We start by examining the scheduling instances of a small-size FDA library, FDA.03.09, where "03" denotes its size (number of target chemicals) and "09" the sampling index for targets. The optimal MILP solution achieves a 37.5% makespan reduction when scheduled on LAB-1 without work shift constraints. Given the three target chemicals, ASKCOS and SPARROW are used to generate synthetic routes that form a reaction network of 18 reactions. As shown in Figure , the routes to the three targets do not share any common intermediates. The corresponding operation network of 110 operations (Figure ) is then constructed using the rules defined in Section 2.3. This operation network, along with the functional module definition and processing time estimates, constitute the input to the scheduler for this library. The optimal schedule of FDA.03.09 on LAB-1 produced by the MILP scheduler is shown in Figure . Compared to its heuristic counterpart (Figure ), the optimal schedule differs in both operation-to-module assignment and processing order (operation sequence) on individual modules. Both schedules follow the precedence relations defined by the three independent routes in Figure . They also share the same initial operations (both start with operations of reaction #1 and #17). However, they differ in terms of the absolute order of reactions (global precedence relations). For example, in the optimal schedule, reaction #9 is scheduled after #7, whereas in the baseline schedule, it precedes #7. As expected, the module utilization is higher for all modules using the optimal schedule. The percentage gap between the two solutions depends on the functional modules. For instance, when this library is scheduled on LAB-2 with other parameters unchanged, the makespan gap increases to 41.2%.