airside can be further processed using algorithms to optimize airside operations such as improving the ecological impact of de-icing practices in airports, minimizing taxiing time and fuel consumption of taxiing aircraft, etc (53,54). Airside data can also be used to develop metrics such as runway utilization, average taxi-out times and departure spacing efficiency to assess and quantify airport performance, thereby generating insights for improvements (55). ...
Preprint
Full-text available
Airports have been constantly evolving and adopting digital technologies to improve operational efficiency, enhance passenger experience, generate ancillary revenues and boost capacity from existing infrastructure. The COVID-19 pandemic has also challenged airports and aviation stakeholders alike to adapt and manage new operational challenges such as facilitating a contactless travel experience and ensuring business continuity. Digitalisation using Industry 4.0 technologies offers opportunities for airports to address short-term challenges associated with the COVID-19 pandemic while also preparing for future long-term challenges that ensue the crisis. Through a systematic literature review of 102 relevant articles, we discuss the current state of adoption of Industry 4.0 technologies in airports, the associated challenges as well as future research directions. The results of this review suggest that the implementation of Industry 4.0 technologies is slowly gaining traction within the airport environment, and shall continue to remain relevant in the digital transformation journeys in developing future airports.
... Algorithms in this category mostly address the minimization of taxi time [4,11] and do not actively or directly optimize fuel efficiency and related emissions. A few exceptions, such as the active routing and scheduling algorithm [5,12], consider fuel consumption and emissions directly. However, the calculation is based on simplified equations of aircraft motion and fuel and emissions models. ...
Article
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Aircraft ground movement plays a key role in improving airport efficiency, as it acts as a link to all other ground operations. Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations. Moreover, autonomous taxiing is envisioned as a key component in future digitalized airports. However, state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases. The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions. This paper proposes a new approach for generating efficient four-dimensional trajectories (4DTs) on the basis of a high-fidelity aircraft model and gain-scheduling control strategy. Working in conjunction with a routing and scheduling algorithm that determines the taxi route, waypoints, and time deadlines, the proposed approach generates fuel-efficient 4DTs in real time, while respecting operational constraints. The proposed approach can be used in two contexts: ① as a reactive decision support tool to generate new trajectories that can resolve unprecedented events; and ② as an autopilot system for both partial and fully autonomous taxiing. The proposed methodology is realistic and simple to implement. Moreover, simulation studies show that the proposed approach is capable of providing an up to 11% reduction in the fuel consumed during the taxiing of a large Boeing 747 jumbo jet.
... In recent years, the environmental impact of the ground movements has gained attention among the researchers, leading to the development of optimization models that explicitly consider the fuel consumption during the optimization process. Weiszer et al. [15] proposed a heuristic approach for the speed profile optimization of the family of k-best taxi routes generated using the k-QPPTW algorithm; this approach has been improved for real-time application by using a pre-computed database of optimized speed profiles for taxi route building blocks [16]. A multi-objective speed profile optimization algorithm for a trade-off between taxi time and fuel consumption is presented in [17]; two different models are proposed for the fuel consumption evaluation: a method based on ICAO emission database and a method based on the base of aircraft data (BADA). ...
Article
Full-text available
The conflicting needs of meeting the growth of air traffic, mitigating the resulting additional air pollution, and reducing costs derived from congestion at airports will push toward the modernization of ground operations and airport management. In this framework, a new solution is proposed to perform just-in-time taxi operations using autonomous electric towbarless tractors. The purpose of this solution was to eliminate queues and to reduce the environmental and economic impacts of ground operations so as to meet the requirements for future air traffic management. A hybrid particle swarm optimization algorithm is developed to provide conflict-free schedules for tractor autopilots. To improve the rate of convergence of the algorithm, a parameter-fixing algorithm has been developed, which constrains the particle elements based on the particle history.
... The Roling PC built a MILP model based on a spatiotemporal network, allowing the aircraft to wait for collisions at the stand and special points, aimed at the minimum total taxi time and total waiting time, and ensuring that no conflicts occur within each planned time window [5]. Weiszer M considered the fuel consumption and environmental impact, introduces the aircraft speed curve, as well as adopts the external database method to reduce the calculation time to meet the real-time operation requirements of the airport [6]. Authors in Guepet et al.(2016) set the objective of taxi scheduling to be minimizing the emission by aircrafts' surface movement which shows the concerning about the environmental protection [7]. ...
