Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5893
Title: Genetic algorithm and neural network hybrid approach for job-shop scheduling
Authors: Zhao, K
Yang, S
Wang, D
Keywords: Job-shop scheduling;Genetic algorithm;Neural network
Issue Date: 1998
Publisher: ACTA Press
Citation: IASTED International Conference on Applied Modelling and Simulation (AMS'98), Calgary, Alberta, Canada: 110 - 114
Abstract: 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.
Description: Copyright @ 1998 ACTA Press
URI: http://bura.brunel.ac.uk/handle/2438/5893
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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