Solving the airline crew recovery problem by a genetic algorithm with local improvement

2005 ◽  
Vol 5 (2) ◽  
pp. 241-259 ◽  
Author(s):  
Yufeng Guo ◽  
Leena Suhl ◽  
Markus P. Thiel
Keyword(s):  
Genetic Algorithm ◽  
Airline Crew ◽  
Local Improvement ◽  
Crew Recovery ◽  
Recovery Problem

Airline Crew Recovery

2000 ◽  
Vol 34 (4) ◽  
pp. 337-348 ◽  
Author(s):  
Ladislav Lettovský ◽  
Ellis L. Johnson ◽  
George L. Nemhauser
Keyword(s):  
Airline Crew ◽  
Crew Recovery

scholarly journals An Enhanced Genetic Algorithm to Solve the Static and Multistage Transmission Network Expansion Planning

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Luis A. Gallego ◽  
Marcos J. Rider ◽  
Marina Lavorato ◽  
Antonio Paldilha-Feltrin
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Heuristic Algorithms ◽  
Initial Population ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion ◽  
Local Improvement ◽  
Linear Programming Problems

An enhanced genetic algorithm (EGA) is applied to solve the long-term transmission expansion planning (LTTEP) problem. The following characteristics of the proposed EGA to solve the static and multistage LTTEP problem are presented, (1) generation of an initial population using fast, efficient heuristic algorithms, (2) better implementation of the local improvement phase and (3) efficient solution of linear programming problems (LPs). Critical comparative analysis is made between the proposed genetic algorithm and traditional genetic algorithms. Results using some known systems show that the proposed EGA presented higher efficiency in solving the static and multistage LTTEP problem, solving a smaller number of linear programming problems to find the optimal solutions and thus finding a better solution to the multistage LTTEP problem.

scholarly journals A Proposal of Genetic Algorithm with a Local Improvement Mechanism and Finding of Fuzzy Rules

1995 ◽  
Vol 7 (5) ◽  
pp. 978-987 ◽  
Author(s):  
Takeshi FURUHASHI ◽  
Ken NAKAOKA ◽  
Hiroshi MAEDA ◽  
Yoshiki UCHIKAWA
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Rules ◽  
Local Improvement

A Column Generation Approach to Railway Crew Recovery Problem in Disruption

2014 ◽  
Vol 134 (3) ◽  
pp. 418-424
Author(s):  
Jun Imaizumi ◽  
Rei Miura ◽  
Eiki Shigeta ◽  
Susumu Morito
Keyword(s):  
Column Generation ◽  
Crew Recovery ◽  
Recovery Problem

scholarly journals Cooperative Path Planning for Aerial Recovery of a UAV Swarm Using Genetic Algorithm and Homotopic Approach

2020 ◽  
Vol 10 (12) ◽  
pp. 4154
Author(s):  
Yongbei Liu ◽  
Naiming Qi ◽  
Weiran Yao ◽  
Jun Zhao ◽  
Song Xu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
High Performance ◽  
Large Scale ◽  
Coupling Mechanism ◽  
Recovery Planning ◽  
Planning Framework ◽  
Uav Swarm ◽  
Recovery Problem ◽  
Multi Uav

To maximize the advantages of being low-cost, highly mobile, and having a high flexibility, aerial recovery technology is important for unmanned aerial vehicle (UAV) swarms. In particular, the operation mode of “launch-recovery-relaunch” will greatly improve the efficiency of a UAV swarm. However, it is difficult to realize large-scale aerial recovery of UAV swarms because this process involves complex multi-UAV recovery scheduling, path planning, rendezvous, and acquisition problems. In this study, the recovery problem of a UAV swarm by a mother aircraft has been investigated. To solve the problem, a recovery planning framework is proposed to establish the coupling mechanism between the scheduling and path planning of a multi-UAV aerial recovery. A genetic algorithm is employed to realize efficient and precise scheduling. A homotopic path planning approach is proposed to cover the paths with an expected length for long-range aerial recovery missions. Simulations in representative scenarios validate the effectiveness of the recovery planning framework and the proposed methods. It can be concluded that the recovery planning framework can achieve a high performance in dealing with the aerial recovery problem.

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