In mitigating occupational hazards, there is often a need to use administrative controls such as job rotation over a prolonged period until the hazards can be eliminated or mitigated to safe levels. This research develops a noise-safe job-rotation optimization model that accounts for learning, forgetting, and boredom effects. Our analysis focuses on the case of human-paced and labor-intensive
operations, considering the trade-off between safety and productivity. A case of multi-skilled workers that have heterogeneous skill levels with varying problem sizes is used to demonstrate the model’s capabilities. A genetic algorithm and a randomized greedy algorithm are developed and shown to be effective in solving large-scale safe job rotation problems. Our results also show how the boredom and forgetting effects create productivity delays when job rotation is used.