Smart Decisions in Complex Systems.
Massotte, Pierre.
Smart Decisions in Complex Systems. - 1st ed. - 1 online resource (381 pages)
Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- List of Acronyms -- Introduction: A World Swept by Complexity -- PART 1 -- 1. The Foundations of Complexity -- 1.1. Complexities and simplexities: paradigms and perspectives -- 1.1.1. Positioning the problem -- 1.1.2. Reminders, basics and neologisms -- 1.1.3. What are the analytical steps in a complex system? -- 1.1.4. Organization and management principles in complex systems -- 1.1.5. Action and decision processes in self-organized systems -- 1.1.6. Notions of centralization and decentralization -- 1.2. What is the prerequisite for the handling of a complex system? -- 1.3. Applications: industrial complex systems -- 1.3.1. Distributed workshop management system -- 1.3.2. Analysis and diagnosis of a complex system -- 1.3.3. Some recommendations and comments to conclude -- 1.4. Time to conclude -- 1.4.1. Summary -- 1.4.2. Lessons and perspectives -- PART 2 -- 2. Evidencing Field Complexity -- 2.1. Introduction -- 2.2. Qualitative study of deterministic chaos in a dynamic simple system -- 2.2.1. Description of a few simple cases -- 2.2.2. Initial conditions related to the emergence of chaos -- 2.2.3. Modeling and mathematical analysis of chaos -- 2.2.4. Application at the level of a simple cell -- 2.3. Test for the presence of deterministic chaos in a simple dynamic system -- 2.3.1. Characterization of the systems studied -- 2.3.2. A general question: is there deterministic chaos? -- 2.4. Properties of chaos in complex systems -- 2.4.1. Study of an elementary cell -- 2.4.2. Complex cellular systems -- 2.5. Effects of fractal chaos in "Complexity" theory -- 2.5.1. Organized complexity -- 2.5.2. Innovative complexity -- 2.5.3. Random complexity -- 2.5.4. Principles of implementation -- 2.6. Self-organization: relations and the role of chaos. 2.6.1. Introduction -- 2.6.2. How to combine self-organization and chaos -- 2.6.3. Critical self-organized systems -- 2.6.4. Networked systems and co-operative systems -- 2.6.5. The three states of a dynamic complex system -- 2.6.6. Towards a typology of behavioral complexity -- 2.7. Applications: introduction of new concepts in systems -- 2.7.1. Questions on the management of complex industrial systems -- 2.7.2. Implementation of the concepts of chaos and self-organization -- 2.8. Conclusions -- 3. The New "Complex" Operational Context -- 3.1. The five phases of economy - how everything accelerates at the same time -- 3.2. The expected impact on just about everything -- 4. Taking Up Complexity -- 4.1. Taking into account complex models -- 4.1.1. A brief overview of the approach called "complexity" -- 4.1.2. Another (bio-inspired) vision of the world: universality -- 4.1.3. How to address complexity in this universal world? -- 4.1.4. The usefulness of this book -- 4.2. Economy and management of risks -- 4.2.1. Important challenges to raise -- 4.2.2. Adapted vocabulary that it is useful to adopt -- 4.2.3. What do we mean by dynamic pricing? -- PART 3 -- 5. Tackling Complexity with a Methodology -- 5.1. Any methodology must first enrich the systemic interrelationships -- 5.1.1. The innovation economy: the dynamic management of innovation -- 5.1.2. A basic mechanism of efficient innovation -- 5.1.3. The benefits of such a shift mechanism -- 5.2. Towards a transdisciplinary co-economy -- 6. Management and Control of Complex Systems -- 6.1. Introduction -- 6.2. Complex systems: the alternatives -- 6.2.1. Notions of sociability in agent communities -- 6.2.2. The evolutionary principles of complex systems -- 6.3. Control principles of production systems -- 6.3.1. Introduction -- 6.3.2. Control: by scheduling or by configuration?. 