Producing robust schedules via an artificial immune system

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Abstract
This paper describes an artificial immune system (AIS) approach to producing robust schedules for a dynamic job-shop 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 AIS 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.

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