Michal Weiszer

Toward a More Realistic, Cost-Effective, and Greener Ground Movement Through Active Routing—Part I: Optimal Speed Profile Generation

Among all airport operations, aircraft ground movement plays a key role in improving overall airport capacity as it links other airport operations. Moreover, ever-increasing air traffic, rising costs, and tighter environmental targets create pressure to minimize fuel burn on the ground. However, current routing functions envisioned in Advanced Surface Movement, Guidance and Control Systems almost exclusively consider the most time-efficient solution and apply a conservative separation to ensure conflict-free surface movement, sometimes with additional buffer times to absorb small deviations from the taxi times. Such an overly constrained routing approach may result in either a too tight planning for some aircraft so that fuel efficiency is compromised due to multiple acceleration phases, or performance could be further improved by reducing the separation and buffer times. In light of this, Parts I and II of this paper present a new Active Routing (AR) framework with the aim of providing a more realistic, cost-effective, and environmental friendly surface movement, targeting some of the busiest international hub airports. Part I of this paper focuses on optimal speed profile generation using a physics-based aircraft movement model. Two approaches based, respectively, on the Base of Aircraft Data and the International Civil Aviation Organization engine emissions database have been employed to model fuel consumption. These models are then embedded within a multiobjective optimization framework to capture the essence of different speed profiles in a Pareto optimal sense. The proposed approach represents the first attempt to systematically address speed profiles with competing objectives. Results reveal an apparent tradeoff between fuel burn and taxi times irrespective of fuel consumption modeling approaches. This will have a profound impact on the routing and scheduling and open the door for the new concept of AR discussed in Part II of this paper.

A heuristic approach to greener airport ground movement

Ever increasing air traffic, rising costs and tighter environmental targets create a pressure for efficient airport ground movement. Ground movement links other airport operations such as departure sequencing, arrival sequencing and gate/stand allocation and its operation can affect each of these. Previously, reducing taxi time was considered the main objective of the ground movement problem. However, this may conflict with efforts of airlines to minimise their fuel consumption as shorter taxi time may require higher speed and acceleration during taxiing. Therefore, in this paper a multi-objective multi-component optimisation problem is formulated which combines two components: scheduling and routing of aircraft and speed profile optimisation. To solve this problem an integrated solution method is adopted to more accurately investigate the trade-off between the total taxi time and fuel consumption. The new heuristic which is proposed here uses observations about the characteristics of the optimised speed profiles in order to greatly improve the speed of the graph-based routing and scheduling algorithm. Current results, using real airport data, confirm that this approach can find better solutions faster, making it very promising for application within on-line applications.

Toward a More Realistic, Cost-Effective, and Greener Ground Movement Through Active Routing: A Multiobjective Shortest Path Approach

This paper draws upon earlier work, which developed a multiobjective speed profile generation framework for unimpeded taxiing aircraft. Here, we deal with how to seamlessly integrate such efficient speed profiles into a holistic decision-making framework. The availability of a set of nondominated unimpeded speed profiles for each taxiway segment, with respect to conflicting objectives, has the potential to significantly impact upon airport ground movement research. More specifically, the routing and scheduling function that was previously based on distance, emphasizing time efficiency, could now be based on richer information embedded within speed profiles, such as the taxiing times along segments, the corresponding fuel consumption, and the associated economic implications. The economic implications are exploited over a day of operation, to take into account cost differences between busier and quieter times of the airport. Therefore, a more cost-effective and tailored decision can be made, respecting the environmental impact. Preliminary results based on the proposed approach show a 9%-50% reduction in time and fuel respectively for two international airports: Zurich and Manchester. The study also suggests that, if the average power setting during the acceleration phase could be lifted from the level suggested by the International Civil Aviation Organization, ground operations may simultaneously improve both time and fuel efficiency. The work described in this paper aims to open up the possibility to move away from the conventional distance-based routing and scheduling to a more comprehensive framework, capturing the multifaceted needs of all stakeholders involved in airport ground operations.

Optimal Speed Profile Generation for Airport Ground Movement with Consideration of Emissions

Emissions during the ground movement are mostly calculated based on International Civil Aviation Organisation (ICAO) emission databank. The fuel flow rate is normally assumed as a constant, hence the emission index. Therefore, no detailed discrimination of power settings during ground movement is considered to account for different emissions at different power settings. This may lead to a suboptimal and often unrealistic taxi planning. At the heart of the recently proposed Active Routing (AR) framework for airport ground movement is the unimpeded optimal speed profile generation, taking into account both time and fuel efficiency. However, emissions have not been included in the process of generating optimal speed profiles. Taking into account emissions in ground operations is not a trial task as not all emissions can be reduced on the same path of reducing time and fuel burn. In light of this, in this paper, a detailed analysis of three main emissions at the airports, viz. CO, Total Hydrocarbon (HC), and NOx, are carried out in order to obtain a minimum number of conflicting objectives for generating optimal speed profiles. The results show that NOx has a strong linear correlation with fuel burn across all aircraft categories. For the heavy aircraft, HC and CO should be treated individually apart from the time and fuel burn objectives. For medium and light aircraft, a strong correlation between HC, CO and time has been observed, indicating a reduced number of objectives will be sufficient to account for taxi time, fuel burn and emissions. The generated optimal speed profiles with consideration of different emissions will have impact on the resulted taxiing planning using the AR and also affect decisions regarding airport regulations.

Preference-Based Evolutionary Algorithm for Airport Runway Scheduling and Ground Movement Optimisation

As airports all over the world are becoming more congested together with stricter environmental regulations put in place, research on optimisation of airport surface operations started to consider both time and fuel related objectives. However, as both time and fuel can have a monetary cost associated with them, this information can be utilised as preference during the optimisation to guide the search process to a region with the most cost efficient solutions. In this paper, we solve the integrated optimisation problem combining runway scheduling and ground movement problem by using a multi-objective evolutionary framework. The proposed evolutionary algorithm is based on modified crowding distance and outranking relation which considers cost of delay and price of fuel. Moreover, the preferences are expressed in a such way, that they define a certain range in prices reflecting uncertainty. The preliminary results of computational experiments with data from a major airport show the efficiency of the proposed approach.

A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation

Recently, the long-term conflict avoidance approaches based on large-scale flights scheduling have attracted much attention due to their ability to provide solutions from a global point of view. However, current approaches which focus only on a single objective with the aim of minimizing the total delay and the number of conflicts, cannot provide controllers with variety of optional solutions, representing different tradeoffs. Furthermore, the flight track error is often overlooked in the current research. Therefore, in order to make the model more realistic, in this paper, we formulate the long-term conflict avoidance problem as a multi-objective optimization problem, which minimizes the total delay and reduces the number of conflicts simultaneously. As a complex air route network needs to accommodate thousands of flights, the problem is a large-scale combinatorial optimization problem with tightly coupled variables, which make the problem difficult to deal with. Hence, in order to further improve the search capability of the solution algorithm, a cooperative co-evolution (CC) algorithm is also introduced to divide the complex problem into several low dimensional sub-problems which are easier to solve. Moreover, a dynamic grouping strategy based on the conflict detection is proposed to improve the optimization efficiency and to avoid premature convergence. The well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D) is then employed to tackle each sub-problem. Computational results using real traffic data from the Chinese air route network demonstrate that the proposed approach obtained better non-dominated solutions in a more effective manner than the existing approaches, including the multi-objective genetic algorithm (MOGA), NSGAII, and MOEA/D. The results also show that our approach provided satisfactory solutions for controllers from a practical point of view.

A rolling window with genetic algorithm approach to sorting aircraft for automated taxi routing

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.