Abstract
As the core of enterprise production management, production scheduling plays a pivotal role in the optimal allocation of resources and scientific operation. A more optimized workpiece scheduling method can greatly reduce the staying time of workpieces in the workshop and improve production efficiency. Most of the existing scheduling algorithms are coded according to the length of the number of workpieces and machines, and the number of workpieces in each batch of the factory is often different. This scheduling method has certain limitations. How to perform different batches and varying numbers of workpieces? Production scheduling has become an urgent problem for modern enterprises. Based on this, this paper proposes a variable-length chromosome encoding method, which can perform production scheduling on different batches and different numbers of workpieces, greatly improving the scope of application of scheduling scenarios. At the same time, because of the shortcomings of low population diversity and slow operation speed in the non-dominated sorting genetic algorithm, an adaptive layering strategy is proposed. Simulation experiments show that the improved algorithm can better complete scheduling tasks and improve production efficiency.
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