Solving air traffic conflict problems via local continuous optimization

DOI10.1016/j.ejor.2014.08.045zbMath1339.90356OpenAlexW1983914348MaRDI QIDQ2629693

Marcel Mongeau, Clément Peyronne, D. Delahaye, Andrew R. Conn

Publication date: 6 July 2016

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2014.08.045

zbMATH Keywords

genetic algorithmssemi-infinite programmingB-splinescontinuous optimizationair traffic conflict problem

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Applications of mathematical programming (90C90) Approximation methods and heuristics in mathematical programming (90C59) Case-oriented studies in operations research (90B90) Semi-infinite programming (90C34)

Related Items (7)

An exact multi-objective mixed integer nonlinear optimization approach for aircraft conflict resolution ⋮ Combination of optimal control approaches for aircraft conflict avoidance via velocity regulation ⋮ A VNS metaheuristic for solving the aircraft conflict detection and resolution problem by performing turn changes ⋮ On the binary formulation of air traffic flow management problems ⋮ Mixed-integer nonlinear and continuous optimization formulations for aircraft conflict avoidance via heading and speed deviations ⋮ Mixed-integer nonlinear programming for aircraft conflict avoidance by sequentially applying velocity and heading angle changes ⋮ Polynomial programming prevents aircraft (and other) conflicts

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Order statistics and region-based evolutionary computation
• How to solve a semi-infinite optimization problem
• A light-propagation model for aircraft trajectory planning
• Introduction to Derivative-Free Optimization
• An exact penalty function for semi-infinite programming
• A trust region method based on interior point techniques for nonlinear programming.

This page was built for publication: Solving air traffic conflict problems via local continuous optimization