Solving combinatorial optimisation problems in transport multi-agent systems using Hopfield-neural network

The tasks planning in the transport domain is a difficult problem which requires the use of analytical techniques and modelling methods resulting from the operational research, the distributed Artificial Intelligence (multiagents systems), the decision analysis, and many other disciplines. Our contribution to this problem consists on one hand of proposing a modelling of the transport system by a multi-agents system (MAS) based on a classification model of agents to manage our different agent Subsystems (supervision subsystem, planning subsystem, ergonomic subsystem) at the same time, while keeping a global structure; and on the other hand of deploying a Fuzzy Hopfield-Neural Network model to solve the routing and scheduling problems within our planning subsystem. The computational experiments were carried out on an extended set of 300 Routing problems with 21 customers. The results demonstrate that the connexionist approach is highly competitive in term of computing time, providing the best solutions to 56% of all test instances within reasonable computing times. The power of our algorithm is confirmed by the results obtained on 21 customer problems from the literature.

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