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Smart Decisions in Complex Systems

ISBN: 978-1-119-36873-1
376 pages
June 2017, Wiley-ISTE
Smart Decisions in Complex Systems (1119368731) cover image

Description

Faced with ever-increasing complexity on a daily basis, the decision-makers of today are struggling to find the appropriate models, methods and tools to face the issues arising in complex systems across all levels of global operations.

Having, in the past, resorted to outdated approaches which limit problem-solving to linear world views, we must now capitalize on complexities in order to succeed and progress in our society.

This book provides a guide to harnessing the wealth inherent to complex systems. It organizes the transition to complex decision-making in all business spheres while providing many examples in various application domains.

The authors offer fresh developments for understanding and mastering the global “uberization” of the economy, the post-modern management of computer-assisted production and the rise of cognitive robotics science applications.

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Table of Contents

Contents

Preface xiii

Acknowledgments    xvii

List of Acronyms  xix

Introduction   xxv

Part 1. 1

Chapter 1. The Foundations of Complexity  3

1.1. Complexities and simplexities: paradigms and perspectives    3

1.1.1. Positioning the problem 4

1.1.2. Reminders, basics and neologisms    5

1.1.3. What are the analytical steps in a complex system?   16

1.1.4. Organization and management principles in complex systems    31

1.1.5. Action and decision processes in self-organized systems    35

1.1.6. Notions of centralization and decentralization    36

1.2. What is the prerequisite for the handling of a complex system?    43

1.3. Applications: industrial complex systems   45

1.3.1. Distributed workshop management system     45

1.3.2. Analysis and diagnosis of a complex system     47

1.3.3. Some recommendations and comments to conclude  48

1.4. Time to conclude    50

1.4.1. Summary  50

1.4.2. Lessons and perspectives   51

Part 2. 53

Chapter 2. Evidencing Field Complexity   55

2.1. Introduction   55

2.2. Qualitative study of deterministic chaos in a dynamic simple system   58

2.2.1. Description of a few simple cases    58

2.2.2. Initial conditions related to the emergence of chaos  59

2.2.3. Modeling and mathematical analysis of chaos    62

2.2.4. Application at the level of a simple cell  63

2.3. Test for the presence of deterministic chaos in a simple dynamic system  68

2.3.1. Characterization of the systems studied  69

2.3.2. A general question: is there deterministic chaos?   70

2.4. Properties of chaos in complex systems   77

2.4.1. Study of an elementary cell   77

2.4.2. Complex cellular systems   81

2.5. Effects of fractal chaos in “Complexity” theory     83

2.5.1. Organized complexity 83

2.5.2. Innovative complexity 84

2.5.3. Random complexity  85

2.5.4. Principles of implementation     87

2.6. Self-organization: relations and the role of chaos    87

2.6.1. Introduction    87

2.6.2. How to combine self-organization and chaos    88

2.6.3. Critical self-organized systems     89

2.6.4. Networked systems and co-operative systems    90

2.6.5. The three states of a dynamic complex system    93

2.6.6. Towards a typology of behavioral complexity    94

2.7. Applications: introduction of new concepts in systems   95

2.7.1. Questions on the management of complex industrial systems  95

2.7.2. Implementation of the concepts of chaos and self-organization  96

2.8. Conclusions   98

Chapter 3. The New “Complex” Operational Context  101

3.1. The five phases of economy – how everything

accelerates at the same time  101

3.2. The expected impact on just about everything     105

Chapter 4. Taking Up Complexity     109

4.1. Taking into account complex models    109

4.1.1. A brief overview of the approach called “complexity”  109

4.1.2. Another (bio-inspired) vision of the world: universality    112

4.1.3. How to address complexity in this universal world?  115

4.1.4. The usefulness of this book   116

4.2. Economy and management of risks     117

4.2.1. Important challenges to raise     117

4.2.2. Adapted vocabulary that it is useful to adopt     118

4.2.3. What do we mean by dynamic pricing?  119

Part 3. 121

Chapter 5. Tackling Complexity with a Methodology   123

5.1. Any methodology must first enrich the systemic interrelationships  123

5.1.1. The innovation economy: the dynamic management of innovation   124

5.1.2. A basic mechanism of efficient innovation     125

5.1.3. The benefits of such a shift mechanism  126

5.2. Towards a transdisciplinary co-economy   126

Chapter 6. Management and Control of Complex Systems   129

6.1. Introduction   129

6.2. Complex systems: the alternatives     132

6.2.1. Notions of sociability in agent communities     132

6.2.2. The evolutionary principles of complex systems   134

6.3. Control principles of production systems   135

6.3.1. Introduction    135

6.3.2. Control: by scheduling or by configuration?     136

6.3.3. The tools used in monitoring and control  140

6.4. PABADIS: an example of decentralized control     141

6.4.1. Introduction    141

6.4.2. Context and objectives of the PABADIS project   142

6.4.3. Conceptual overview of PABADIS   142

6.4.4. Principle of adopted convergence: the inverse solution    144

6.4.5. Implementation   145

6.5. Generalization of the concepts and mechanisms     146

6.5.1. Introduction    146

6.5.2. Allocation of resources: the agents in complex production systems    147

6.5.3. Allocation of resources: the negotiation protocols   147

6.5.4. Optimization of the resource allocation process    148

6.6. A basic mechanism of control – the auction  150

6.6.1. Introduction    150

6.6.2. The mechanism of the auction     151

6.6.3. Comparative review of the types of auctions     153

6.6.4. Findings on the interest of the auction mechanism   155

6.7. The control of self-organized systems    156

6.7.1. Introduction    156

