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In the field of complexity, the presence of deterministic chaos in electronic circuits and signal processing is known to many automation engineers [MIR 95]. These include: looped systems with pulse modulation, or networks with switching elements, manipulators performing repetitive tasks, nonlinear recursive prediction, adaptive control and monitoring, etc.

In all these cases, the control and management methods of a system subjected to a chaotic phenomenon cannot be similar to those of a stable system; either stabilizing devices or disturbances are then introduced. The approach depends on the precise nature of their behavior.

In macroscopic and industrial engineering industrial systems, the basic assumptions and approaches used are still conventional. However, an interest is beginning to emerge around the “chaos” phenomenon because, between the order and turbulence phases, there is a poorly exploited area with interesting properties. We will describe some classes of situations in production systems where such phenomena may occur:

 – a network of workshops driven by MRP production management systems;

 – a flexible multi-product, multi-process workshop;

 – the distributed processing of information.

In order to qualitatively predict the evolution of these so-called “complex” systems, simulation models are often used, as we have already seen, based on a strong assumption: product and information flows are uniform and regular on a large scale. However, these have strong inhomogeneities that are also distributed over time according to particular density functions [MAS 97b]. This translates into very specific characteristics or phenomena that we have observed and that we summarize:

 – the distributions used are often of a known statistical type. However, as shown, the available data have no statistical significance and must be studied using new approaches (e.g. Levy distribution);

 – the magnitude and recombination phenomena related to feedback loops highlight the presence of deterministic chaos as well as large-scale fluctuations that lead to the emergence of order structures;

 – depending on the origin of the fluctuations manipulated, different classes of models must be used; what is valid and observed at the living level, or at the material level, what must be taken into account in an industrial system. For example:- the notions of initial quantum fluctuations taken into account at the agent level (such as those found in particle physics) can lead, during a phase of inflation and by propagation to its close neighbors, to the formation of simple and diluted aggregates [MAT 96a]. By placing themselves within an industrial system that is itself an assembly of agents, fluctuations in product flow do not correspond to strong initial disruptions and result in “apparently” random groupings at the end of the line,- typological defects related to phase transitions are due to symmetry breaks between fundamental interactions. However, these phenomena exist in chaotic systems where fluctuations are amplified and/or inhibited by feedback loops in order to generate clusters. This, of course, has a significant effect on product propagation times (product exit horizon) and therefore on the distribution of cycle time or T.A.T. (“Turn Around Time”). Similarly, a local defect will have a direct effect on close neighbors and more widespread effects on a global level due to the more or less strong interactions that may occur at long distances.

Based on these remarks, the following strategies can be formulated.