Haven’t there been enough approaches to scheduling problems? In terms of variety the answer might be ‘yes’. However, the answer is not as straightforward in terms of effectiveness. In many scheduling problems, it is highly improbable, if not impossible, to obtain optimal schedules within a reasonable amount of time in spite of adopting a wide range of approaches, including evolutionary computation (EC), artificial neural networks (ANN), fuzzy systems (FS), simulated annealing (SA) and Tabu search (TS). In recent years attention has been drawn to another biologically-inspired computing paradigm called artificial immune systems (AIS). An AIS abstracts and models the principles and processes of the biological immune system in order to effectively tackle challenging problems in dynamic environments. Major AIS models include negative selection, clonal selection, immune networks and more recently danger theory (Garrett in Evol. Comput. 13(2):145–177, 2005). There are some similarities between these principles and processes in the immune system and those found in other nature-inspired computing approaches, EC and ANN in particular. However, there are also substantial differences. In particular, adaptive cloning and mutation processes make AIS distinctive and useful.