Autonomous control means the decentralized coordination of intelligent logistic objects and the routing through a logistic system by the intelligent parts themselves. This paper shows the application of a pheromone-based autonomous control method to a matrix model of a shop floor and compares the performance to an earlier developed method in different dynamic demand situations. The discrete event simulations are analysed by comparing statistics on throughput time data resulting from the system's behaviour in dynamic order arrival situations.

The concept of autonomous control requires on one hand logistic objects that are able to receive local information, process these information, and make a decision about their next action. On the other hand, the logistic structure has to provide distributed information about local states and different alternatives to enable decisions generally. These features will be made possible through the development of Ubiquitous Computing technologies (Fleisch et al. 2003).The application of autonomous control in production logistics can be realized by recent information and communication technologies such as radio frequency identification (RFID), wireless communication networks etc. These technologies enable intelligent and autonomous parts and products to communicate with each other and with their resources such as machines and transportation systems and to process the acquired information. This leads to a coalescence of material flow and information flow and allows every item or product to manage and control its manufacturing process autonomously . The coordination of these intelligent objects requires advanced planning and control concepts and strategies to realize autonomous control of logistic processes. To develop and analyze such autonomous control strategies dynamic models are required.In order to prove that the implementation of autonomous control in production systems is more advantageous than conventionally managed systems, it is essential to develop an adequate evaluation system. This system reflects the degree of achievement of logistic objectives related to the level of autonomous control and the level of complexity. Within the Collaborative Research Centre 637 "Autonomous Cooperating Logistic Processes: A

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Summary. The increasing complexity of production and logistics networks and the requirement of higher flexibility lead to a change of paradigm in control: Autonomously controlled systems where decisions are taken by parts or goods themselves become more attractive. The question of stability is an important issue for the dynamics of such systems. In this paper we are going to touch this question for a special production network with autonomous control. The stability region for a corresponding fluid model is found empirically. We point out that further mathematical investigations have to be undertaken to develop some stability criteria for autonomous systems.