Article
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Joint optimization of runway and taxiway will help increase the usage rate of airport’s existing hardware and software resources and ease flight delays. Firstly, the paper considers the relevant regulations of taxiing and the constraints of runway release interval, and takes the shortest total taxi time of each flight as the objective function to construct a joint optimization model based on the basic element layout of the airport. Secondly, the A* algorithm is improved for its characteristics and the actual situation of the airport surface, and the optimization model is solved by this improved method. Finally, taking Nanjing Lukou International Airport as an example, the optimal solution obtained by improved A* algorithm is compared with the actual running data to verify the applicability of the model.
... Liang et al. (2015) solved two closely related airline planning problems, i.e., the robust weekly aircraft maintenance routing problem and the tail assignment problem, in order to minimize the total expected propagated delay of the aircraft routes. Weiszer et al. (2015) developed a real-time active routing approach via a database for airport surface movement. Maher et al. (2014) investigated the single day aircraft maintenance routing problem by applying the recoverable robustness framework and the Pareto-optimal approach. ...
Conference Paper
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Aviation or air transportation refers to the activities surrounding mechanical flights in the airlines and the aircraft industries. In this paper, we present a recent literature survey on aviation management. The literature review is classified into the following main categories: Airline Capacity Analysis; Air Traffic Flow Management; Airline Fleet Assignment; Tail Assignment with Aircraft Maintenance Routing; Airline Crew Pairing; Airline Recovery and Rescheduling; Airline Revenue Management; Collaborative Decision Making; Aircraft Scheduling. This classification aims to motivate the researchers and practitioners in aviation management to develop more applicable, realistic and wide-ranging optimization methodologies for meeting the current needs of aviation industry.
... Liang et al. (2015) solved two closely related airline planning problems, i.e., the robust weekly aircraft maintenance routing problem and the tail assignment problem, in order to minimize the total expected propagated delay of the aircraft routes. Weiszer et al. (2015) developed a real-time active routing approach via a database for airport surface movement. Maher et al. (2014) investigated the single day aircraft maintenance routing problem by applying the recoverable robustness framework and the Pareto-optimal approach. ...
Conference Paper
Full-text available
Aviation or air transportation refers to the activities surrounding mechanical flights in the airlines and the aircraft industries. In this paper, we present a recent literature survey on aviation management. The literature review is classified into the following main categories: Airline Capacity Analysis; Air Traffic Flow Management; Airline Fleet Assignment; Tail Assignment with Aircraft Maintenance Routing; Airline Crew Pairing; Airline Recovery and Rescheduling; Airline Revenue Management; Collaborative Decision Making; Aircraft Scheduling. This classification aims to motivate the researchers and practitioners in aviation management to develop more applicable, realistic and wide-ranging optimization methodologies for meeting the current needs of aviation industry.
... Often the focus is on optimising taxi times, but other objectives have attracted some attention, particularly reducing aircraft emissions and fuel consumption due to taxiing [11,12,17,35]. An advantage of accurate and eicient taxi routing is that aircraft can be made to wait at the gate until the last possible minute before starting engines and pushing back, saving fuel that would otherwise be wasted waiting in a queue at the runway [4]. ...
Conference Paper
With increasing demand for air travel and overloaded airport facilities, inefficient airport taxiing operations are a significant contributor to unnecessary fuel burn and a substantial source of pollution. Although taxiing is only a small part of a flight, aircraft engines are not optimised for taxiing speed and so contribute disproportionately to the overall fuel burn. Delays in taxiing also waste scarce airport resources and frustrate passengers. Consequently, reducing the time spent taxiing is an important investment. An exact algorithm for finding shortest paths based on A* allocates routes to aircraft that maintains aircraft at a safe distance apart, has been shown to yield efficient taxi routes. However, this approach depends on the order in which aircraft are chosen for allocating routes. Finding the right order in which to allocate routes to the aircraft is a combinatorial optimization problem in itself. We apply a rolling window approach incorporating a genetic algorithm for permutations to this problem, for real-world scenarios at three busy airports. This is compared to an exhaustive approach over small rolling windows, and the conventional first-come-first-served ordering. We show that the GA is able to reduce overall taxi time with respect to the other approaches.
... This achieves more realistic trajectory modelling through curves and intersections. Speed profiles (Chen et al., 2016a) and Active Routing (Weiszer et al., 2015) also represent a path to more realistic routing. Khadilkar and Balakrishnan (2016) used a dynamic programming approach combined with a model drawing on taxi times, arrival airspace and departures to determine the optimal pushback times for aircraft waiting on their stands at Boston Logan International Airport. ...
