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Digital twin–enabled hybrid scheduling framework for multi-product cellular manufacturing systems
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DOI：10.1080/0951192X.2026.2626922.png)
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Abstract
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This study proposes a digital twin–enabled hybrid scheduling framework that integrates mixed integer linear programming (MILP) and genetic algorithms (GA) for efficient scheduling in multi-product cellular manufacturing systems. Setup time during product changeovers directly affects production efficiency, yet existing rule-based methods and single-algorithm approaches fall short of capturing the complexity of actual production environments. The proposed framework embeds a hybrid MILP–GA within a digital twin, where simulation serves a dual role: evaluating candidate schedules during GA evolution and validating the final optimized schedule. By combining the extensive solution space exploration capability of GA with the precise optimization ability of MILP, the framework addresses the single-machine scheduling problem with sequence-dependent setup times and achieves both makespan minimization and setup time reduction. Experimental results demonstrated that the proposed framework reduced makespan by up to 4.3% and setup time by up to 9.4% relative to the current system. This research contributes a practical and adaptable scheduling solution for small-batch multi-product cellular manufacturing systems, highlighting the value of integrating digital twin technology with hybrid optimization in smart manufacturing environments.
Keywords:
Digital twin
cellular manufacturing systems
setup time minimization
hybrid optimization
production scheduling
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