A typical way to schedule a set of jobs is to evaluate and optimize different job sequences and then process them in the best found order. This global optimization approach can be applied for any set of jobs. Unfortunately, optimizing a subset of these jobs requires a new optimization run for this particular subsequence. In this paper we show the generation of dispatching rules that aid job sequencing in single machine environments using genetic programming and delta features. The rules are applied to sets of jobs and yield priorities depending on certain characteristics. These priorities are then used to create job orders dynamically depending on the last executed job. Once generated for specific scenarios, the rules provide on-the-fly sequence generation capability for queued subsets of jobs. Finally, we compare the performance and robustness of the generated rules against the scheduling approach.