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A dynamic multi-objective evolutionary greedy algorithm for distributed hybrid flow shop rescheduling problem
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DOI：10.1016/j.swevo.2025.102054.png)
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Abstract
En 中文
• A distributed hybrid flowshop rescheduling problem with new job arrivals and random machine breakdowns is studied. • A dynamic multi-objective evolutionary greedy algorithm is proposed. • Based on problem-specific knowledge, the acceleration mechanism and population evolution mechanism of the algorithm are investigated. • A deep reinforcement learning algorithm is adopted to determine whether dynamic events in production require rescheduling.
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