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Allocating efficient routes to taxiing aircraft, known as the Ground Movement problem, is increasingly important as air traffic levels continue to increase. If taxiways cannot be reliably traversed quickly, aircraft can miss valuable assigned slots at the runway or can waste fuel waiting for other aircraft to clear. Efficient algorithms for this problem have been proposed, but little work has considered the uncertainties inherent in the domain. This paper proposes an adaptive Mamdani fuzzy rule based system to estimate taxi times and their uncertainties. Furthermore, the existing Quickest Path Problem with Time Windows (QPPTW) algorithm is adapted to use fuzzy taxi time estimates. Experiments with simulated taxi movements at Manchester Airport, the third-busiest in the UK, show the new approach produces routes that are more robust, reducing delays due to uncertain taxi times by 10–20% over the original QPPTW.
... Keywords: Aviation, airside operations, meta-heuristics, literature review, classification framework order, complex and stochastic combinatorial problem, or even real-life CO applications [10]. Heuristics is regarded as basic approximate algorithms for providing near optimal results [9,11]. However, the design of heuristics is problem-specific and problem-dependent methods. ...
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... In recent years, the environmental impact of ground movements has gained more attention, leading to the development of optimization models that explicitly consider the fuel consumption during the optimization process, with the final goal of having a better estimate of the fuel burn relative to the simply time proportionality model. Weiszer et al. [21] proposed a heuristic approach aiming at the optimization of the speed profile for the family of k-best taxi routes, generated using the k-QPPTW algorithm; this approach has been improved for real-time applications using a precomputed database of optimized speed profiles, for taxi route building blocks [22]. A multi-objective speed profile optimization algorithm for a trade-off between taxi time and fuel consumption is presented in [23]; two different models are proposed for the fuel consumption evaluation: one based on the international civil aviation organization (ICAO) emission database, and one that exploits the Eurocontrol base of aircraft data (BADA). ...
Thesis
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The optimization of airport operations has gained increasing interest by the aeronautical community, due to the substantial growth in the number of airport movements (landings and take-offs) experienced in the past decades all over the world. Forecasts have confirmed this trend also for the next decades. The result of the expansion of air traffic is an increasing congestion of airports, especially in taxiways and runways, leading to additional amount of fuel burnt by airplanes during taxi operations, causing additional pollution and costs for airlines. In order to reduce the impact of taxi operations, different solutions have been proposed in literature; the solution which this dissertation refers to uses autonomous electric vehicles to tow airplanes between parking lots and runways. Although several analyses have been proposed in literature, showing the feasibility and the effectiveness of this approach in reducing the environmental impact, at the beginning of the doctoral activity no solutions were proposed, on how to manage the fleet of unmanned vehicles inside the airport environment. Therefore, the research activity has focused on the development of algorithms able to provide pushback tractor (also referred as tugs) autopilots with conflict-free schedules. The main objective of the optimization algorithms is to minimize the tug energy consumption, while performing just-in-time runway operations: departing airplanes are delivered only when they can take-off and the taxi-in phase starts as soon as the aircraft clears the runway and connects to the tractor. Two models, one based on continuous time and one on discrete time evolution, were developed to simulate the taxi phases within the optimization scheme. A piecewise-linear model has also been proposed to evaluate the energy consumed by the tugs during the assigned missions. Furthermore, three optimization algorithms were developed: two hybrid versions of the particle swarm optimization and a tree search heuristic. The following functional requirements for the management algorithm were defined: the optimization model must be easily adapted to different airports with different layout (reconfigurability); the generated schedule must always be conflict-free; and the computational time required to process a time horizon of 1h must be less than 15min. In order to improve its performance, the particle swarm optimization was hybridized with a hill-climb meta-heuristic; a second hybridization was performed by means of the random variable search, an algorithm of the family of the variable neighborhood search. The neighborhood size for the random variable search was considered varying with inverse proportionality to the distance between the actual considered solution and the optimal one found so far. Finally, a tree search heuristic was developed to find the runway sequence, among all the possible sequences of take-offs and landings for a given flight schedule, which can be realized with a series of taxi trajectories that require minimum energy consumption. Given the taxi schedule generated by the aforementioned optimization algorithms a tug dispatch algorithm, assigns a vehicle to each mission. The three optimization schemes and the two mathematical models were tested on several test cases among three airports: the Turin-Caselle airport, the Milan-Malpensa airport, and the Amsterdam airport Schiphol. The cost required to perform the generated schedules using the autonomous tugs was compared to the cost required to perform the taxi using the aircraft engines. The proposed approach resulted always more convenient than the classical one.
