Detalles MARC
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13612nam a2200337 a 4500 |
| 003 - Identificador del Número de control |
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AR-sfUTN |
| 008 - Códigos de información de longitud fija-Información general |
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170717b ||||| |||| 00| 0 d |
| 020 ## - ISBN |
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| ISBN |
9781848210905 |
| 040 ## - Fuente de la catalogación |
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| Centro transcriptor |
AR-sfUTN |
| 041 ## - Código de lengua |
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| Código de lengua del texto |
eng |
| 080 ## - CDU |
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| Clasificación Decimal Universal |
658.7 T347 |
| Edición de la CDU |
2000 |
| 100 1# - Punto de acceso principal-Nombre de persona |
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| Nombre personal |
Thierry, Caroline |
| Término indicativo de función |
ed. |
| 245 10 - Mención de título |
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| Título |
Simulation for supply chain management / |
| Mención de responsabilidad |
edited by Caroline Thierry, André Thomas, Gérard Bel. |
| 260 ## - Publicación, distribución, etc. (pie de imprenta) |
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| Lugar de publicación, distribución, etc. |
London: |
| Nombre del editor, distribuidor, etc. |
John Wiley and Sons, |
| Fecha de publicación, distribución, etc. |
2008 |
| 300 ## - Descripción física |
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| Extensión |
346 p. |
| 336 ## - Tipo de contenido |
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rdacontent |
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texto |
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txt |
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rdamedia |
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sin mediación |
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n |
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rdacarrier |
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volumen |
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nc |
| 490 ## - Mención de serie |
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| Mención de serie |
Control Systems, Robotics and Manufacturing series |
| 505 80 - Nota de contenido con formato |
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| Nota de contenido con formato |
CONTENIDO<br/>Chapter 1. Supply Chain Management Simulation: An Overview 1<br/>THIERRY , Caroline; BEL, Gérard and THOMAS, André<br/>1.1. Supply chain management 1<br/>1.1.1. Supply chain viewpoints 1<br/>1.1.2. Supply chain management 2<br/>1.1.2.1. Supply chain processes: the integrated supply chain point of view 2<br/>1.1.2.2. Dynamic behavior of supply chain management system 4<br/>1.1.2.3. Supply chain processes: the collaborative supply chain point of view 5<br/>1.2. Supply chain management simulation 5<br/>1.2.1. Why use simulation for SCM? 5<br/>1.2.2. How can we use SCM simulation? 7<br/>1.3. Supply chain management simulation types 8<br/>1.3.1. Production management models focus 8<br/>1.3.1.1. Time bucket models 9<br/>1.3.1.2. Starting time models 10<br/>1.3.2. Simulation types 11<br/>1.3.2.1. Size of the system 11<br/>1.3.2.2. Complexity of the production management system 11<br/>1.3.2.3. Different types of models for SCM simulation 11<br/>1.3.3. SCM simulation using continuous simulation approach 12<br/>1.3.3.1. System dynamics 12<br/>1.3.3.2. Production management models/simulation models 13<br/>1.3.4. SCM simulation using discrete-event approach 13<br/>1.3.4.1. Time bucket-driven approach 15<br/>1.3.4.2. Event-driven approach 18<br/>1.3.5. Simulation of supply chain management using games 19<br/>1.3.5.1. Games and simulation 19<br/>1.3.5.2. Production management models/simulation models 20<br/>1.4. Decision systems and simulation models (systems) 20<br/>1.4.1. Models and system distribution 20<br/>1.4.2. Centralized simulation 24<br/>1.4.3. Multi-agent system decision simulation 25<br/>1.4.4. Simulation for product-driven systems 26<br/>1.4.5. Model synchronization 