Intelligent Scheduling of Automotive Stamping Workshops Based on an Enhanced Genetic Algorithm

With the rapid development of the automotive manufacturing industry, enhancing production efficiency and flexibility in stamping workshops has become crucial for maintaining competitiveness. This paper presents an intelligent scheduling method based on an enhanced genetic algorithm to address the scheduling challenges in automotive stamping workshops. By incorporating the magic square selection method for initializing the population and an improved roulette wheel selection operation, this approach significantly enhances the search efficiency and solution quality of the genetic algorithm. During the algorithm’s implementation, innovative crossover methods and decoding mechanisms were designed to better accommodate the production characteristics and constraint conditions of stamping workshops. The proposed method was validated using production data from an automotive factory. The scheduling results demonstrate that the enhanced genetic algorithm effectively addresses over 95% of the scheduling issues encountered in intelligent scheduling of automotive stamping workshops. The algorithm markedly improves workshop production efficiency, reduces production cycles, and enhances the stability and feasibility of production plans. This study provides a robust technical solution for intelligent production management in automotive stamping workshops, offering substantial theoretical significance and practical application value.