Joint scheduling optimization of production assembly considering testing groups in robot manufacturing

In the evolving field of industrial intelligent robot manufacturing, efficient production scheduling is crucial, particularly in scenarios where workshops operate independently due to varying demands, a situation described as “No Collaboration in Workshops” (NCW). This study addresses NCW inefficiencies by proposing a bi-level programming model to optimize scheduling in sustainable robot manufacturing. In this model, the leader at the upper level makes decisions that directly influence the follower’s decisions at the lower level. The upper-level model aims to minimize total delay costs, while the lower-level model focuses on minimizing the makespan. To solve this bi-level problem, we developed a hybrid Grey Wolf Optimizer with an acceleration strategy (GWO-AC). This algorithm features a novel acceleration strategy that filters out inferior solutions passed from the upper level to the lower level, significantly enhancing the model’s computational efficiency. Extensive computational experiments show that GWO-AC significantly outperforms existing methods, substantially improving production efficiency and cost reduction.