Jean-Baptiste Gotteland

Genetic algorithms applied to airport ground traffic optimization

Due to air traffic growth and especially hubs development, major European airports can easily become bottlenecks in the global air transportation network. Therefore, accurate models of airport traffic prediction become more and more necessary for ground controllers. In this paper, a ground traffic simulation tool is proposed and applied to Roissy Charles De Gaulle airport. Two global optimization methods, using genetic algorithms at the airport, are developed to minimize taxing time while respecting separation and runway capabilities. In order to compare the efficiency of the different methods, simulations are carried out on a one day traffic sample, and ground delay is correlated to the traffic density at the airport.

Finding and proving the optimum: cooperative stochastic and deterministic search

In this article, we introduce a global cooperative approach between an Interval Branch and Bound Algorithm and an Evolutionary Algorithm, that takes advantage of both methods to optimize a function for which an inclusion function can be expressed. The Branch and Bound algorithm deletes whole blocks of the search space whereas the Evolutionary Algorithm looks for the optimum in the remaining space and sends to the IBBA the best evaluation found in order to improve its Bound. The two algorithms run independently and update common information through shared memory. The cooperative algorithm prevents premature and local convergence of the evolutionary algorithm, while speeding up the convergence of the branch and bound algorithm. Moreover, the result found is the proved global optimum. In part 1, a short background is introduced. Part 2.1 describes the basic Interval Branch and Bound Algorithm and part 2.2 the Evolutionary Algorithm. Part 3 introduces the cooperative algorithm and part 4 gives the results of the algorithms on benchmark functions. The last part concludes and gives suggestions of avenues of further research.

Genetic Algorithms Applied to Air Traffic Management

The constant increase in air traffic, since the beginning of the commercial aviation, has led to problems of saturation on airports, approaching areas, or higher airspace.Whereas the aircraft are largely optimized and automated, the air traffic control is still essentially relying on human experience. The present case study details two problems of air traffic management (ATM) for which a genetic algorithm based solution has been proposed. The first application deals with the en route conflict resolution problem. The second application deals with the traffic management problem in an airport platform.

Preventing Premature Convergence and Proving the Optimality in Evolutionary Algorithms

Evolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their exponential complexity and their inability to quickly compute a good approximation of the global minimum. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a branch and bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and is highly competitive with both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality.

Metaheuristics for Air Traffic Management

Air Traffic Management involves many different services such as Airspace Management, Air Traffic Flow Management and Air Traffic Control. Many optimization problems arise from these topics and they generally involve different kinds of variables, constraints, uncertainties. Metaheuristics are often good candidates to solve these problems. The book models various complex Air Traffic Management problems such as airport taxiing, departure slot allocation, en route conflict resolution, airspace and route design. The authors detail the operational context and state of art for each problem. They introduce different approaches using metaheuristics to solve these problems and when possible, compare their performances to existing approaches

Visualizing complexities: the human limits of air traffic control

Air traffic management is organized into filters in order to prevent tactical controllers from dealing with complex conflicting situations. In this article, we describe an experiment showing that a dynamic conflict display could improve human performance on complex conflict situations. Specifically, we designed a display tool that represents the conflicting portions of aircraft trajectories and the evolution of the conflict zone when the user adds a maneuver to an aircraft. The tool allows the user to dynamically check the potential conflicting zones with the computer mouse before making a maneuver decision. We tested its utility on a population of forty students: twenty air traffic controller (ATC) students at the end of their initial training and twenty engineering students with the same background but no ATC training. They had to solve conflicts involving 2–5 aircraft with a basic display and with the dynamic visualization tool. Results show that in easy situations (2 aircraft), performance is similar with both displays. However, as the complexity of the situations grows (from 3 to 5 aircraft), the dynamic visualization tool enables users to solve the conflicts more efficiently. Using the tool leads to fewer unsolved conflicts and shorter delays. No significant differences are found between the two test groups except for delays: ATC students give maneuvers that generate less delays than engineering students. These results suggest that humans are better able to manage complex situations with the help of our conflict visualization tool.

Applications to Air Traffic Management

Air traffic management (ATM) is an endless source of challenging optimization problems. Before discussing applications of metaheuristics to these problems, let us describe an ATM system in a few words, so that readers who are not familiar with such systems can understand the problems being addressed in this chapter. Between the moment passengers board an aircraft and the moment they arrive at their destination, a flight goes through several phases: push back at the gate, taxiing between the gate and the runway threshold, takeoff and initial climb following a Standard instrument departure (SID) procedure, cruise, final descent following standard terminal arrival route (STAR), landing on the runway, and taxiing to the gate. During each phase, the flight is handled by several air traffic control organizations: airport ground control, approach and terminal control, en-route control. These control organizations provide services that ensure safe and efficient conduct of flights, from departure to arrival.

Hybridization of Interval CP and Evolutionary Algorithms for Optimizing Difficult Problems

The only rigorous approaches for achieving a numerical proof of optimality in global optimization are interval-based methods that interleave branching of the search-space and pruning of the subdomains that cannot contain an optimal solution. State-of-the-art solvers generally integrate local optimization algorithms to compute a good upper bound of the global minimum over each subspace. In this document, we propose a cooperative framework in which interval methods cooperate with evolutionary algorithms. The latter are stochastic algorithms in which a population of candidate solutions iteratively evolves in the search-space to reach satisfactory solutions. Within our cooperative solver Charibde, the evolutionary algorithm and the interval-based algorithm run in parallel and exchange bounds, solutions and search-space in an advanced manner via message passing. A comparison of Charibde with state-of-the-art interval-based solvers (GlobSol, IBBA, Ibex) and NLP solvers (Couenne, BARON) on a benchmark of difficult COCONUT problems shows that Charibde is highly competitive against non-rigorous solvers and converges faster than rigorous solvers by an order of magnitude.