Resource-oriented scheduling for real world manufacturing systems

Scheduling algorithms that achieve good or near optimal solutions and can efficiently adapt them to perturbations are, in most cases, preferable to those that achieve optimal ones but that cannot implement such an adaptation. In fact, in real world, where problems are essentially of dynamic and stochastic nature, these methods or algorithms are of very little use. This is the case with most algorithms for solving the so-called static scheduling problem for different setting of both single and multi-machine systems arrangements. This paper is concerned with a scheduling system, based on Genetic Algorithms, for the resolution of the dynamic version of the same problem. An additional constraint will be considered in our approach for the JSSP, the existence of different job release and due dates and different assembly levels for the jobs.