Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
DSS for rescheduling of railway services under unplanned events
Decision making support systems
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
Distributed decision evaluation model in public transportation systems
Engineering Applications of Artificial Intelligence
An Introduction to MultiAgent Systems
An Introduction to MultiAgent Systems
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Synchronization and Control of Multiagent Systems
Synchronization and Control of Multiagent Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Path planning of mobile robot with neuro-genetic-fuzzy technique in static environment
International Journal of Hybrid Intelligent Systems
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This paper deals with the problem of optimally regulating planned vehicle timetables for public transport when unforeseen events occur in real-time in the network. A multicriteria problem is solved by using an integrated intelligent approach that combines agent-based techniques, Tabu search algorithm and fuzzy preference model. The agents of our model act cooperatively in order to generate efficient solutions that optimize simultaneously and separately the different regulation objectives. This optimization is performed by means of a distributed tabu search algorithm. Efficient solutions are then, classified according to a fuzzy preference model by using an interactive solutions evaluation among agents. In order to assess the distributed approach, an experimental study was carried out on the base of some scenarios of disturbances occurred in a public transportation network.