Using constraint satisfaction adaptive neural network and efficient heuristics for job-shop scheduling (original) (raw)
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The research integrates constraint satisfaction adaptive neural networks (CSANN) and efficient heuristics to address the complexities of job-shop scheduling problems. It aims to enhance scheduling efficiency through advanced optimization techniques, specifically focusing on the adaptation of neural networks to manage constraints effectively. The findings suggest significant improvements in solving scheduling challenges compared to traditional methods, demonstrating the potential of CSANN in real-world applications.
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