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Departure Management is responsible for creating a departure sequence of flights and for deciding which aircraft will takeoff firstly in scenarios of cancellation or delay. In many cases, this activity depends only on the experience of air traffic controllers who will empirically decide the departure sequence. This work presents two computational m...
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Context 1
... flight only can belong to one slot, or else the flight would departure at two different slots. Each flight is identified by its flight number and its airline, Figure 1 shows an example with 6 genes (6 flights) and their 6 positions(6 slots). Chromosome Each chromosome represents a possible solution to a problem, it is composed by several genes. ...
Context 2
... Each chromosome represents a possible solution to a problem, it is composed by several genes. In this solution, the chromosome is formed by a number array, where each element of the array is a flight that belongs to a airline, Figure 1 shows an example of a chromosome string. If we choose a 4 hour interval to study, with a 1 minute slot size, each chromosome will have 240 slots. ...
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Citations
... The research in [10] was set in Brazilian airspace, where the departure management was done empirically by the air traffic controllers, negotiating with the airlines and then generating an First-Come First-Served (FCFS) sequence. ...
... Strings that form possible solutions are called chromosomes, the used alphabets are called genes. [10] The Genetic Algorithm was used to work with a population of 1000 possible flight departure sequence solutions. In the approach, 100 generations were executed and the tests occurred simulating flight departures during normal and high flow schedules. ...
The Collaborative Trajectory Options Program (CTOP) makes each airline possible to share its route options to air traffic control center, and so achieve better business goals by reducing strategic operational costs. In Brazil, there are initial efforts to verify the benefits of CTOP implementation to improve the air traffic fluency and financial results. This paper presents a novel approach for Brazilian airspace using Genetic Algorithms to decrease the delay between available slots during CTOP. The slot optimization keeps improving in a safety-separating window of each aircraft en route. The case study presented an reducement about 70% of delay of a certain airline, when used this decision support system by air traffic control authority.
... In classic evolutionary algorithms such as Genetic Algorithms, the population evolves through insertion of mutations and crossovers. The fittest individuals last longer whilst the weaker ones are obliterated as life cycles goes by [18]. In PSO, all population members remain active until the end of processing and the interactions among them improves the quality of problem solutions over time [19]. ...
The main goal in Continuous Descent Arrival (CDA) operations is the management of flight trajectories in
order to optimize the operating capability of the aircraft in Terminal Manoeuvring Area (TMA), reducing fuel burn and emissions during the descent/approach phase. At the same time the maximization of airspace efficiency/capacity needs to be addressed considering local airspace requirements and constraints.
The arrival coordination task can be viewed as a scenario in which several aircraft approach at a common merging point within optimum time windows, ensured by ATC in order to eliminate en-route conflicts with an adequate sequencing of the arrivals. It is proposed a 4D-compliant framework that integrates the individual performance preferences of the landing aircraft and the ATC arrival management procedures. The ATC
agent should use a particle swarm optimization algorithm in order to build valid, safe arrival sequencing that decreases and distributes the overall operational costs among the aircraft in a fair and efficient manner. Using actual data from Brasilia International Airport (SBBR) to evaluate the effectiveness of the proposed modeling, a case study shows that 77% of the flights are able to accomplish their desired time window flying a CDA procedure.
... In Figure 2, we present an example on how this optimization would work on Brazilian air space. Since the Middleware connects each part of the flight to differente optimizations, we can, for example, use Genetic Algorithms to optimize the taxiing (Pessoa et al., 2013), Game Theory to optimize departures (Ferreira et al., 2014) and several other possibilities in just one platform, connecting all these optimizations. The first necessary configuration for this middleware is determining the airport that will be optimized, with this information the middleware will have access to the entire structure of the airport: taxi routes, lanes, location of gates, runways, etc. ...
TRIZ (the theory of inventive problem solving; an acronym for the Russian phrase "Teoriya Resheniya Izobretatelskikh Zadatch") is a broad title representing a collection of methodologies, tools, knowledge bases, and model-based technologies for generating inventive ideas and solutions. It has been developed for over 65 years to support engineers and natural scientists solving problems in the fields of physics, chemistry and mechanical systems by using knowledge of former inventors.
In today business environment, new technologies and competitions force business organizations to invest additional resources in order to continuously improve their business models, products, processes, services, management, information system and knowledge of employees. As a part of these efforts, appears an exigency for strengthening the importance of inventive solving problems capabilities in area of software engineering, supporting innovative aspects of business organizations in extremely changing environment.
Although the software engineering (SE) is “a branch of computer science that deals with the design, implementation, and maintenance of complex computer programs”, the existing research shows that only a part of the available TRIZ toolkit is applicable to this specific field. The current literature records numerous attempts made in different directions in order to fulfil a prominence need for adjustment, widening or upgrading existing TRIZ methodology to SE domain. This article synthetizes those efforts and proposes the development of semantic portal for open TRIZ in supporting collaborative software engineers` inventive problem solving process. As a part of this development process, authors have offered a solution for facilitating the ontology acquisition activities using inductive learning.
... AMAN systems must operate in conjunction with departure management (DMAN) systems. The integration between landing and departure systems is important in airport ground operations under the guidance of collaborative decision making (CDM) [9], [10]. ...
Collaborative Decision Making (CDM) is an operational paradigm in which decisions are based on complete, shared, and up-to-date information among all the stakeholders involved in Air Traffic Flow Management (ATFM). Such stake-holders include air traffic controllers and airlines. However, in Brazil, these operations are still coordinated manually. To decide departure sequencing in airports, we propose a novel, collaborative approach, using the Game Theory, in which, each aircraft is represented as a player in the negotiation process for slot allocation. The Collaborative Departure Management system (CoDMAN) we propose here is designed to provide efficient departure sequencing, based on the negotiations among the aircrafts in a dynamic scenario, modelled under the Rubinstein protocol and CDM principles. A prototype of this system was used to simulate real-world scenarios, based on actual flight plans from the Brasilia Terminal Control Area (TMA). Using CoDMAN for departure sequencing reduces delays in arrivals/departures of aircraft.
... When compared this GA model against a Game Theory model, a reduction of 41% of total delay time is seen versus a reduction of just 16%, showing the powerful application of Genetic Algorithm. Mendes et al. (2014). A mathematical model using B-spline and Genetic Algorithm to optimize air-traffic conflict resolution. ...
Transportation networks are dynamic systems that suffer continuous unexpected events. People in charge of managing them in an efficient way usually make their decisions based on their personal expertise, relevant data and the accumulated experience gained on the matter. However, the cognitive capacity of the human brain is limited, and therefore the design of the solution cannot take advantage of the extensive data available. Despite the fact that this paper deals with the specific transportation network of Airport management, the proven excellence and virtues of the application of Genetic Algorithms to support the decision-making process can be extended to any transport domain. This paper addresses an airport management scenario, in particular, the management of runway configuration changes. When wind forecasts lead to thoughts of the need to change the use of the existing runway configuration, the person in charge of making that decision has to do his best by stating when and how to execute the change. Concurrently, there are flights approaching the airport, flights taxiing to the runway holding points, while other flights are still on-block at their gates. The better the decision is, the less the related delay will be. Nevertheless, delay is not the only performance indicator that may interest an Airport Manager; other aspects such as fuel consumption, safety, capacity, predictability and so on may be determinant. Consequently the aforementioned aspects introduce a new and wider set of parameters to take into account when making the decision. In order to support this complex decision making process, an automated tool, based on genetic algorithms and called RUCCMAN (Runway Configuration Change MANager), has been designed and validated.
