
Modeling for Decision Support in Network-Based Services
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Content
- Title Page
- Preface
- Table of Contents
- Introduction
- Modeling for Decision Support in Network-Based Services
- Introduction
- Conceptual Framework of SSME
- Network Science
- Computational and Analytical Modeling
- Knowledge Science and Engineering
- Decision Support
- Overview of the Articles
- Network Science and SSME
- Computational and Analytical Modeling in SSME
- Knowledge Science and SSME
- Summary
- References
- Section I - Network Science
- Supporting and Optimizing Interactive Decision Processes in Grid Environments with a Model-Driven Approach
- Introduction
- Problem Statement
- Use Case: Metal Casting
- Use Case: Collision Analysis in Metal Forming
- Process Support
- Solution Architecture
- Grid Computing Value Chain
- Supporting Human Tasks in Grid Workflows
- Model-Driven Workflow Optimization
- Prototype Implementation
- Implementation of Human Task Support
- Implementation of the Workflow Optimization Facility
- Sample Application
- Processing Human Tasks
- Workflow Optimization
- Related Work
- Conclusions
- References
- Optimization Approaches in the Strategic and Tactical Planning of Networks for Letter, Parcel and Freight Mail
- Introduction
- Distribution Networks in Postal Logistics
- The Decomposition Approach into Planning Phases and Subnetworks
- Network Design in Freight Transportation
- Services, Time-Windows and Cut-Off-Times
- The Optimization Problems for Distribution Network Design in Postal Logistics
- Selected Case Studies
- The Deutsche Post Night-Airmail Network for Letter Mail (NAN)
- The Delivery Station Location Optimization Problem
- DHL Freight's Transportation Problem of Swap Body Containers
- Conclusions
- References
- Adaptation of Optimization Models in Volatile Transport Scenarios
- Introduction
- Autonomous Adaptation of Interacting Agents
- Vehicle Routing in a Volatile Environment
- Previous and Related Work
- Informal Challenge Description
- Decision Model
- Test cases
- Online Planning Approach
- Memetic Algorithm Schedule Generation
- Constraint Handling Techniques
- Basic Re-scheduling Algorithm
- Experimental Setup
- Numerical Assessment of PEN and HARD
- Image Modification Approach
- Static, Dynamic and Adaptive Modeling Rules
- Algorithmic Model Adaptation
- Adaptive Sharpening of the Constraint Set
- Situation-Based Adaptation of the Objective Function
- Computational Experiments
- Layout of the Experimental Field
- Presentation and Interpretation of Numerical Results
- Conclusions
- References
- Section II - Computational and Analytical Modeling
- A Method for Selectively Designing Modeling Languages for Product-Service Systems
- Customer Solutions and Product-Service Systems
- Properties of Customer Solutions
- Value Creation in Product-Service Systems
- Requirements towards Modeling Languages for PSS
- Conceptual Modeling and Modeling Languages
- Department-Specific Perspectives of Customer Solutions and PSS
- A Method for Selectively Designing Modeling Language Support for PSS
- Outline of the Method
- Demonstration of the Method in the Area of PSS
- Conclusion and Outlook
- References
- SC-CoJava: A Service Composition Language to Unify Simulation and Optimization of Supply Chains
- Introduction
- Motivation
- The Challenge: Simulation vs. Optimization
- Contributions
- Service Composition Framework and Its Simulation Semantics
- Conceptual SC Framework
- Java Representation and Simulation-Like Semantics
- Emergency Response Service Example
- SC-CoJava Syntax and Optimization Semantics
- SC-CoJava Syntax
- Restrictions on the Non-deterministic (Nd) Class Methods
- SC-CoJava Optimization Semantics
- Emergency Response Example as Optimization
- Implementation Notes
- Constraint Compiler
- SC-CoJava Symbolic Expression Data Type
- SC-CoJava Flow Control
- SC-CoJava Constraint Reduction
- Related Work
- Conclusions and Future Work
- References
- An Ontology-Based Model Management Architecture for Service Innovation
- Introduction
