By António J. M. Castro
Most of the study efforts facing airline scheduling were performed on off-line plan optimization. besides the fact that, these days, with the more and more complicated and enormous site visitors at airports, the true problem is find out how to react to unforeseen occasions that could reason plan-disruptions, resulting in flight delays.
Moreover those disruptive occasions often have an effect on a minimum of 3 assorted dimensions of the location: the plane assigned to the flight, the team project and infrequently forgotten, the passengers’ trip and satisfaction.
This publication contains solutions to this problem and proposes using the Multi-agent process paradigm to speedily compose a multi-faceted technique to the disruptive occasion considering attainable personal tastes of these 3 key elements of the problem.
Negotiation protocols occurring among brokers which are specialists in fixing different challenge dimensions, mixture of other application capabilities and never less significant, the inclusion of the human within the automated decision-making loop make MASDIMA, the approach defined during this e-book, compatible for real-life plan-disruption administration purposes.
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Extra info for A New Approach for Disruption Management in Airline Operations Control (Studies in Computational Intelligence)
Passenger Recovery (PR) The process of finding alternate itineraries, commencing at the disrupted passenger location and terminating at their destination or a location nearby, while minimizing a specific objective (usually the passenger trip time and the airline costs). 7). 13. 7), simultaneously or not. 14. , all dimensions are of equal importance. 15. Aircraft Readiness Time at which the aircraft is ready for another flight after arriving at an airport. This implies that all regulations and restrictions are contemplated.
2. Besides supporting the definition of preferences over the attribute values the GQN supports interdependent dimensions. 3. GQN accommodate agents with adaptive strategies. 4. GQN protocol supports agents with either full or partial knowledge, making it more suitable for distributed environments. (Jain & Deshmukh, 2009) takes advantage of fuzzy logic and proposes an hybrid negotiation mechanism that combines competitive and cooperative negotiation. It is a bilateral, one-to-many, multidimensional protocol supporting interdependent dimensions and requiring agents with full knowledge to present proposal.
4. A Domain Language is necessary so that the agents are able to refer and understand the concepts of the domain, different kind of messages, temporal concepts and, of course, the objects of negotiation. This includes description of the attributes under negotiation as well as the constants that represent the negotiation attributes’ value and any other needed descriptor symbols. Efforts have been made to provide standard domain languages that can be used by agents in heterogeneous environments .