Genetic algorithm based on receding horizon control for arrival sequencing and scheduling

The concept of Receding Horizon Control (RHC) is introduced into Genetic Algorithm (GA) in this paper to solve the problem of Arrival Scheduling and Sequencing (ASS) at a busy hub airport. A GA based method is proposed for solving the dynamic ASS problem, and the focus is put on the methodology of integrating the RHC strategy into the GA for real-time implementations in a dynamic environment of air traffic control (ATC). Receding horizon and terminal penalty are investigated in depth as two key techniques of this novel RHC based GA. Simulation results show that the new method proposed in this paper is effective and efficient to solve the ASS problem in a dynamic environment. Key words: Receding Horizon Control, Genetic Algorithm, Air Traffic Contr

An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem

The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in air traffic control (ATC), which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS problem in the form of a permutation problem and proposes a new solution framework that makes the first attempt at using an ant colony system (ACS) algorithm based on the receding horizon control (RHC) to solve it. The resultant RHC-improved ACS algorithm for the ASS problem (termed the RHC-ACS-ASS algorithm) is robust, effective, and efficient, not only due to that the ACS algorithm has a strong global search ability and has been proven to be suitable for these kinds of NP-hard problems but also due to that the RHC technique can divide the problem with receding time windows to reduce the computational burden and enhance the solution's quality. The RHC-ACS-ASS algorithm is extensively tested on the cases from the literatures and the cases randomly generated. Comprehensive investigations are also made for the evaluation of the influences of ACS and RHC parameters on the performance of the algorithm. Moreover, the proposed algorithm is further enhanced by using a two-opt exchange heuristic local search. Experimental results verify that the proposed RHC-ACS-ASS algorithm generally outperforms ordinary ACS without using the RHC technique and genetic algorithms (GAs) in solving the ASS problems and offers high robustness, effectiveness, and efficienc

Genetic algorithm based on receding horizon control for real-time implementations in dynamic environments

This paper introduces the concept of Receding Horizon Control (RHC) to Genetic Algorithm (GA) for real-time implementations in dynamic environments. The methodology of the new GA is presented with the emphases on how to effectively integrate the RHC strategy by following some RHC practices in control engineering, particularly, how to choose the length of receding horizon and how to design terminal penalty. Simulation results show that, when the RHC based GA is applied in dynamic environments, both computational efficiency and performance are improved in comparison with existing GAs

A Framework of Point Merge-based Autonomous System for Optimizing Aircraft Scheduling in Busy TMA

International audienceIn this article we present recent work towards the development of an autonomous system with point merge (PM) that performs sequencing, merging and spacing for arrival aircraft in the busy terminal area. This autonomous arrival management system aims to safely solve the major arrival flight scheduling problems currently handled by human controllers. With PM, it has the potential to handle higher traffic demands without more workload on controllers, consequently increasing capacity and reducing delay. The main objective of this paper is to introduce the framework of this autonomous system with PM. Based on analysis of classic PM route structure, a novel PM-based route network is firstly designed for Beijing Capital International Airport. Vertically, this PM system consists of multi-layers on the sequencing legs for different categories of aircraft with Heavy and Medium, horizontally, it is shaped as a lazy “8”. Then, a multiple-objectives function is discussed for this aircraft scheduling problem, operational constraints and conflict detection and resolution are analysed in detail, a modelling strategy with sliding time window and simulated annealing algorithm is proposed for solving this real-time dynamic problem. Experimental results verify our algorithm is well adapting the high-density traffic optimisation, and finally a conclusion is made and future work is pointed ou

Multiairport capacity management: genetic algorithm with receding horizon

The inability of airport capacity to meet the growing air traffic demand is a major cause of congestion and costly delays. Airport capacity management (ACM) in a dynamic environment is crucial for the optimal operation of an airport. This paper reports on a novel method to attack this dynamic problem by integrating the concept of receding horizon control (RHC) into a genetic algorithm (GA). A mathematical model is set up for the dynamic ACM problem in a multiairport system where flights can be redirected between airports. A GA is then designed from an RHC point of view. Special attention is paid on how to choose those parameters related to the receding horizon and terminal penalty. A simulation study shows that the new RHC-based GA proposed in this paper is effective and efficient to solve the ACM problem in a dynamic multiairport environment

管制運用に向けた航空交通流過密状態における進化計算に基づく着陸順最適化

電気通信大学201

Behavioral Learning of Aircraft Landing Sequencing Using a Society of Probabilistic Finite State Machines

Air Traffic Control (ATC) is a complex safety critical environment. A tower controller would be making many decisions in real-time to sequence aircraft. While some optimization tools exist to help the controller in some airports, even in these situations, the real sequence of the aircraft adopted by the controller is significantly different from the one proposed by the optimization algorithm. This is due to the very dynamic nature of the environment. The objective of this paper is to test the hypothesis that one can learn from the sequence adopted by the controller some strategies that can act as heuristics in decision support tools for aircraft sequencing. This aim is tested in this paper by attempting to learn sequences generated from a well-known sequencing method that is being used in the real world. The approach relies on a genetic algorithm (GA) to learn these sequences using a society Probabilistic Finite-state Machines (PFSMs). Each PFSM learns a different sub-space; thus, decomposing the learning problem into a group of agents that need to work together to learn the overall problem. Three sequence metrics (Levenshtein, Hamming and Position distances) are compared as the fitness functions in GA. As the results suggest, it is possible to learn the behavior of the algorithm/heuristic that generated the original sequence from very limited information

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

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-firstserved ordering. We show that the GA is able to reduce overall taxi time with respect to the other approaches

Receding horizon control for free-flight path optimisation

This paper presents a Receding Horizon Control (RHC) algorithm to the problem of on-line flight path optimization for aircraft in a Free Flight (FF) environment. The motivation to introduce the concept of RHC is to improve the robust performance of solutions in a dynamic and uncertain environment, and also to satisfy the restrictive time limit to the real-time optimization of this complicated air traffic control problem. Firstly, the mathematical model for the on-line FF path optimization problem is set up and discussed. Then, the proposed RHC algorithm is described in details. Simulation results illustrate that the new algorithm is very efficient and promising for practical applications. While achieving almost the same optimal solution as an existing algorithm in the absence of environmental uncertainties, it works better in a dynamic and uncertain environment. In either case, the online computational time of the proposed RHC algorithm is only a fraction of that of the existing algorithm