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There is an urgent demand to shift the adaptive manufacturing from passive scheduling to proactive scheduling. The uncertain evolution of the scheduling performance and the equipment state causes difficulty in balancing the production load and the predicted maintenance under the degradation effect. This paper aims at the production scheduling and mixed\u2010flow assembly line maintenance and proposes an improved hybrid Tabu Search and Genetic Algorithm (hybrid TSGA)\u2013based proactive scheduling method for production prediction. Initially, the method constructs a real\u2010time state model for the equipment state, workshop manufacturing efficiency, manufacturing resource state, and manufacturing execution system feedback information. The production process status information is used as input to a mathematical modeling method, which is used to obtain the production trend of mixed flow assembly line in the workshop. Afterward, the hybrid TSGA is deployed and its sequential decision\u2010making capability is used to generate a proactive scheduling scheme based on the production trend prediction. The proposed methodology was implemented in a robotic flexible welding automobile production line for production scheduling, with its efficacy empirically validated. Simulation results demonstrated a 6% reduction in total completion time for multimachine, multiprocess scheduling scenarios, demonstrating superior performance.<\/jats:p>","DOI":"10.1155\/int\/4600789","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T18:48:43Z","timestamp":1757702923000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Improved Hybrid Tabu Search and Genetic Algorithm for Proactive Scheduling of Mixed\u2010Flow Assembly Line Under Degradation 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