Scheduling Models and Algorithms for TMA Traffic Management

Bianco, Lucio; Dell'Olmo, Paolo; Giordani, Stefano

doi:10.1007/978-3-642-60836-0_7

Cited by 53 publications

(53 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To fully investigate the performance of the RHC based GA proposed in this paper, which is hereafter denoted as RHC_GA, we compare it with two other algorithms based on conventional dynamic optimization (CDO) strategy: a conventional GA, denoted as CDO_GA, and the algorithm reported in [10], denoted as CDO_TSP. The algorithm in [10] is developed by modeling the ASS problem as a traveling salesman problem (TSP).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…These differences in separation are mandatory and recognized by ATC regulations. For the sake of simplicity, like [10], aircraft waiting to land are classified into a relatively small number of distinct categories, according to speed, capacity, weight and other technical characteristics. Table 1 shows the minimum LTIs relative to main categories of commercial aircraft.…”

Section: Arrival Sequencing and Scheduling (Ass) Is One Of The Standamentioning

confidence: 99%

“…Many efforts have been made to study the problem of "position shifting" based ASS in the past decades, for example, the ASS problem is modeled as a Traveling Salesman Problem (TSP) and dynamic programming algorithms are developed, e.g., see [7], [8], [10] and [11]. The above papers mainly focus on developing effective static algorithms.…”

Section: Arrival Sequencing and Scheduling (Ass) Is One Of The Standamentioning

confidence: 99%

“…The above papers mainly focus on developing effective static algorithms. Although the dynamic case is also discussed in [7], [8] and [10], the proposed dynamic algorithms are simple extensions of the associated main static results by introducing some constraints. In the real world of ATC, ASS is always carried out in a dynamic environment.…”

Section: Arrival Sequencing and Scheduling (Ass) Is One Of The Standamentioning

confidence: 99%

“…(a), is not suitable for dynamic optimization processes. However, most algorithms in literature on the ASS problem are mainly tested by using offline strategy, so-called static version, e.g., see [7], [8] and [10]. The one-step-ahead adjustment strategy in Figure 2.…”

Section: The Basic Idea Of Rhcmentioning

confidence: 99%

See 4 more Smart Citations

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

Chen

2005

Engineering Applications of Artificial Intelligence

109

View full text Add to dashboard Cite

Abstract: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.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Arrival Sequencing and Scheduling (Ass) Is One Of The Standamentioning

confidence: 99%

Section: Arrival Sequencing and Scheduling (Ass) Is One Of The Standamentioning

confidence: 99%

Section: Arrival Sequencing and Scheduling (Ass) Is One Of The Standamentioning

confidence: 99%

Section: The Basic Idea Of Rhcmentioning

confidence: 99%

See 3 more Smart Citations

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

Chen

2005

Engineering Applications of Artificial Intelligence

109

View full text Add to dashboard Cite

show abstract

Mathematical model for optimising the sequence for clearing snow from the manoeuvring area during winter operations

Fernández

Gómez

Arnaldo

2016

J of Advced Transportation

View full text Add to dashboard Cite

Summary This article considers the optimisation of the sequence for clearing snow from stretches of the manoeuvring area of an airport. This issue involves the optimisation of limited resources to remove snow from taxiways and runways thereby leaving them in an acceptable condition for operating aircraft. The airfield is divided into subsets of significant stretches for the purpose of operations and target times are established during which these are open to aircraft traffic. The document contains several mathematical models each with different functions, such as the end time of the process, the sum of the end times of each stretch and gap between the estimated and the real end times. During this process, we introduce different operating restrictions on partial fulfilment of the operational targets as applied to zones of special interest, or relating to the operation of the snow‐clearing machines. The problem is solved by optimisation based on linear programming. The article gives the results of the computational tests carried out on five distinct models of the manoeuvring area, which cover increasingly complex situations and larger areas. The mathematical model is particularised for the case of the manoeuvring area of Adolfo Suarez Madrid—Barajas Airport. Copyright © 2016 John Wiley & Sons, Ltd. Optimal sequence for clearing snow from the manoeuvring area of an airport. Contains optimising algorithms solved using CPLEX LP‐based tree search. Restrictions on partial fulfilment of operational targets applied to subsets of significant stretches, used for planning the operation of snow‐clearing machines. Model applied to the case of the manoeuvring area of Adolfo Suárez Madrid Barajas Airport. Conclusions are given on the results of the computational tests carried out. There are five models of the manoeuvring area which cover increasingly complex situations and larger areas.

show abstract

Airport taxi planning: Lagrangian decomposition

Marı́n

2011

J of Advced Transportation

View full text Add to dashboard Cite

SUMMARYThe airport taxi planning (TP) module is a decision tool intended to guide airport surface management operations. TP is defined by a flow network optimization model that represents flight ground movements and improves aircraft taxiing routes and schedules during periods of aircraft congestion. TP is not intended to operate as a stand-alone tool for airport operations management: on the contrary, it must be used in conjunction with existing departing and arriving traffic tools and overseen by the taxi planner of the airport, also known as the aircraft ground controller. TP must be flexible in order to accommodate changing inputs while maintaining consistent routes and schedules already delivered from past executions. Within this dynamic environment, the execution time of TP may not exceed a few minutes. Classic methods for solving binary multi-commodity flow networks with side constraints are not efficient enough; therefore, a Lagrangian decomposition methodology has been adapted to solve it. We demonstrate TP Lagrangian decomposition using actual data from the Madrid-Barajas Airport.

show abstract

Scheduling Models and Algorithms for TMA Traffic Management

Cited by 53 publications

References 12 publications

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

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

Mathematical model for optimising the sequence for clearing snow from the manoeuvring area during winter operations

Airport taxi planning: Lagrangian decomposition

Contact Info

Product

Resources

About