Constraint satisfaction adaptive neural network and efficient heuristics for job-shop scheduling
An efficient constraint satisfaction based adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The adaptive neural network has the property of adaptively adjusting its connection weights and biases of neural units according to the sequence and resource constraints of job-shop scheduling problem while solving feasible solution. Two heuristics are used in the hybrid approach: one is used to accelerate the solving process of neural network and guarantee its convergence, the other is used to obtain non-delay schedule from solved feasible solution by neural solution by neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and excellent efficiency.
Citation : Yang, S. and Wang, D. (1999) Constraint satisfaction adaptive neural network and efficient heuristics for job-shop scheduling. Proceedings of the 14th IFAC World Congress, Vol. J: Discrete Event Systems, Stochastic Systems, Fuzzy and Neural Systems I, pp. 175-180
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes