Application of RNA Genetic Algorithm with Adaptive Strategy in Production Scheduling

Production scheduling is a critical aspect of manufacturing, aiming to enhance efficiency, reduce costs, and meet customer demands through optimized resource allocation and task sequencing. Traditional approaches, such as heuristic algorithms and mathematical programming, often underperform in complex, dynamic environments. In recent years, bio-inspired optimization algorithms-including genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE)-have been widely adopted due to their robust global search capabilities. Nevertheless, these methods exhibit limitations such as slow convergence and susceptibility to local optima when addressing intricate scheduling problems. To overcome these challenges, this paper introduces an adaptive RNA genetic algorithm (RNA-GA) for production scheduling. Drawing inspiration from RNA molecular behavior, RNA-GA employs an adaptive mechanism to dynamically adjust algorithmic parameters and operations, thereby improving search efficiency and solution quality. Experimental comparisons with traditional GA, PSO, and DE validate the superior performance of the proposed method.