the general aspects of discrete flow modeling in the most diverse networks. The technical level of this chapter is limited to a basic knowledge of graphs and Petri nets, DEVS, alongside a fundamental familiarity of statistics and probability.
On the basis of a model deemed as representative of the real phenomena implemented in a network, an analyst will have at their disposal state-of-the-art performance evaluation and enhancement methods. As with most scientific domains, we will proceed here with exact methods, heuristic or digital simulation techniques; or even a combination of these different approaches. These exact methods respond to a scientific ideal by pre-establishing a parametric solution, and are thus valid for a class of cases. On the one hand, the strengths of exact methods are multiple:
– The speed of performance evaluation by the simple instantiation of parameter values for pre-established solutions.
– The facilitation of reverse engineering logic that consists, for a given performance objective, of determining the values of the parameters that lead to the desired performance.
– More broadly, by providing a deep understanding of the link between system configuration and resulting performance.
On the other hand, the weak point of exact methods and, to a lesser extent, of approximate (heuristic) methods of resolution, is the requisite that the case in question respect the hypotheses required by the theoretical pre-resolution of a general problem, in turn reserving this approach either for systems of low complexity, or else those belonging to strongly typical case classes. A contrario, complex networks require the use of a simulation technique, the advantages and limitations of which are opposite to those of exact methods. Indeed, the strong point of simulation is its applicability to the evaluation of any network, provided that it has previously modeled the main mechanisms of its operation. However, the weak point of simulation is the lack of an inverse model, which deprives the analyst of a deeper understanding of the connections between the network configuration and the resulting performance. Exploring this link requires empirical iterative simulation campaigns, which may encounter computational, time and cost constraints.
Part 2, “Network Analysis Methods and Applications”, illustrates the alternative mentioned above. Chapter 3 brings together the main theoretical methods of evaluating, and even optimizing, the kinematics of discrete flows and the performances associated with them, within uncomplicated networks of a particular type. These are, on the one hand, networks with additive flows responding to Kirchhoff’s current law, and, on the other hand, networks with synchronized flows, examples of which can be found in flow-shop organizations of manufacturing production. We will thus deal with a workshop sizing problem by expressing the production rate as a function of the operating times of the machines and the number of containers in circulation. A contrario, Chapter 4 presents the general simulation techniques that can be used for network analysis, as well as a specific application for an analysis of the propagation process in social network flows. The technical nature of Chapters 3 and 4 may require some external reading (see Table I.1).
Part 3, “Case Studies”, illustrates, through examples from projects, the similarity and specificities of network engineering in various fields: Smart Grid, forestry logistics, information dissemination within a social network.
For each case, we will first present a project description sheet summarizing:
– the function or nature of the service offered by the network;
– the type of network: topological (the nodes represent fixed places) versus sociological (the nodes represent mobile individuals);
– the mode of user inclusion: are they circulating entities, are they associated with network nodes, if so which ones (source nodes, intermediaries, terminals)?
– whether or not the network infrastructure is dedicated;
– the possible intermediation of operators;
– the nature of the flows (physical versus intangible, continuous versus discrete) and the unit of flow;
– the mode of transport ensuring the flows (ambient vs. routing);
– the command mode (centralized, on-board, distributed);
– the engineering context relating to the project presented (design, redesign, management) and the issue motivating the study (evaluation, optimization);
– the analysis tools used (formal resolution, optimization, numerical simulation).
Table I.1 seeks to assist the reader in identifying key areas of knowledge ahead of reading certain chapters.
Table I.1. Required reading
| Title | Prerequisite knowledge | ||
| Part 1 | Chapter 1 | Network Typology | – |
| Chapter 2 | Modeling Discrete Flow Networks | Graphs, Petri nets, statistics and probabilities (bases) | |
| Part 2 | Chapter 3 | Exact Methods Applied to the Flow Analysis of Topological Networks | Graphs, Petri nets, statistics and probabilities |
| Chapter 4 | Simulation Techniques Applied to the Analysis of Sociological Networks | State machines Agent languages | |
| Part 3 | Chapter 5 | Smart Grid | Optimization concepts |
| Chapter 6 | Forestry Logistics | Optimization concepts | |
| Chapter 7 | Multi-layered Digital Social Networks | Multi-agent simulation, networks and processes | |
Конец ознакомительного фрагмента.
