Balance of mixed flow assembly line based on industrial engineering mathematics and simulated annealing improved algorithm

Abstract

The frequent changes in customer orders have led to the need for production line rebalancing and design. The increasing complexity of mixed assembly of multiple products makes it an urgent issue to perfect the balance of mixed flow assembly lines. This study proposes an improved simulated annealing algorithm by combining industrial engineering mathematical methods with intelligent optimization algorithms. The purpose is to achieve balanced optimization of mixed flow assembly lines by minimizing production rhythm, balancing load between workstations, and minimizing loss time within workstations. Firstly, industrial engineering methods are used to analyze the mixed flow assembly line, and then a mathematical optimization model is established by combining simulated annealing algorithm and genetic algorithm. Finally, through example simulation, the effectiveness of the algorithm is verified, and the superiority of its performance is verified through algorithm comparison. The experiment shows that the balance rate of the optimized assembly line for a total of 60 processes in 12 workstations has increased by about 6%. The smoothness index decreases by about 4. The loss coefficient has decreased by approximately 0.06. The overall efficiency has increased by about 12%. The smoothness index decreases by 0.3 in comparison with the solution results of Jackson's law in a case of 11 processes in 5 workstations. This fully demonstrates that the hybrid model has higher optimization efficiency, achieves balanced optimization of the mixed flow assembly line, improves production efficiency, and reduces equipment idle rate.