6.3.3. The tools used in monitoring and control -- 6.4. PABADIS: an example of decentralized control -- 6.4.1. Introduction -- 6.4.2. Context and objectives of the PABADIS project -- 6.4.3. Conceptual overview of PABADIS -- 6.4.4. Principle of adopted convergence: the inverse solution -- 6.4.5. Implementation -- 6.5. Generalization of the concepts and mechanisms -- 6.5.1. Introduction -- 6.5.2. Allocation of resources: the agents in complex production systems -- 6.5.3. Allocation of resources: the negotiation protocols -- 6.5.4. Optimization of the resource allocation process -- 6.6. A basic mechanism of control - the auction -- 6.6.1. Introduction -- 6.6.2. The mechanism of the auction -- 6.6.3. Comparative review of the types of auctions -- 6.6.4. Findings on the interest of the auction mechanism -- 6.7. The control of self-organized systems -- 6.7.1. Introduction -- 6.7.2. The types and mechanisms of self-organization -- 6.7.3. Towards a dynamic integrated model: Cellular Automata (CA) -- 6.7.4. Self-organization: forms and configurations obtained -- 6.7.5. Conclusion and implementation of the ACCA concept, a major model -- 7. Platforms for Taking up Complexity -- 7.1. The VFDCS: a platform for implementation -- 7.1.1. Controlling the phenomena of self-organization -- 7.1.2. Methodology for implementation and the validation of concepts -- 7.2. The application of VFDCS: the auction market -- 7.2.1. The concept of the "Container" in the auction market -- 7.2.2. Feedbacks and results -- 7.2.3. Discussion -- 7.3. The application of VFDCS: the virtual supply chain -- 7.3.1. Introduction -- 7.3.2. Architecture of the virtual supply chain -- 7.3.3. Results and comments -- 7.3.4. Conclusion -- 7.3.5. Enhancement of the multi-agent platform -- 7.4. General method for the control of systems -- 7.4.1. Introduction -- 7.4.2. Reminders and definitions. 7.4.3. Analytical approach to consistency -- 7.4.4. Methods for the analysis and monitoring of performances -- 7.4.5. Critical analysis of the convergence of configurations -- 7.5. Conclusions and prospects -- 7.5.1. Synthesis -- 7.5.2. Discussion -- 7.5.3. Comparison of approaches, tools and applications -- 7.5.4. Results -- PART 4 -- Introduction to Part 4: Overviweing Trends to Complex Fututes -- 8. Applying Intrinsic Complexity: The Uberization of the Economy -- 8.1. Preamble -- 8.2. The context: new opportunities and new consumption needs -- 8.3. The domains that are studied in this chapter -- 8.4. Concepts, definitions and remainders -- 8.4.1. Uberization -- 8.4.2. Digitalization of the economy -- 8.4.3. Collaborative consumption (CC) -- 8.4.4. Model generalization: the sharing economy -- 8.4.5. Participatory financing -- 8.5. The business model and key elements -- 8.5.1. Practicing networks -- 8.5.2. Positive and negative impacts of network applications -- 8.5.3. The problem of producer-consumers and consumer-producers -- 8.5.4. Underlying mechanisms: some differences with the usual economic systems -- 8.5.5. A form of social hypocrisy? -- 8.5.6. Generalization: the management rules for P2P -- 8.6. The problem of property and resource allocation -- 8.6.1. The growing role of platforms -- 8.6.2. The prisoner's dilemma -- 8.6.3. Games theory: an introduction -- 8.6.4. Nonlinear models in game theory -- 8.7. The uberization approach in context -- 8.7.1. Simplexification -- 8.7.2. Increasing complexity: the influence of cognitive approaches -- 8.8. Generalization: the complexity of allocation problems -- 8.9. Conclusion -- 9. Computer-assisted Production Management -- 9.1. Introduction and reminders -- 9.2. Intercommunication networks -- 9.2.1. Notions of complexity in networks [LET 11] -- 9.2.2. A few concepts of parallelism. 