Heurestics and reinforcement learning in manufacturing control: Optimization by phases

Manufacturing control still continues to attract the attention of researchers for 3 decades already. But what concerns them most is to find a compromise between optimizing either for cost, time, ect. and responsiveness to cope with competition increasingly growing. In this paper we aim to develop a bi-phases manufacturing control, the first phase is assignment which is allocating resource to task, we use here an heuristic based on iterative optimization. Than the second phase sequencing which define a beginning and end date for each task, we use for that holon negotiation and reactive learning. we applied the developed approach on flexibal job shop (FJS).