Genetic Algorithms (GAs) have shown to fit the complex needs arisung from many tasks of academic optimization for almost 20 years.(...)

The application of adaptive optimization strategies to scheduling in manufacturing systems has recently become a research topic of broad interest. Population based approaches to scheduling predominantly treat static data models, whereas real-world scheduling tends to be a dynamic problem. This...

A frequently observed difficulty in the application of genetic algorithms to the domain of optimization ariscs from premature convergence. In order to preserve genotype diversity we develop a new model of auto-adaptive behavior for individuals. In this model a population member is an active...

The population of parallel genetic algorithms (PGAs) can easily be split up to match the needs of a coarse grained parallelism. A cluster of interconnected workstations, seen as an MIMD-architecture, is the chosen hardware to express this kind of parallelism. A PGA implementation, as any other...

We consider job shop scheduling problems with release and due-dates, as well as various tardiness objectives. To date, no efficient general-purpose heuristics have been developed for these problems. genetic algorithms can be applied almost directly, but come along with apparent weaknesses.

A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem ...

This paper adresses job tardiness for non deterministic job shop scheduling. A comparative study shows that a GA consistently outperforms different priority rules regardless of the workload and the objective pursued.