Design and Practical Application of Computer Intelligent Algorithm-Driven Scheduling System for Electrically Automated Production Lines

With the advancement of Industry 4.0, electrically automated production lines have become the core carrier for the transformation and upgrading of the manufacturing industry. As the "central nervous system" of the production line, the performance of the scheduling system directly determines production efficiency, cost control, and resource utilization. Currently, traditional scheduling methods are ill-suited to the flexible production demands of multi-variety, small-batch production, exhibiting problems such as lag in dynamic response, insufficient multi-objective optimization capabilities, and resource imbalance, thus hindering the full realization of the potential of automated production lines. This paper aims to address these issues by conducting research on the design and practice of a computer-driven intelligent algorithm-based scheduling system for electrical automated production lines. First, the core requirements and constraints of electrical automated production line scheduling are identified, and a multi-objective scheduling optimization model is constructed. Second, a hybrid genetic algorithm-particle swarm optimization algorithm is selected and improved to enhance the algorithm’s convergence speed and optimization accuracy in dynamic scheduling scenarios. Furthermore, based on this hybrid intelligent algorithm, a scheduling system architecture comprising a data acquisition layer, an algorithm decision-making layer, and an execution control layer was designed, and corresponding functional modules were developed. Finally, an experimental platform was built, and typical production scenarios were selected for verification. Experimental results show that dynamic interference events affected the performance of all three systems to varying degrees, but the experimental group’s scheduling system exhibited superior stability and rapid response capabilities: when three urgent orders (accounting for 20% of the current uncompleted orders) were inserted in the 12th hour, the production cycle fluctuation of the experimental group only increased to 8.3%. The on-time order rate briefly decreased and then quickly rebounded to over 95%, demonstrating good response performance in scenarios such as dynamic order insertion and sudden equipment failures, effectively adapting to flexible production needs.