For scheduling instances where the MILP scheduler found an optimal schedule (363 instances shown in Figure ), the percentage gap ranges from 0% to 57.8% with an average of 20.5%. For instances where at least one feasible solution is found (686 instances including 363 optimal schedules and 323 suboptimal schedules), the average percentage gap becomes 9.8%. The reduction in the average percentage gap is mainly due to 14 of 323 of suboptimal schedules having makespan values higher than baseline schedules, i.e. the percentage gaps are negative for these instances. In general, the percentage gap tends to be larger for larger libraries, which is expected as larger operation graphs tend to have more degrees of freedom and provide greater opportunity for efficient scheduling. Another observation from Figure is that optimal schedules are hard to find for schedules on LAB-1 compared to LAB-2, e.g.