27<br/>1.5. Simulation software 29<br/>1.6. Simulation methodology 29<br/>1.6.1. Evaluation of simulation models 29<br/>1.6.2. Reduction of simulation models 30<br/>1.6.2.1. Reducing model literature review 30<br/>1.6.2.2. The reducing model problem 31<br/>1.6.2.3. Another state reduction using the bottleneck notion 32<br/>Chapter 2. Continuous Simulation for SCM 37<br/>THIEL, Daniel and HOA, Vo Thi Le<br/>2.1. System dynamics models for SCM 37<br/>2.1.1. Complexity in supply chain logistics 37<br/>2.1.2. Cybernetics and feedback concept 38<br/>2.1.3. Basic principles of system dynamics 39<br/>2.1.3.1. Forrester's theory 39<br/>2.1.3.2. Simulation techniques 42<br/>2.1.4. How can we represent the supply chain decision system? 44<br/>2.1.5. Literature review 46<br/>2.2. Application: recent research into the bullwhip effect 48<br/>2.2.1. Bullwhip effect in supply chains 48<br/>2.2.1.1. Bullwhip effect definition 48<br/>2.2.1.2. Supply chain perturbations 48<br/>2.2.1.3. Bullwhip effect causes 48<br/>2.2.1.4. Bullwhip effect reduction solutions 49<br/>2.2.2. Bullwhip effect modeling 50<br/>2.2.2.1. Example of a BE diffusion model 50<br/>2.2.2.2. BE system dynamics models 52<br/>2.2.2.3. BE multi-agent models 57<br/>Chapter 3. Discrete-event Simulation for Supply Chain Management 69<br/>BOTTA-GENOULAZ, Valérie; LAMOTHE, Jacques; PICARD, Florence; RIANE, Fouad and VALLA, Anthony<br/>3.1. Discrete-event simulation and supply chain 69<br/>3.1.1. Introduction 69<br/>3.1.2. Event-driven and time bucket-driven simulation for supply chains 70<br/>3.2. Discrete-event simulation for supply chain redesign 73<br/>3.2.1. Problem definition 73<br/>3.2.2. Problem statement 74<br/>3.2.3. Decision aid approach 75<br/>3.2.4. Models of the decision aid approach 76<br/>3.2.5. Discrete-event simulation model 77<br/>3.2.5.1. Resources 77<br/>3.2.5.2. Simulated processes 77<br/>3.2.5.3. Simulation and decision-making 79<br/>3.2.5.4. Performance indicators 80<br/>3.2.6. Illustrative application 80<br/>3.3. Discrete-event simulation for cooperation process risk analysis 83<br/>3.3.1. Context of the study 83<br/>3.3.2. The simulator's principles 84<br/>3.3.3. Example of application 87<br/>3.4. Discrete-event simulation for business process reengineering 91<br/>3.4.1. Methodology 91<br/>3.4.1.1. Modeling 92<br/>3.4.1.2. Simulation and diagnosis 93<br/>3.4.1.3. Evaluation of different solutions 93<br/>3.4.2. Application 94<br/>3.4.2.1. Description of the business process 94<br/>3.4.2.2. Application of the modeling stage 95<br/>3.4.2.3. Application of the simulation and diagnosis stage 95<br/>3.4.2.4. Evaluation of different solutions in the third stage 97<br/>3.4.3. Discussion 98<br/>Chapter 4. Simulation Games 103<br/>MOYAUX, Thierry; BALLOT, Éric; GREIF, Michel and SIMON, Bertrand<br/>4.1. Introduction 103<br/>4.2. Literature review 104<br/>4.2.1. Board games 106<br/>4.2.1.1. The Beer Game 106<br/>4.2.1.2. Derivatives of the Beer Game 108<br/>4.2.1.3. The Trust and Tracing Game 111<br/>4.2.1.4. The Mortgage Service Game 111<br/>4.2.1.5. Legostics Management 111<br/>4.2.1.6. Risk pooling 112<br/>4.2.2. Sophisticated games 112<br/>4.2.2.1. Trading Agent Competition - Supply Chain Management 112<br/>4.2.2.2. Chain Game for distributed trading and negotiation 113<br/>4.2.2.3. Business Network Lab 114<br/>4.2.2.4. SIMBU 114<br/>4.3. Theories about the usage of games 114<br/>4.3.1. Games as a booster for learners? 115<br/>4.3.1.1. Backgrounds 115<br/>4.3.1.2. Evaluation 116<br/>4.3.2. Games as a research field for managerial behavior 117<br/>4.3.2.1. The role of the "human factor" in replenishment or inventory decisions 117<br/>4.3.2.2. Why choose games to conduct supply chain research? 