Advances in artificial transportation systems and simulation
The Intelligent Systems Series encompasses theoretical studies, design methods, and real-world implementations and applications. It publishes titles in three core sub-topic areas: Intelligent Automation, Intelligent Transportation Systems, and Intelligent Computing. Titles focus on professional and...
| Otros Autores: | , , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
San Diego, California :
Academic Press
2015.
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| Edición: | 1st edition |
| Colección: | Intelligent systems series.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629204406719 |
Tabla de Contenidos:
- Cover; Title Page; Copyright Page; Table of Contents; List of contributors; Preface; Chapter 1 - ITSUMO: An Agent-Based Simulator for Intelligent Transportation Systems; 1.1 - Introduction and Motivation; 1.2 - Description of the Simulator; 1.2.1 - Microscopic Simulation Model and Simulation Kernel; 1.2.2 - Database Module; 1.2.3 - Output Module: Statistics and Visualization; 1.3 - Control: Traffic Light Agent Module; 1.3.1 - Greedy Traffic Light Agent; 1.3.2 - Reinforcement Learning-Based Methods; 1.3.3 - Swarm-Intelligence Inspired Signal Plan Choice; 1.4 - Demand
- 1.4.1 - Routing of the Demand1.4.2 - Deadlock Handling; 1.4.3 - Driver Definition; 1.4.4 - Drivers and En-Route Replanning; 1.5 - Case-Study: Aggregating Intelligence to Traffic Simulation; 1.6 - Conclusion; Acknowledgment; References; Chapter 2 - A Pattern-Based Framework for Building Self-Organizing Multi-Agent Systems; 2.1 - Introduction; 2.2 - JASOF; 2.2.1 - Main Idea; 2.2.2 - JASOF Structure; 2.2.2.1 - Environment; 2.2.2.2 - Agent location; 2.2.2.3 - Diffusion pattern; 2.2.2.4 - Evaporation pattern; 2.2.2.5 - Aggregation pattern; 2.2.2.6 - Replication pattern; 2.2.3 - JASOF Hotspots
- 2.3 - Case Study: A Self-Organized Automatic Guided Vehicle2.3.1 - Main Idea; 2.3.2 - Destination Agent; 2.3.3 - Warehouse Agent; 2.3.4 - Transporter Agent; 2.3.5 - Location Agent; 2.3.6 - Execution; 2.3.7 - System Composition; 2.4 - Related Work; 2.5 - Conclusions and Future Work; Acknowledgment; References; Chapter 3 - An Agent Methodology for Processes, the Environment, and Services; 3.1 - Introduction; 3.2 - Background; 3.3 - Analysis and Design for MAS; 3.3.1 - Scenario Description and Early Requirements Analysis; 3.3.2 - Analysis Phase; 3.3.3 - Architectural Design
- 3.3.4 - Detailed Design3.4 - Discussion and Future Work; Acknowledgment; References; Chapter 4 - A Role-Based Method for Analyzing Supply Chain Models; 4.1 - Introduction; 4.2 - Related Work; 4.3 - A Framework of Supply Chain Roles, Responsibilities and Interactions; 4.3.1 Roles; 4.3.2 - Responsibilities; 4.3.3 - Interaction; 4.3.4 - An Illustrating Example; 4.4 - A Method for Analyzing Supply Chain Simulation Models; 4.5 - Applicability and Validity of the Framework and Analysis Method; 4.5.1 - Analysis of the TAPAS Simulation Model
- 4.5.2 - Analysis of a Supply Chain Model by Strader et al. (1998)4.5.3 - Analysis of a Supply Chain Model by Gjerdrum et al. (2001); 4.5.4 - Discrete Event Simulation of a Food Supply Chain; 4.5.5 - Dynamic Simulation of a Short Life Cycle Product Supply Chain; 4.6 - Concluding Remarks and Future Work; References; Chapter 5 - Applying Delegate MAS Patterns in Designing Solutions for Dynamic Pickup and Delivery Problems; 5.1 - Introduction; 5.2 - Related Work; 5.2.1 - Combinatorial Optimization-Based Approaches for Solving PDP; 5.2.2 - MAS-Based Approaches for Solving PDP
- 5.2.3 - Patterns for MAS
