This paper describes an artificial immune system (AIS) approach
to producing robust schedules for a dynamic jobshop
scheduling problem in which jobs arrive continually,
and the environment is subject to change due to practical
reasons. We investigate whether an AIS can be evolved using
a genetic algorithm, (GA), and then used to produce sets
of schedules which together cover a range of contingencies,
both foreseeable and unforeseeable. We compare the quality
of the schedules to those produced using a genetic algorithm
specifically designed for tackling job-shop scheduling
problems, and find that the schedules produced from the
evolved AIS compare favourably to those produced by the
GA. Furthermore, we find that the AZS schedules are robust
in that there are large similarities between each schedule in
the set, indicating that a switch from one schedule to another
could be performed with minimal disruption if rescheduling
is required.