117<br/>4.3.2.3. Testing hypothesis on manager behavior 117<br/>4.4. Examples of implementation methodologies and obtained results 119<br/>4.4.1. Kanban game in academic institutions 121<br/>4.4.2. A field for experimentation 123<br/>Chapter 5. Centralized Approaches for Supply Chain Simulation: Issues and Applications 129<br/>BENYOUCEF, Lyes; JAIN, Vipul and CHARPENTIER, Patrick<br/>5.1. Introduction 129<br/>5.2. Supply chain centralized simulation - a literature review 130<br/>5.3. Supply chain simulation using centralized approaches 134<br/>5.4. Some industrial and practical applications 134<br/>5.4.1. Production - distribution network design in automotive industry 134<br/>5.4.1.1. Network description 135<br/>5.4.1.2. Make-to-Stock and Make-to-Order strategies 136<br/>5.4.1.3. The simulation model 136<br/>5.4.1.4. Optimization variables 138<br/>5.4.1.5. Optimization specifications 139<br/>5.4.1.6. Experimental results and analyses 139<br/>5.4.2. Supplier selection problem in textile industry 141<br/>5.4.2.1. Supply chain description 141<br/>5.4.2.2. The simulation-optimization model 143<br/>5.4.2.3. Genetic representation and operations 143<br/>5.4.2.4. Discrete-event simulation model 144<br/>5.4.2.5. Experimental results and analyses 145<br/>5.4.3. Another practical example from the automotive industry 147<br/>5.4.3.1. Supply chain description 147<br/>5.4.3.2. From the generic model of a supply flow to its simulation 149<br/>5.4.3.3. Illustrative example 152<br/>Chapter 6. The Interest of Agents for Supply Chain Simulation 159<br/>MONTEIRO, Thibaud; ANCIAUX, Didier; ESPINASSE, Bernard; FERRARINI, Alain; LABARTHE, Olivier and ROY, Daniel<br/>6.1. Decision problems in enterprise networks 159<br/>6.2. State of the art: modeling and simulation of supply chains with agents 161<br/>6.2.1. Introduction to the agent and MAS 161<br/>6.2.1.1. Agent definition and typology 162<br/>6.2.1.2. MAS 164<br/>6.2.2. Supply chain simulation with agents 168<br/>6.2.2.1. Interests of the agent approach 168<br/>6.2.2.2. Review of works on agent-based supply chain modeling and simulation 171<br/>6.3. Conclusion and summary of the projects 181<br/>Chapter 7. Agent-based Simulation of Business Network Planning and Coordination Systems 189<br/>MONTEIRO, Thibaud; ANCIAUX, Didier; D'AMOURS, Sophie; ESPINASSE, Bernard; FERRARINI, Alain; LABARTHE, Olivier and ROY, Daniel<br/>7.1. Decision system in a supply chain 189<br/>7.2. Decision-making tools to supply chain control 190<br/>7.2.1. Distributed planning in supply chain 191<br/>7.2.1.1. Multi-agent architecture 191<br/>7.2.1.2. Planning the supply chain 192<br/>7.2.2. Confirmed order management in a stochastic environment 197<br/>7.2.2.1. Decision problem 197<br/>7.2.2.2. Decision process for new order integration 198<br/>7.2.3. Experimental agent-based platform for tactical planning in the softwood lumber industry 200<br/>7.3. Simulation tools to design supply chain planning and coordination systems 203<br/>7.3.1. Order management evaluation 203<br/>7.3.2. Performance evaluation of various coordination policies according to the location of the decoupling point 206<br/>7.3.3. Design of cooperation mechanism 212<br/>7.3.3.1. Example of simulation for multi-negotiation parameter 213<br/>7.3.4. SPEE 215<br/>Chapter 8. Simulation for Product-driven Systems 221<br/>THOMAS, André; CASTAGNA, Pierre; PANNEQUIN, Rémi; KLEIN, Thomas; EL HAOUZI, Hind; BLANC, Pascal and CARDIN, Olivier<br/>8.1. Introduction 221<br/>8.2. Control architectures of manufacturing systems 222<br/>8.2.1. Hierarchical control architectures 222<br/>8.2.2. Heterarchical control architectures 223<br/>8.2.3. Product-driven architectures 224<br/>8.3. Validation with simulation in HMS or product-driven systems 227<br/>8.3.1. Concept of emulation 228<br/>8.3.2. Simulation modeling with emulator and control system 229<br/>8.3.2.1. Emulation model 229<br/>8.3.2.2. Control model 230<br/>8.4. Simulation: a computer-aided tool for product-driven systems 232<br/>8.5. Industrial applications 234<br/>8.5.1. Furniture company case study 234<br/>8.5.1.1. Context 234<br/>8.5.1.2. Proposed architecture 236<br/>8.5.2. Multi-line synchronization 239<br/>8.5.2.1. Industrial context 239<br/>8.5.2.2. System architecture at Trane 240<br/>8.5.2.3. Limits and perspectives 244<br/>8.5.3. AGP case study 245<br/>8.5.3.1. Context 245<br/>8.5.3.2. Proposed architecture 246<br/>8.5.3.3. Evaluation of the control by simulation 251<br/>Chapter 9. HLA Distributed Simulation Approaches for Supply Chains 257<br/>OUNNAR, Fouzia; ARCHIMEDE, Bernard; CHARBONNAUD, Philippe and PUJO, Patrick<br/>9.1. Introduction 57<br/>9.2. Modeling and discrete-event simulation 259<br/>9.2.1. Specification using DEVS and SIMBA 259<br/>9.2.2. Model interoperability 