... Recently, the research on airport ground movement has started to take into account a speed profile optimisation problem so that not only time efficiency but also fuel saving and decrease in airport emissions can be achieved at the same time [12]. This problem is difficult to solve due to its computational load. ...
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... f) Currently, calculating the efficient speed profiles and integrating them into the routing and scheduling is extremely computational demanding and is not suitable for on-line decision support, thus it is worth exploring some pre-processing techniques to reduce the complexity of the airport taxiway layout so that complete efficient speed profiles for this reduced set can be pre-calculated and stored in a database; this is envisioned as the key to bring the proposed AR framework up to on-line decision support. The preliminary results in [56] using such an approach indicate that fast computational time is achievable. g) There is currently a lack of accurate fuel estimation models for airport ground operations, however, with the aircraft engine performance data and fuel consumption data logged by airlines through the flight radar recorders, the proposed AR framework could be calibrated and serve as the airport ground fuel estimation tool. ...
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In this paper an efficient Medium Term Conflict Detection and Resolution (MTCD&R) approach based on 4D trajectories to solve conflicts in a Terminal Maneuvering Area (TMA) is presented. The conflict detection subsystem (CD) is based on a Spatial Data Structure (SDS), avoiding non-efficient pairwise trajectory comparisons, and using a simplified wake vortex modeling through 4D tubes to detect time-based separation infringements between aircraft. The conflict resolution subsystem (CR) solves the detected conflicts with an efficient and dynamic 3D allocation of the arrival routes that takes into consideration the execution of Continuous Descent Approaches (CDAs). Algorithms have been tested with several stressing traffic scenarios (rush hour and saturation rush hour) taking place in a 3D simulation model of Gran Canaria Extended TMA. The resulting conflict-free trajectories have been validated for flyability conformance both with real A380 FMS avionics and with a certified B738 Full Flight Simulator. A new CR performance metric to measure the degree of runway utilization is also proposed in order to enable comparisons between different MTCD&R systems. Finally, a discussion about strengths and limitations of the algorithms for reducing controller's workload while increasing airspace capacity of the future Single European Sky is outlined. (c) 2012 Elsevier Ltd. All rights reserved.
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A ring R with identity is called strongly clean if every element of R is the sum of an idempotent and a unit that commute. Local rings are strongly clean. It is unknown when a matrix ring is strongly clean. However it is known from [J. Chen, X. Yang, Y. Zhou, On strongly clean matrix and triangular matrix rings, preprint, 2005] that for any prime number p, the 2×22×2 matrix ring M2(Zˆp) is strongly clean where Zˆp is the ring of p-adic integers, but M2(Z(p))M2(Z(p)) is not strongly clean where Z(p)Z(p) is the localization of ZZ at the prime ideal generated by p. Let R be a commutative local ring. A criterion in terms of solvability of a simple quadratic equation in R is obtained for M2(R)M2(R) to be strongly clean. As consequences, M2(R)M2(R) is strongly clean iff M2(R〚x〛)M2(R〚x〛) is strongly clean iff M2(R[x]/(xn))M2(R[x]/(xn)) is strongly clean iff M2(RC2)M2(RC2) is strongly clean.
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Nature is the international weekly journal of science: a magazine style journal that publishes full-length research papers in all disciplines of science, as well as News and Views, reviews, news, features, commentaries, web focuses and more, covering all branches of science and how science impacts upon all aspects of society and life.
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To reduce fuel consumption in the transportation sector research focuses mainly on the development of more efficient drive train technologies and alternative drive train designs. Another and immidiately applicable way found to reduce fuel consumption in road vehicles is to change vehicle operation such that system efficiency is maximized. The concept of Eco- driving refers to the change of driver behavior in a fuel saving way or more generally in an energy saving way. In this paper system efficiency of a vehicle is optimized using a dynamic programming optimization approach. Given a drive cycle a so called 'eco-drive cycle' is identified in which a vehicle performs the same distance with the same stops in equivalent time, while consuming less fuel.
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The article describes and analyzes NAMOA*, an algorithm for multiobjective heuristic graph search problems. The algorithm is presented as an extension of A*, an admissible scalar shortest path algorithm. Under consistent heuristics A* is known to improve its efficiency with more informed heuristics, and to be optimal over the class of admissible algorithms in terms of the set of expanded nodes and the number of node expansions. Equivalent beneficial properties are shown to prevail in the new algorithm.
