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

No more conflicts: the development of a generic airport model in a sequence-optimization framework

International audienceComponents of the airport airside such as runways, taxiways and aprons, have a significant impact in the total capacity of the airport system, where capacity is usually considered as maximum number of air traffic movements or number of passengers accommodated in a given period of time. Operations on the airside impact in the propagation of delay and consequently in the perceived level of service by passengers the terminal buildings. This paper put the focus on the airside operations at airports. A methodology for modelling operations on the ground and the successive optimization is proposed. The methodology presented in this paper is generic enough in the sense that it can be applied to any airport. The objective of this work is to come up with a generic tool that can be used by air traffic controllers in order to minimize conflicts on the ground and consequently increase the airport capacit

Implementation of an Optimization and Simulation-Based Approach for Detecting and Resolving Conflicts at Airport

International audienceIn this paper is presented a methodology that uses simulation together with optimization techniques for a conflict detection and resolution at airports. This approach provides more robust solutions to operative problems, since, optimization allows to come up with optimal or suboptimal solutions, on the other hand, simulation allows to take into account other aspects as stochasticity and interactions inside the system. Both the airport airspace (terminal manoeuvring area), and airside (runway taxiways and terminals), were modelled. In this framework, different restrictions such as speed, separation minima between aircraft, and capacity of airside components were taken into account. The airspace was modeled as a network of links and nodes representing the different routes, while the airside was modeled in a low detail, where runway, taxiways and terminals were modeled as servers with a specific capacity. The objective of this work is to detect and resolve conflicts both in the airspace and in the airside and have a balanced traffic load on the ground

An Application of Ant Colony Optimization in Industrial Training Allocation

The process of assigning a visiting university’s supervisor to visit a group of industrial training practical students in the university is currently being done manually. In order to perform such task, two constraints need to be fulfilled at any time: (1) Practical student can only be supervised by university supervisor from the same department; (2) location of the places to be visited by the visiting university’s supervisor must be as near as possible in order to optimize the travelling cost, time and budget. Using manual approach, the process can be very tedious and time consuming especially when it involved large number of practical students and lecturers. Furthermore, the optimized result is seldom achievable as not all practical student-lecturer combinations are examined. By automating the process, the tedious and time consuming process can be avoided as well as establishing optimized combinations based on the given constraints. This paper discusses on how the assignment process is automated using Ant Colony Optimization (ACO). The results are then compared with Dijkstra’s Algorithm to evaluate the ability of ACO algorithms. The algorithm design, implementation, its future direction and improvements are discussed as well

Aircraft sequencing problem solve by using simulated annealing method

Since commercial aircraft exists in the late 1960’s and early 1970’s, air traffic has experience a tremendous amount of growth and is now known as one of the complex logistical system. Over the past few decades, aircraft sequencing problem (ASP) has become one of the most important area of research in the OR field as the number of passengers using the air transportation has increased significantly. ASP aims is to assign each aircraft with scheduled landing time while maintaining the operational and safety constraints. In Malaysia, there is a system called Air Traffic Management (AMAN) that can produce a sequence for the aircraft to land. However, one of the weaknesses of the system is the inability of the system to provide the best route for the aircraft to land even if there is no other aircraft flying at the same period. To tackle this problem, this research will develop a program that can provide the best route for the aircraft to land by considering alternative admissible routes provided by the ATC-KL with the objective of minimizing the total airborne time of all aircrafts while satisfying the separation time constraint between the aircraft. This research will use the Simulated Annealing method with three different neighborhood structures, initial temperatures and temperature reduction formulas. From the computational results, this research has concluded that the best neighborhood structure is Swap and Reroute with an initial temperature of 300 000 and temperature reduction of where P is the random number generated by the program

An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs

An Efficient Approximation Algorithm for Aircraft Arrival Sequencing and Scheduling Problem

The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in airports' runway scheduling system, which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS in the form of a constrained permutation problem and designs a new approximation algorithm to solve it. Then the numerical study is conducted, which validates that this new algorithm has much better performance than ant colony (AC) algorithm and CPLEX, especially when the aircraft types are not too many. In the end, some conclusions are summarized

Tackling Uncertainty for the Development of Efficient Decision Support System in Air Traffic Management

International audienceAirport capacity has become a constraint in the air transportation networks, due to the growth of air traffic demand and the lack of resources able to accommodate this demand. This paper presents the algorithmic implementations of a decision support system for making a more efficient use of the airspace and ground capacity. The system would be able to provide support for air traffic controllers in handling large amount of flights while reducing to a minimum the potential conflicts. In this framework, airspace together with ground airport operations are considered. Conflicts are defined as separation minima violation between aircraft for what concerns airspace and runways, and as capacity overloads for taxiway network and terminals. The methodology proposed in this work consists of an iterative approach that couples optimization and simulation to find solutions that are resilient to perturbations due to the uncertainty present in different phases of the arrival and departure process. An optimization model was employed to find a (sub)optimal solution while a discrete event-based simulation model evaluated the objective function. By coupling simulation with optimization, we generate more robust solutions resilient to variability in the operations, this is supported by a case study of Paris Charles de Gaulle Airport