9.2.3. Elements of parallelism and associated architectures -- 9.2.4. Transposition into industrial or social applications -- 9.3. Commmunicatioon network topologies -- 9.3.1. Some characteristics of different network topologies -- 9.3.2. Construction of a hypercube -- 9.3.3. Notions of symmetry: cutting a hypercube -- 9.3.4. The shortest path between two processors -- 9.4. A few important properties -- 9.5. Analysis of new concepts and methods in manufacturing sciences: instabilities, responsiveness and flexibility -- 9.5.1. General approach: planning and scheduling -- 9.5.2. Illustration in management systems -- 9.5.3. Problems and remarks [MAS 14] -- 9.5.4. Improvements in planning and scheduling -- 9.5.5. Improvements in configuration/reconfiguration [MAS 99a, MAS 99b] -- 9.5.6. Global improvements through simulation -- 9.5.7. Inverse modeling and simulation [MAS 06] -- 9.6. New concepts for managing complex systems -- 9.6. New concepts for managing complex systems -- 9.6.1. Traditional approach -- 9.6.2. Recent improvements in the management of systems [MAS 14] -- 9.7. The change of conduct -- 9.8. Improvements in manufacturing: process balancing -- 9.9. Conclusion: main action principles in complex environments -- 10. Complexity and Cognitive Robotics -- 10.1. Introduction -- 10.2. The new industrial revolution -- 10.3. The factory of the future: trend or revolution? -- 10.4. Inputs for the factory of the future and their impact on the industry's professions -- 10.5. Conditions for success -- 10.6. The data sciences -- 10.6.1. Introduction to the characteristics of "Big Data" -- 10.6.2. The problem of Big Data -- 10.6.3. A new profession: the data scientist -- 10.6.4. Some ask, how will this be possible? -- 10.6.5. The field of large numbers -- 10.7. A few technologies in data sciences. 10.7.1. The steps of reasoning based on the experience of the inductive approach and on the verification of hypotheses.
9781119368731
System theory.
Electronic books.
Q295.M377 2017
003
Smart Decisions in Complex Systems. - 1st ed. - 1 online resource (381 pages)
Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- List of Acronyms -- Introduction: A World Swept by Complexity -- PART 1 -- 1. The Foundations of Complexity -- 1.1. Complexities and simplexities: paradigms and perspectives -- 1.1.1. Positioning the problem -- 1.1.2. Reminders, basics and neologisms -- 1.1.3. What are the analytical steps in a complex system? -- 1.1.4. Organization and management principles in complex systems -- 1.1.5. Action and decision processes in self-organized systems -- 1.1.6. Notions of centralization and decentralization -- 1.2. What is the prerequisite for the handling of a complex system? -- 1.3. Applications: industrial complex systems -- 1.3.1. Distributed workshop management system -- 1.3.2. Analysis and diagnosis of a complex system -- 1.3.3. Some recommendations and comments to conclude -- 1.4. Time to conclude -- 1.4.1. Summary -- 1.4.2. Lessons and perspectives -- PART 2 -- 2. Evidencing Field Complexity -- 2.1. Introduction -- 2.2. Qualitative study of deterministic chaos in a dynamic simple system -- 2.2.1. Description of a few simple cases -- 2.2.2. Initial conditions related to the emergence of chaos -- 2.2.3. Modeling and mathematical analysis of chaos -- 2.2.4. Application at the level of a simple cell -- 2.3. Test for the presence of deterministic chaos in a simple dynamic system -- 2.3.1. Characterization of the systems studied -- 2.3.2. A general question: is there deterministic chaos? -- 2.4. Properties of chaos in complex systems -- 2.4.1. Study of an elementary cell -- 2.4.2. Complex cellular systems -- 2.5. Effects of fractal chaos in "Complexity" theory -- 2.5.1. Organized complexity -- 2.5.2. Innovative complexity -- 2.5.3. Random complexity -- 2.5.4. Principles of implementation -- 2.6. Self-organization: relations and the role of chaos. 