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A flow-based modeling approach is proposed to identify candidate airspace for high-density flow corridors. The input to the model is a set of projected user-preferred, wind optimal, and unconstrained 4D trajectories (4DT). We compute Velocity Vector Fields (VVF) in the 4D space-time and cluster the velocity vectors both in time and space to define flow of aircraft when they fly their preferred trajectories under high capacity conditions. A sliding time window is implemented to dynamically create and optimize corridors' coordinates based on the changes in preferred trajectories. From this process we compute a NAS-wide corridor network that mimics the dynamics of user preferred trajectories. In operational setting, flights will have the option of joining a corridor that is closest to their optimal trajectory. Using NAS-wide simulation, we asses the benefit of corridor network by comparing efficiency gained by joining the corridor network against extra distance traveled to join the network. We show that much of the overall corridors benefit may be gained by creating very few corridors.
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We study the problem of finding a shortest path between two vertices in a directed graph. This is an important problem with many applications, including that of computing driving directions. We allow preprocessing the graph using a linear amount of extra space to store auxiliary information, and using this information to answer shortest path queries quickly. Our approach uses A * search in combination with a new graph-theoretic lower-bounding technique based on landmarks and the triangle inequality. We also develop new bidirectional variants of A * search and investigate several variants of the new algorithms to find those that are most efficient in practice. Our algorithms compute optimal shortest paths and work on any directed graph. We give experimental results showing that the most efficient of our new algorithms outperforms previous algorithms, in particular A * search with Euclidean bounds, by a wide margin on road networks. We also experiment with several synthetic graph families.
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We present a new speedup technique for route planning that exploits the hierarchy inherent in real world road networks. Our algorithm preprocesses the eight digit number of nodes needed for maps of the USA or Western Europe in a few hours using linear space. Shortest (i.e. fastest) path queries then take around eight milliseconds to produce exact shortest paths. This is about 2000 times faster than using Dijkstra’s algorithm.
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The evaluation of aerospace designs is synonymous with the use of long running and computationally intensive simulations. This fuels the desire to harness the efficiency of surrogate-based methods in aerospace design optimization. Recent advances in surrogate-based design methodology bring the promise of efficient global optimization closer to reality. We review the present state of the art of constructing surrogate models and their use in optimization strategies. We make extensive use of pictorial examples and, since no method is truly universal, give guidance as to each method's strengths and weaknesses.
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The primary objective of this paper is to put forward a general framework under which clear definitions of immune operators and their roles are provided. To this aim, a novel Population Adaptive Based Immune Algorithm (PAIA) inspired by Clonal Selection and Immune Network theories for solving multi-objective optimization problems (MOP) is proposed. The algorithm is shown to be insensitive to the initial population size; the population and clone size are adaptive with respect to the search process and the problem at hand. It is argued that the algorithm can largely reduce the number of evaluation times and is more consistent with the vertebrate immune system than the previously proposed algorithms. Preliminary results suggest that the algorithm is a valuable alternative to already established evolutionary based optimization algorithms, such as NSGA II, SPEA and VIS.
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We present a route planning technique solely based on the concept of node contraction. The nodes are first ordered by ‘importance’. A hierarchy is then generated by iteratively contracting the least important node. Contracting a node v means replacing shortest paths going through v by shortcuts. We obtain a hierarchical query algorithm using bidirectional shortest-path search. The forward search uses only edges leading to more important nodes and the backward search uses only edges coming from more important nodes. For fastest routes in road networks, the graph remains very sparse throughout the contraction process using rather simple heuristics for ordering the nodes. We have five times lower query times than the best previous hierarchical Dijkstra-based speedup techniques and a negative space overhead, i.e., the data structure for distance computation needs less space than the input graph. CHs can be combined with many other route planning techniques, leading to improved performance for many-to-many routing, transit-node routing, goal-directed routing or mobile and dynamic scenarios.
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Algorithms for route planning in transportation networks have recently undergone a rapid development, leading to methods that are up to one million times faster than Dijkstra's algorithm. We outline ideas, algorithms, implementations, and experimental methods behind this development. We also explain why the story is not over yet because dynamically changing networks, flexible objective functions, and new applications pose a lot of interesting challenges.
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Up to now, research on speed-up techniques for Dijkstra’s algorithm focused on single-criteria scenarios. The goal was to find the quickest route within a transportation network. However, the quickest route is often not the best one. A user might be willing to accept slightly longer travel times if the cost of the journey is less. A common approach to cope with such a situation is to find Pareto-optimal (concerning other metrics than travel times) routes. Such routes have the property that each route is better than any other route with respect to at least one metric under consideration, e.g., travel costs or number of train changes. In this work, we study multi-criteria search in road networks. On the one hand, we focus on the problem of limiting the number of Pareto paths. On the other hand, we present a multi-criteria variant of our recent SHARC algorithm.