6.7.2. The types and mechanisms of self-organization    157

6.7.3. Towards a dynamic integrated model: Cellular Automata (CA)   160

6.7.4. Self-organization: forms and configurations obtained  165

6.7.5. Conclusion and implementation of the ACCA concept, a major model   167

Chapter 7. Platforms for Taking up Complexity    169

7.1. The VFDCS: a platform for implementation  169

7.1.1. Controlling the phenomena of self-organization    171

7.1.2. Methodology for implementation and the validation of concepts   172

7.2. The application of VFDCS: the auction market     174

7.2.1. The concept of the “Container” in the auction market  176

7.2.2. Feedbacks and results 176

7.2.3. Discussion  178

7.3. The application of VFDCS: the virtual supply chain   179

7.3.1. Introduction    179

7.3.2. Architecture of the virtual supply chain  181

7.3.3. Results and comments 184

7.3.4. Conclusion  185

7.3.5. Enhancement of the multi-agent platform     186

7.4. General method for the control of systems  186

7.4.1. Introduction    186

7.4.2. Reminders and definitions   187

7.4.3. Analytical approach to consistency   188

7.4.4. Methods for the analysis and monitoring of performances  189

7.4.5. Critical analysis of the convergence of configurations  192

7.5. Conclusions and prospects 194

7.5.1. Synthesis  194

7.5.2. Discussion  195

7.5.3. Comparison of approaches, tools and applications   197

7.5.4. Results   199

Part 4. 201

Introduction to Part 4   203

Chapter 8. Applying Intrinsic Complexity:

The Uberization of the Economy     207

8.1. Preamble   207

8.2. The context: new opportunities and new consumption needs  207

8.3. The domains that are studied in this chapter  208

8.4. Concepts, definitions and remainders    209

8.4.1. Uberization    209

8.4.2. Digitalization of the economy     210

8.4.3. Collaborative consumption (CC)    211

8.4.4. Model generalization: the sharing economy     211

8.4.5. Participatory financing 211

8.5. The business model and key elements    213

8.5.1. Practicing networks  213

8.5.2. Positive and negative impacts of network applications   214

8.5.3. The problem of producer–consumers and consumer–producers    215

8.5.4. Underlying mechanisms: some differences with the usual economic systems 216

8.5.5. A form of social hypocrisy?   217

8.5.6. Generalization: the management rules for P2P    219

8.6. The problem of property and resource allocation.    220

8.6.1. The growing role of platforms     220

8.6.2. The prisoner’s dilemma 223

8.6.3. Games theory: an introduction     224

8.6.4. Nonlinear models in game theory    224

8.7. The uberization approach in context    226

8.7.1. Simplexification.   227

8.7.2. Increasing complexity: the influence of cognitive approaches    227

8.8. Generalization: the complexity of allocation problems   230

8.9. Conclusion   234

Chapter 9. Computer-assisted Production Management    235

9.1. Introduction and reminders 235

9.2. Intercommunication networks   236

9.2.1. Notions of complexity in networks   236

x Smart Decisions in Complex Systems

9.2.2. A few concepts of parallelism     237

9.2.3. Elements of parallelism and associated architectures  237

9.2.4. Transposition into industrial or social applications   239

9.3. Communication network topologies    240

9.3.1. Some characteristics of different network topologies  241

9.3.2. Construction of a hypercube     242

9.3.3. Notions of symmetry: cutting a hypercube     243

9.3.4. The shortest path between two processors     244

9.4. A few important properties 244

9.5. Analysis of new concepts and methods in manufacturing sciences: instabilities, responsiveness and flexibility     246

9.5.1. General approach: planning and scheduling     247

9.5.2. Illustration in management systems   247

9.5.3. Problems and remarks 250

9.5.4. Improvements in planning and scheduling     251

9.5.5. Improvements in configuration/reconfiguration    252

9.5.6. Global improvements through simulation     253

9.5.7. Inverse modeling and simulation    254

9.6. New concepts for managing complex systems     256

9.6.1. Traditional approach  257

9.6.2. Recent improvements in the management of systems  260

9.7. The change of conduct  264

9.8. Improvements in manufacturing: process balancing    266

9.9. Conclusion: main action principles in complex environments   267

Chapter 10. Complexity and Cognitive Robotics    271

10.1. Introduction  271

10.2. The new industrial revolution   272

10.3. The factory of the future: trend or revolution?     272

10.4. Inputs for the factory of the future and their impact on the industry’s professions  275

10.5. Conditions for success  276

10.6. The data sciences    277

10.6.1. Introduction to the characteristics of “Big Data”   277

10.6.2. The problem of Big Data   277

10.6.3. A new profession: the data scientist   279

10.6.4. Some ask, how will this be possible?  279

10.6.5. The field of large numbers   280

10.7. A few technologies in data sciences    281

10.7.1. The steps of reasoning based on the experience of the inductive approach and on the verification of hypotheses   281

10.7.2. The “Lasso” method 281

10.7.3. Kernel regression methods   282

10.7.4. The random forests  283

10.7.5. Neural networks   284

10.7.6. Comments on clustering and graph partitioning issues 286

10.7.7. Cognitive informatics – cognitivism   286

10.8. Mechanisms of conventional cognitive engineering   288

10.9. The new mechanisms of engineering    289

10.9.1. Transduction    289

10.9.2. Reasoning by constructed analogies   290

10.10. The study of links and relationships in large databases  290

10.10.1. Comment    291

10.11. Application of cognitive robotics: the Watson platform  291

10.11.1. Applications   292

10.12. The impossibilities and unpredictabilities of complexity    293

10.13. Current strategies of digitalization    295

10.13.1. Reference examples and discussion  296

10.13.2. GNOSIS  298

10.13.3. “Data is Centric”  299

10.14. Conclusion: a maximum risk economy   300

Bibliography   303

Index  327

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