2.6.1. Introduction -- 2.6.2. How to combine self-organization and chaos -- 2.6.3. Critical self-organized systems -- 2.6.4. Networked systems and co-operative systems -- 2.6.5. The three states of a dynamic complex system -- 2.6.6. Towards a typology of behavioral complexity -- 2.7. Applications: introduction of new concepts in systems -- 2.7.1. Questions on the management of complex industrial systems -- 2.7.2. Implementation of the concepts of chaos and self-organization -- 2.8. Conclusions -- 3. The New "Complex" Operational Context -- 3.1. The five phases of economy - how everything accelerates at the same time -- 3.2. The expected impact on just about everything -- 4. Taking Up Complexity -- 4.1. Taking into account complex models -- 4.1.1. A brief overview of the approach called "complexity" -- 4.1.2. Another (bio-inspired) vision of the world: universality -- 4.1.3. How to address complexity in this universal world? -- 4.1.4. The usefulness of this book -- 4.2. Economy and management of risks -- 4.2.1. Important challenges to raise -- 4.2.2. Adapted vocabulary that it is useful to adopt -- 4.2.3. What do we mean by dynamic pricing? -- PART 3 -- 5. Tackling Complexity with a Methodology -- 5.1. Any methodology must first enrich the systemic interrelationships -- 5.1.1. The innovation economy: the dynamic management of innovation -- 5.1.2. A basic mechanism of efficient innovation -- 5.1.3. The benefits of such a shift mechanism -- 5.2. Towards a transdisciplinary co-economy -- 6. Management and Control of Complex Systems -- 6.1. Introduction -- 6.2. Complex systems: the alternatives -- 6.2.1. Notions of sociability in agent communities -- 6.2.2. The evolutionary principles of complex systems -- 6.3. Control principles of production systems -- 6.3.1. Introduction -- 6.3.2. Control: by scheduling or by configuration?. 6.3.3. The tools used in monitoring and control -- 6.4. PABADIS: an example of decentralized control -- 6.4.1. Introduction -- 6.4.2. Context and objectives of the PABADIS project -- 6.4.3. Conceptual overview of PABADIS -- 6.4.4. Principle of adopted convergence: the inverse solution -- 6.4.5. Implementation -- 6.5. Generalization of the concepts and mechanisms -- 6.5.1. Introduction -- 6.5.2. Allocation of resources: the agents in complex production systems -- 6.5.3. Allocation of resources: the negotiation protocols -- 6.5.4. Optimization of the resource allocation process -- 6.6. A basic mechanism of control - the auction -- 6.6.1. Introduction -- 6.6.2. The mechanism of the auction -- 6.6.3. Comparative review of the types of auctions -- 6.6.4. Findings on the interest of the auction mechanism -- 6.7. The control of self-organized systems -- 6.7.1. Introduction -- 6.7.2. The types and mechanisms of self-organization -- 6.7.3. Towards a dynamic integrated model: Cellular Automata (CA) -- 6.7.4. Self-organization: forms and configurations obtained -- 6.7.5. Conclusion and implementation of the ACCA concept, a major model -- 7. Platforms for Taking up Complexity -- 7.1. The VFDCS: a platform for implementation -- 7.1.1. Controlling the phenomena of self-organization -- 7.1.2. Methodology for implementation and the validation of concepts -- 7.2. The application of VFDCS: the auction market -- 7.2.1. The concept of the "Container" in the auction market -- 7.2.2. Feedbacks and results -- 7.2.3. Discussion -- 7.3. The application of VFDCS: the virtual supply chain -- 7.3.1. Introduction -- 7.3.2. Architecture of the virtual supply chain -- 7.3.3. Results and comments -- 7.3.4. Conclusion -- 7.3.5. Enhancement of the multi-agent platform -- 7.4. General method for the control of systems -- 7.4.1. Introduction -- 7.4.2. Reminders and definitions. 