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http://bura.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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