- Services and Services Innovation
- The Concept of Services
- Service Innovation
- Role of Decision Models in Service Innovation
- Motivating Scenarios
- Scenario 1: Intra-organizational Model Sharing
- Scenario 2: Models as Knowledge Objects
- Scenario 3: Model Management Supporting Service Design and Agile Business Processes
- Related Work on Model Management
- An Architecture for Distributed Model Management
- Design Considerations
- Models as Services
- Distributed Model Management Architecture
- Semantic Descriptions of Models
- Implementation and Current Status
- Case Studies
- Case study 1: Model Sharing in an Inter- or Intra organizational Setting
- Case study 2: Model Composition in an Inter-organizational Setting
- References
- Virtual Environments for Computational and Analytical Modeling: A Telemedicine Application
- Introduction
- Evolution of Virtuality
- Modeling and Simulation in the Scientific Method
- The Virtuality - Reality Continuum
- Modeling of Service Systems: Negotiated Reality and SSME
- Virtual Environments (VE) for Computational and Analytical Modeling (CAM): An Integrative Approach
- The VECAM Platform
- Carrier Systems to Support Virtual Environments
- Analytical Modeling Languages for VECAM
- Modeling Telemedicine Using VECAM
- Summary
- References
- Section III - Knowledge Science
- On Deriving Indicators from Texts
- A First Look
- The Validity Question
- The Aptness Question
- Further Methods
- Categorization of Indicators
- Calculating More Nuanced Indicators
- Ontologies
- Information Mashing
- Information Extraction
- Association Distributions
- Categorized Document Bases with Normalized Hits
- A Governing Framework and Related Work
- Categorization/Classification
- Measurement
- Association
- Discussion
- References
- An Ontological Framework for Model-Based Problem-Solving
- Introduction
- Modelling Problems
- Approach
- Framework Outline
- Use Cases
- Functional Architecture of the Framework
- Layered Ontological Knowledge Bases
- Toolbox to Support Model-Based Problem-Solving
- Knowledge Bases
- Problem, Model and Modelling Knowledge Base
- Meta-ontology
- Modelling-KB
- Problem-KB
- Model-KB
- Connections between the Knowledge Bases
- Modelling Support Tool (MoST)
- Introduction to MoST
- Setting-Up Modelling Projects
- Guiding Modelling Projects
- Monitoring Modelling Projects
- Reporting on Modelling Projects
- Training Material and Help
- Testing
- Reuse and Extension of the Framework
- Discussion
- Introduction
- Scientific Innovation
- QA for Modelling
- Multidisciplinary Collaboration
- Public Participation
- Present Status and Future Plans
- Modelling Problems Solved
- Concluding Remarks
- References
- Knowledge Pentagram System and Applications
- Introduction
- Knowledge Pentagram System
- Creative Space
- Pentagram System, Shinayakana Systems Approach and Critical Systems Thinking
- Road-Mapping
- Intervention
- Intelligence
- Involvement
- Imagination
- Integration
- Sociological Interpretation
- Structure-Agency-Action Paradigm
- An Intercultural View of Knowledge Construction
- Application to Fresh Food Management
- Use of the i-System
- Managerial Knowledge
- Consumer Purchasing Behavior
- A Fresh Food Management System
- Summary and Future Outlook
- References
- Ontology Construction and Its Applications in Local Research Communities
- Introduction
- The Goals and Ways of Constructing Ontology of the COE Program
- Bottom-Up Classification and Specification: Keyword Analysis
- Keyphrases Extractor
- Keyphrases Clustering
- Top-Down Reflection on the Concept of Knowledge Science
- Final Proposal of the Ontology
- Other Perspectives and Ontological Approaches
- Other Approaches used in JAIST
- A Review of Ontological Engineering Tools
- Ontology Construction for National Institute of Telecommunications
- Bottom-Up Ontology Formation Experiments
- Top-Down Ontology Formation and Reflection
- An Application: Adaptive Hermeneutic Agent (AHA) and Network Services
- Conclusions and Future Research
- References
- Author Index
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