From Logistic Networks to Social Networks

Jean-Paul Bourrières is Emeritus Professor at the University of Bordeaux, France. Nathalie Pinède is Associate Professor at Bordeaux Montaigne University, France. Mamadou Kaba Traoré is Professor at the University of Bordeaux, France. Gregory Zacharewicz is Professor at IMT Mines Alès, France.

• Foreword ixIntroduction xiPart 1. Network Variety and Modeling 1Chapter 1. Network Typology 31.1. Introduction 31.1.1. Network description levels 31.1.2. Network, graph and flow 41.1.3. Shared or dedicated infrastructure 51.1.4. User inclusion 61.2. The principal networks 61.2.1. (Human) transport networks 61.2.2. (Goods) distribution and collection networks 71.2.3. Dedicated distribution and collection networks (of fluids and energy) 81.2.4. IT networks 91.2.5. Communication networks 91.2.6. Social and digital social networks 101.3. Characterization and typology of networks 111.3.1. Key characteristics 111.3.2. Network integration 121.3.3. Typology 131.4. Engineering issues 161.5. Performance indicators, evaluation, optimization 181.5.1. Performance indicators 181.5.2. Evaluation and optimization 201.6. Conclusion 23Chapter 2. Modeling Discrete Flow Networks 252.1. Introduction 252.2. Structure 282.3. Characterization of a discrete flow 302.3.1. Statistical description 302.3.2. Probabilistic description 322.4. Activities 322.5. Control system 372.6. Resources 402.7. Fluid kinematics 412.7.1. Flow/resource/decision synchronization 422.7.2. Congestion phenomenon 482.7.3. Dissemination of information in social networks 512.8. Formalisms for modeling flows in a network 522.8.1. BPM tools 532.8.2. Timed Petri nets 532.8.3. Flow networks 542.8.4. Queuing networks 552.9. Multi-modeling 572.9.1. Multi-formalism versus mono-formalism 572.9.2. The DEVS hierarchical model 602.9.3. Multi-layer networks 622.10. Conclusion 64Part 2. Network Analysis Methods and Applications 67Chapter 3. Exact Methods Applied to the Flow Analysis of Topological Networks 693.1. Introduction 693.2. Additive flow networks – deterministic modelling by flow networks 713.2.1. Two-terminal series–parallel graph 723.2.2. General case – max-flow/min-cut 743.3. Additive flow networks – stochastic modelling by queuing networks 763.4. Synchronized flow networks – modeling by timed event graphs 813.4.1. Steady-state analysis of timed event graphs 813.4.2. Example of application: sizing a flow-shop 833.5. Conclusion 88Chapter 4. Simulation Techniques Applied to the Analysis of Sociological Networks 914.1. Introduction 914.2. Simulation techniques 924.2.1. Discrete event simulation (worldviews) 944.2.2. DEVS formalism 964.2.3. Coupling simulation/resolutive methods 1004.2.4. Distributed simulation 1024.2.5. Architectural solutions 1034.2.6. Time management and synchronization 1044.2.7. Pessimistic approach 1044.2.8. Optimistic approach 1054.2.9. HLA 1064.2.10. Cosimulation 1074.2.11. FMI/FMU 1084.2.12. FMI/FMU and HLA coupling 1094.3. Simulation of flows in sociological networks 1104.3.1. Behavioral simulation based on DEVS formalism 1114.3.2. Application study 1134.4. Conclusion 116Part 3. Case Studies 119Chapter 5. Smart Grid 1215.1. Summary of the study 1225.2. Demand profile 1225.3. Solar power station, fuel station and regional import 1235.4. Hydroelectric power station and PHES 1235.5. Operational issues 1245.6. Model 1255.6.1. Decision variables 1255.6.2. Constraints 1265.6.3. Objective function 1275.7. Optimization results 128Chapter 6. Forestry Logistics 1316.1. Summary of the study 1326.2. Forest timber supply problem 1326.3. Tactical planning model 1346.4. Logistics benchmarking 1366.4.1. AS IS scenario (non-collaborative logistics) 1366.4.2. TO BE scenario (collaborative logistics) 1376.4.3. Results 1386.5. Conclusion 139Chapter 7. Multi-layered Digital Social Networks 1437.1. Summary of the study 1447.2. Digital social networks 1447.3. Studying digital social networks via an interview broadcast 1457.3.1. Pre-interview social network scenario 1467.3.2. Social network audience 1487.4. Modeling and simulation 1487.4.1. Modeling the interview production and broadcast processes 1487.4.2. MSN/HLA simulation architecture 1497.5. Simulation results 1527.6. Conclusion and perspectives 154References 157Index 167