Title :
A Learning Agent to Assist in Airline Disruption Management
Author :
Langerman, J.J. ; Ehlers, E.M.
Author_Institution :
Standard Bank of South Africa, Marshalltown
Abstract :
Airline disruption management, also known as airline operational scheduling, is becoming an important research topic. During the last few decades great strides have been made to generate optimal schedules. The problem is that when the ideal schedule gets disrupted on the day of operation we need to recover as quickly as possible. A combination of an expert system and a Q-Learning system, implemented as an intelligent agent can be used as a technological approach to solve this problem
Keywords :
expert systems; learning systems; scheduling; travel industry; Q-Learning system; airline disruption management; airline operational scheduling; expert system; intelligent agent; learning agent; Africa; Air traffic control; Aircraft; Costs; Expert systems; Humans; Intelligent agent; Job shop scheduling; Optimal scheduling; Processor scheduling;
Conference_Titel :
Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on
Conference_Location :
Vienna
Print_ISBN :
0-7695-2504-0
DOI :
10.1109/CIMCA.2005.1631286