260<br/>9.2.3. Model interaction protocols 261<br/>9.3. Self-organized control of supply chain networks 264<br/>9.3.1. Problematics 264<br/>9.3.2. Choice of a decision structure 265<br/>9.3.3. Holonic approach for self-organized control of logistic network 266<br/>9.3.4. DEVS-EPA modeling and distributed simulation in HLA environment 269<br/>9.3.5. Ranking and evaluation of the supplier process 272<br/>9.3.6. Analysis of the simulation results: manufacturing of cosmetic products by an enterprise network 274<br/>9.4. Reactive control by evaluation of multi-site plans 276<br/>9.4.1. Problem statement 276<br/>9.4.2. Development method and tools of multi-site plans 277<br/>9.4.3. Conceptual multi-agent SCEP model 278<br/>9.4.4. Principle of deployment in the SCEP network 281<br/>9.4.5. Development process of multi-site plans 283<br/>9.4.6. Evaluation method and tools of multi-site plans 283<br/>9.4.7. Evaluation by distributed simulation, interest and limits 288<br/>Chapter 10. Software Tools for Simulation 295<br/>FONTANILI, Franck; CASTAGNA, Pierre and YANNOU, Bernard<br/>10.1. Short history of the tools for simulation in industrial engineering 295<br/>10.2. Typology of the simulation tools for the supply chain 296<br/>10.2.1. General classification 297<br/>10.2.2. Classification according to the versatility and the facility of use 298<br/>10.2.3. Classification of discrete-event simulation according to the life-cycle of the process 299<br/>10.2.4. Specific classification for SCM 301<br/>10.2.5. The system dynamics software 301<br/>10.3. Key points of the construction of a simulation model 304<br/>10.3.1. Stage of modeling the actions of a process 304<br/>10.3.2. Stage of describing the laws and rules 305<br/>10.3.3. Logic elements 305<br/>10.3.4. Horizon of simulation 306<br/>10.4. Limits and objectives of simulation tools 307<br/>10.4.1. What they can do 307<br/>10.4.2. What they cannot do 307<br/>10.5. Methodology of a simulation project 308<br/>10.5.1. Step 1: problem analysis 309<br/>10.5.2. Step 2: modeling and programming 310<br/>10.5.3. Step 3: simulations 313<br/>10.5.4. Step 4: report/ratio and conclusions 313<br/>10.6. Possibilities of coupling 314<br/>10.6.1. Input/output data analysis 314<br/>10.6.2. Inputs/outputs via spreadsheet or database 315<br/>10.6.3. Control simulator from an external client 316<br/>10.6.4. Coupling with the real process (online simulation) 317<br/>10.7. Main functionalities and criteria of selection of a tool 318<br/>10.8. Classification of the commercial tools 319<br/>10.8.1. Offer highlights 319<br/>10.8.2. General presentation of three software tools 320<br/>10.8.2.1. Arena 320<br/>10.8.2.2. Witness 320<br/>10.8.2.3. Quest 321<br/>10.9. Example of modeling with three tools 321<br/>10.9.1. Description of the process and knowledge model 321<br/>10.9.2. Modeling with Arena 322<br/>10.9.3. Modeling and simulation with Witness 326<br/>10.9.4. Modeling with Quest 331<br/>10.9.5. Example of modeling of a total logistic chain 335<br/>10.10. Useful links 335<br/>List of Authors 339<br/>Index 345<br/> |
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LOGISTICA EMPRESARIAL |
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| Término de materia |
BUSINESS LOGISTICS |
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| Término de materia |
PRODUCTION MANAGEMENT |
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| Término de materia |
COMPUTER SIMULATION |
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| Término de materia |
PRODUCCION INDUSTRIAL |
| 650 ## - Punto de acceso adicional de materia - Término de materia |
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| Término de materia |
ORGANIZACION DE LA PRODUCCION |
| 700 1# - Punto de acceso adicional - Nombre de persona |
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| Nombre personal |
Thomas, André |
| Término indicativo de función |
ed. |
| 700 1# - Punto de acceso adicional - Nombre de persona |
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| Nombre personal |
Bel, Gérard |
| Término indicativo de función |
ed. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Tipo de ítem Koha |
Libro |
| Esquema de clasificación |
Clasificación Decinal Universal |