7.4.3. Analytical approach to consistency -- 7.4.4. Methods for the analysis and monitoring of performances -- 7.4.5. Critical analysis of the convergence of configurations -- 7.5. Conclusions and prospects -- 7.5.1. Synthesis -- 7.5.2. Discussion -- 7.5.3. Comparison of approaches, tools and applications -- 7.5.4. Results -- PART 4 -- Introduction to Part 4: Overviweing Trends to Complex Fututes -- 8. Applying Intrinsic Complexity: The Uberization of the Economy -- 8.1. Preamble -- 8.2. The context: new opportunities and new consumption needs -- 8.3. The domains that are studied in this chapter -- 8.4. Concepts, definitions and remainders -- 8.4.1. Uberization -- 8.4.2. Digitalization of the economy -- 8.4.3. Collaborative consumption (CC) -- 8.4.4. Model generalization: the sharing economy -- 8.4.5. Participatory financing -- 8.5. The business model and key elements -- 8.5.1. Practicing networks -- 8.5.2. Positive and negative impacts of network applications -- 8.5.3. The problem of producer-consumers and consumer-producers -- 8.5.4. Underlying mechanisms: some differences with the usual economic systems -- 8.5.5. A form of social hypocrisy? -- 8.5.6. Generalization: the management rules for P2P -- 8.6. The problem of property and resource allocation -- 8.6.1. The growing role of platforms -- 8.6.2. The prisoner's dilemma -- 8.6.3. Games theory: an introduction -- 8.6.4. Nonlinear models in game theory -- 8.7. The uberization approach in context -- 8.7.1. Simplexification -- 8.7.2. Increasing complexity: the influence of cognitive approaches -- 8.8. Generalization: the complexity of allocation problems -- 8.9. Conclusion -- 9. Computer-assisted Production Management -- 9.1. Introduction and reminders -- 9.2. Intercommunication networks -- 9.2.1. Notions of complexity in networks [LET 11] -- 9.2.2. A few concepts of parallelism. 9.2.3. Elements of parallelism and associated architectures -- 9.2.4. Transposition into industrial or social applications -- 9.3. Commmunicatioon network topologies -- 9.3.1. Some characteristics of different network topologies -- 9.3.2. Construction of a hypercube -- 9.3.3. Notions of symmetry: cutting a hypercube -- 9.3.4. The shortest path between two processors -- 9.4. A few important properties -- 9.5. Analysis of new concepts and methods in manufacturing sciences: instabilities, responsiveness and flexibility -- 9.5.1. General approach: planning and scheduling -- 9.5.2. Illustration in management systems -- 9.5.3. Problems and remarks [MAS 14] -- 9.5.4. Improvements in planning and scheduling -- 9.5.5. Improvements in configuration/reconfiguration [MAS 99a, MAS 99b] -- 9.5.6. Global improvements through simulation -- 9.5.7. Inverse modeling and simulation [MAS 06] -- 9.6. New concepts for managing complex systems -- 9.6. New concepts for managing complex systems -- 9.6.1. Traditional approach -- 9.6.2. Recent improvements in the management of systems [MAS 14] -- 9.7. The change of conduct -- 9.8. Improvements in manufacturing: process balancing -- 9.9. Conclusion: main action principles in complex environments -- 10. Complexity and Cognitive Robotics -- 10.1. Introduction -- 10.2. The new industrial revolution -- 10.3. The factory of the future: trend or revolution? -- 10.4. Inputs for the factory of the future and their impact on the industry's professions -- 10.5. Conditions for success -- 10.6. The data sciences -- 10.6.1. Introduction to the characteristics of "Big Data" -- 10.6.2. The problem of Big Data -- 10.6.3. A new profession: the data scientist -- 10.6.4. Some ask, how will this be possible? -- 10.6.5. The field of large numbers -- 10.7. A few technologies in data sciences. 10.7.1. The steps of reasoning based on the experience of the inductive approach and on the verification of hypotheses.
9781119368731
System theory.
Electronic books.
Q295.M377 2017
003