Development of scheduling strategies with Genetic Fuzzy systems

This paper presents a methodology for automatically generating online scheduling strategies for a complex objective defined by a machine provider. To this end, we assume independent parallel jobs and multiple identical machines. The scheduling algorithm is based on a rule system. This rule system classifies all possible scheduling states and assigns a corresponding scheduling strategy. Each state is described by several parameters. The rule system is established in two different ways. In the first approach, an iterative method is applied, that assigns a standard scheduling strategy to all situation classes. Here, the situation classes are fixed and cannot be modified. Afterwards, for each situation class, the best strategy is extracted individually. In the second approach, a Symbiotic Evolution varies the parameter of Gaussian membership functions to establish the different situation classes and also assigns the appropriate scheduling strategies. Finally, both rule systems will be compared by using real workload traces and different possible complex objective functions.