Genetic algorithm and neural network hybrid approach for job-shop scheduling
This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and the speed of calculation.
Citation : Zhao, K., Yang, S. and Wang, D. (1998) Genetic algorithm and neural network hybrid approach for job-shop scheduling. Proceedings of the IASTED Int. Conf. on Applied Modelling and Simulation (AMS'98), pp. 110-114
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes