A New Method for Human-Machine Ratio Configuration Scheduling Problem

As automation and intelligence become more prevalent, large-scale scheduling problems increasingly involve collaboration between human labor and machines. To optimize situations where a single worker oversees a work center withmultiple machines, this paper presents a new two-stage algorithm which dispatches jobs to the work centers with different human-machine ratio, and determines the job sequence on each machine to maximize the average utilization rate. The proposed algorithm utilizes a nested partition (NP) framework to address the parallel machine scheduling problem involving complex human-machine collaboration operations. Two new heuristic approaches are designed and integrated into the NP framework for better performance. Furthermore, the algorithm is refined by incorporating prune and reproduction mechanisms, leading to the development of the innovative Reproduction Improved Nested Partition (RINP) framework. This paper conducts simulation experiments to compare the proposed algorithms with the conventional nested partition framework, and the results demonstrate the superiority of the proposed approaches.