have always favored the current followed by theorists, and more and more phenomena have been described, explained and interpreted. Advances have made it possible to achieve immense scientific progress and better control our environment. By applying such principles in specific areas of industrial engineering, such as planning or scheduling, we could say: “if we know the position, condition and manufacturing process of each product in a production system, it is then possible to precisely determine the situation and condition of that system in the future, as well as how it will evolve”. This obviously requires in-depth and rudimentary knowledge of technical data such as nomenclatures and ranges, a technical description and history of products and processes, etc.
However, considering multi-product and multi-process systems involving hundreds of operations and references, we cannot calculate or predict a specific event at a future time. The same is true for the dynamic behavior of this system: this is simply due to the application of the principles of uncertainty as defined by Heisenberg and to nonlinear amplification phenomena relating to the system under study. In addition, our scientific theories are more and more elaborate, and mathematical formulas and demonstrations are more and more complicated, in order to be able to identify the increasingly imperceptible, growing and hidden difficulties in phenomena.
On another level, the change in industrial needs nowadays imposes more stringent requirements to take into account technical (dismantling, reuse of components), social (customization of products), ecological and economic (pollution, energy savings) constraints. These constraints are grouped under the name NMPP (New Manufacturing Production Paradigm) and their consideration in future industrial management systems raises adaptation problems in terms of approaches, techniques and methods. The transformation of a discipline is attributed to the development of a new technique or the use of a new technique that has been ignored until now. In fact, it is never a question of developing a new technique: it is the nature of the results obtained that prevails and enables us, compared to other approaches, to implement or reveal new properties.
In the following, we will focus more specifically, after defining it, on the behavioral complexity of a system and explore the notion of deterministic chaos. Its nature is fractal and we will see how to exploit its properties in complex industrial systems.
1.1.2. What are the problems to be solved?
The ultimate objective – the purpose – of a complex system is the gain, which means that it tries to achieve, collectively, an overall objective. We therefore continue here on the topic of intentions, but not on firm and rational commitments, rather “safe” strategies to achieve an overall optimum. Indeed, in a programmable network, comprising many interacting elements and complex behavior, we are confronted with strange attractors; we cannot predict in advance the attractor on which we are, at which precise point in the cycle we are and at which precise horizon we will have converged. Methodological elements need to be defined to identify which sets of objectives can be achieved.
We can be inspired by the approaches used in the game of checkers for example. In this case and according to a context, each partner, when playing, chooses a tactic and explores the moves as much as possible; he or she tries to anticipate the opponent’s reactions, evaluates them and decides on the least bad or best possible movement. By doing so, the player optimizes an economic function over a given time horizon. On the contrary, to accelerate the process and according to past experiences, he or she will be led to carry out reflex actions that are the result of winning repetitive strategies and that will have been the subject of successive learning. In this simple example, relating to a specific game, we see an interesting approach emerging:
– it is a system in which agents are intelligent (i.e. with behavior capable of emulating, in part, that of the human brain), autonomous, capable of communicating or exchanging information with partners or agents, with whom they are in a competitive or cooperative situation (hence the notion of conflicting objectives!);
– it is a situation and a mode of operation that we encounter, to a greater or lesser extent, in any distributed system and whatever the field considered. For example, we can cite decision-making problems in industry, the evolution of the immune system in a cell, the phenomena of metabolic adaptation in a living being, the flexibility of the behavior of a population of individuals in the context of the human and social sciences, etc.;
– understanding how global objectives and behaviors can emerge is a key factor in guiding the evolution of complex systems at the structural, organizational and operational levels, in order to move step by step towards a predefined goal.
Many other examples exist in chemistry, economics, metabolism, the immune system, etc. However, communication techniques between agents based on game theory make it possible to define very elaborate strategies whose evolutions and results are impossible to guess. Indeed, several elements specific to a complex system come into play:
– there are many interactions in a given neighborhood;
– each element modifies not only its own state, but also that of its close neighbors, according to rules with a low visibility horizon;
– the objectives are local but it is common for them to overlap with those of the neighborhood and to be in conflict with others;
– each element tries to improve a number of its own properties and reduce the least valuable or effective ones in relation to a given criterion.
From these examples, it can easily be deduced that the strategies and tactics commonly used in production or industrial engineering are not applicable here. Indeed, the systems we currently have are not decomposable and are nonlinear. Moreover, because of all the existing diffuse feedback, it is impossible to start from a global objective (linked to quality or performance) and apply it to the subsystems and finally to the agents, i.e. the elementary components of the system.
For this, and in order to deduce how to proceed in practice, we can refer to Sontag’s work in Mathematical Control Theory [SON 98]. This work describes how to make local adjustments to system parameters to achieve certain objectives.
However, a different approach in Control Theory, based on Horowitz’s work [ALB 02], consists of designing feedback loops and links such that they allow the system to evolve within predefined tolerance bands.
Finally, the strategies developed by IBM as part of the Deep Blue project in the 1990s to improve decision-making processes (with the famous application to chess games) are remarkable in the sense that they combine the concepts we have just mentioned with learning techniques.
1.1.3. What is the “engineering” approach developed here?
This chapter is intended to present a modern and global view of the design and management of complex systems. In terms of engineering, the problem can be approached in two different ways:
– either we start from the production system as it is, and we adapt the production management system to manage it as well as possible. New management methods are therefore defined, a different IDSS (Interactive Decision Support System) is designed and the decision-making process of the decision-maker is modified, etc., in order to try to control the effects of this complexity and to keep control over the production system. We remain confined to method engineering (what we could call Manufacturing Management System Engineering);
– or the production system itself is modified, i.e. its structure, organization and possibly its architecture. We then move into a radically different field: that of Process Design or Manufacturing Process Engineering.
For
