Abstract
Abstract This paper re-evaluates the job-shop scheduling problem by showing how the standard definition is far more restrictive than necessary and by presenting a new technique capable of tackling a highly generalized version of the problem. This technique is based on a massively parallel distributed genetic algorithm and is capable of simultaneously optimizing the process plans of a number of different components, at the same time a near-optimal schedule emerges. Underlying the evolutionary machinery is a specialized feature-based generative process planner.
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