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020 _a9781786306579
020 _a9781119988700
_q(electronic bk.)
020 _a1119988705
_q(electronic bk.)
024 7 _a10.1002/9781119988700
_2doi
035 _a(OCoLC)1333221454
040 _aDG1
_beng
_erda
_epn
_cDG1
041 _aeng
050 4 _aQA402
082 0 4 _a003
_223
100 1 _aBourrieres, Jean-Paul.
_0http://id.loc.gov/authorities/names/n2010002485.
245 1 0 _aFrom logistic networks to social networks :
_bsimilarities, specificities, modeling, evaluation /
_cJean-Paul Bourrieres, Nathalie Pinede, Mamadou Kaba Traore, Gregory Zacharewicz.
264 1 _aLondon, UK :
_bISTE, Ltd. ;
_aHoboken, NJ :
_bWiley,
_c2022.
300 _a1 online resource.
336 _atext
_btxt
_2rdacontent.
337 _acomputer
_bc
_2rdamedia.
338 _aonline resource
_bcr
_2rdacarrier.
505 0 _aTable of Contents Foreword ix Introduction xi Part 1. Network Variety and Modeling 1 Chapter 1. Network Typology 3 1.1. Introduction 3 1.1.1. Network description levels 3 1.1.2. Network, graph and flow 4 1.1.3. Shared or dedicated infrastructure 5 1.1.4. User inclusion 6 1.2. The principal networks 6 1.2.1. (Human) transport networks 6 1.2.2. (Goods) distribution and collection networks 7 1.2.3. Dedicated distribution and collection networks (of fluids and energy) 8 1.2.4. IT networks 9 1.2.5. Communication networks 9 1.2.6. Social and digital social networks 10 1.3. Characterization and typology of networks 11 1.3.1. Key characteristics 11 1.3.2. Network integration 12 1.3.3. Typology 13 1.4. Engineering issues 16 1.5. Performance indicators, evaluation, optimization 18 1.5.1. Performance indicators 18 1.5.2. Evaluation and optimization 20 1.6. Conclusion 23 Chapter 2. Modeling Discrete Flow Networks 25 2.1. Introduction 25 2.2. Structure 28 2.3. Characterization of a discrete flow 30 2.3.1. Statistical description 30 2.3.2. Probabilistic description 32 2.4. Activities 32 2.5. Control system 37 2.6. Resources 40 2.7. Fluid kinematics 41 2.7.1. Flow/resource/decision synchronization 42 2.7.2. Congestion phenomenon 48 2.7.3. Dissemination of information in social networks 51 2.8. Formalisms for modeling flows in a network 52 2.8.1. BPM tools 53 2.8.2. Timed Petri nets 53 2.8.3. Flow networks 54 2.8.4. Queuing networks 55 2.9. Multi-modeling 57 2.9.1. Multi-formalism versus mono-formalism 57 2.9.2. The DEVS hierarchical model 60 2.9.3. Multi-layer networks 62 2.10. Conclusion 64 Part 2. Network Analysis Methods and Applications 67 Chapter 3. Exact Methods Applied to the Flow Analysis of Topological Networks 69 3.1. Introduction 69 3.2. Additive flow networks – deterministic modelling by flow networks 71 3.2.1. Two-terminal series–parallel graph 72 3.2.2. General case – max-flow/min-cut 74 3.3. Additive flow networks – stochastic modelling by queuing networks 76 3.4. Synchronized flow networks – modeling by timed event graphs 81 3.4.1. Steady-state analysis of timed event graphs 81 3.4.2. Example of application: sizing a flow-shop 83 3.5. Conclusion 88 Chapter 4. Simulation Techniques Applied to the Analysis of Sociological Networks 91 4.1. Introduction 91 4.2. Simulation techniques 92 4.2.1. Discrete event simulation (worldviews) 94 4.2.2. DEVS formalism 96 4.2.3. Coupling simulation/resolutive methods 100 4.2.4. Distributed simulation 102 4.2.5. Architectural solutions 103 4.2.6. Time management and synchronization 104 4.2.7. Pessimistic approach 104 4.2.8. Optimistic approach 105 4.2.9. HLA 106 4.2.10. Cosimulation 107 4.2.11. FMI/FMU 108 4.2.12. FMI/FMU and HLA coupling 109 4.3. Simulation of flows in sociological networks 110 4.3.1. Behavioral simulation based on DEVS formalism 111 4.3.2. Application study 113 4.4. Conclusion 116 Part 3. Case Studies 119 Chapter 5. Smart Grid 121 5.1. Summary of the study 122 5.2. Demand profile 122 5.3. Solar power station, fuel station and regional import 123 5.4. Hydroelectric power station and PHES 123 5.5. Operational issues 124 5.6. Model 125 5.6.1. Decision variables 125 5.6.2. Constraints 126 5.6.3. Objective function 127 5.7. Optimization results 128 Chapter 6. Forestry Logistics 131 6.1. Summary of the study 132 6.2. Forest timber supply problem 132 6.3. Tactical planning model 134 6.4. Logistics benchmarking 136 6.4.1. AS IS scenario (non-collaborative logistics) 136 6.4.2. TO BE scenario (collaborative logistics) 137 6.4.3. Results 138 6.5. Conclusion 139 Chapter 7. Multi-layered Digital Social Networks 143 7.1. Summary of the study 144 7.2. Digital social networks 144 7.3. Studying digital social networks via an interview broadcast 145 7.3.1. Pre-interview social network scenario 146 7.3.2. Social network audience 148 7.4. Modeling and simulation 148 7.4.1. Modeling the interview production and broadcast processes 148 7.4.2. MSN/HLA simulation architecture 149 7.5. Simulation results 152 7.6. Conclusion and perspectives 154 References 157 Index 167
545 0 _aAbout the Author 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.
650 0 _aSystem analysis.
_0http://id.loc.gov/authorities/subjects/sh85131733.
655 4 _aElectronic books.
700 1 _aPinede, Nathalie.
_0http://id.loc.gov/authorities/names/nb2014007601.
700 1 _aTraore, Mamadou K.
_q(Mamadou Kaba)
_0http://id.loc.gov/authorities/names/no2012013542.
700 1 _aZacharewicz, Gregory.
_0http://id.loc.gov/authorities/names/n2019183667.
856 4 0 _uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781119988700
_yFull text is available at Wiley Online Library Click here to view
942 _2ddc
_cER