Dynamics in Logistics : : Twenty-Five Years of Interdisciplinary Logistics Research in Bremen, Germany.

• Author: Freitag, Michael.
• TeilnehmendeR: Kotzab, Herbert.; Megow, Nicole.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2021.; Ã2021.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (322 pages)

Table of Contents

• Intro
• Preface
• History
• This Book
• Contents
• Part I Models and Methods for Planning in Logistics
• Autonomous Control of Logistic Processes: A Retrospective
• 1 Introduction
• 2 The Concept of Autonomous Control
• 3 Reference Scenarios for Autonomous Logistic Processes
• 3.1 Description of the Reference Scenarios
• 3.1.1 Shop-Floor Scenario
• 3.1.2 Transportation Scenario
• 3.2 Modeling Approaches
• 3.3 Evaluation of Autonomously Controlled Systems
• 4 Methods for Autonomous Control
• 4.1 Engineering Methodology for Autonomous Logistic Processes
• 4.2 Autonomous Control Methods
• 4.2.1 Production Scheduling
• 4.2.2 Transportation Scheduling
• 4.3 Coupling of Conventional Planning and Autonomous Control
• 5 Evaluation of Autonomous Control
• 5.1 Performance of Autonomous Control and Influence of Complexity
• 5.2 Autonomous Control and Conventional Planning and Control
• 6 Application of Autonomous Control
• 6.1 Finished Vehicle Logistics
• 6.2 Apparel Logistics
• 6.3 Event Logistics
• 6.4 Material Supply for Production
• 7 Conclusion and Outlook
• 7.1 Conclusion
• 7.2 Outlook
• References
• Explorable Uncertainty Meets Decision-Making in Logistics
• 1 Uncertainty in Logistics
• 2 Power of Exploring Uncertain Data in Logistics
• 3 Optimization Under Explorable Uncertainty
• 3.1 The Model
• 3.1.1 Example: Minimum and Selection Problems
• 3.1.2 Example: Minimum Spanning Tree Problem
• 3.2 Mandatory Queries
• 3.2.1 Identifying Mandatory Queries for the Minimum Problem
• 3.2.2 Identifying Mandatory Queries for the Minimum Spanning Tree Problem
• 3.3 Methods and Results
• 3.3.1 Witness Set Algorithm for the Minimum Problem
• 3.3.2 Witness Set Algorithm for the Minimum Spanning Tree Problem
• 4 Explorable Uncertainty Beyond Worst-Case Analysis
• 4.1 Exploiting Untrusted Predictions.
• 4.1.1 Error Measures and Learnability
• 4.1.2 Methods and Results
• 4.2 Exploiting Stochastic Information
• 5 Concluding Remarks
• 6 Bibliographical Notes
• References
• Complex Networks in Manufacturing and Logistics: A Retrospect
• 1 Introduction
• 2 Complex Networks in Manufacturing and Logistics
• 2.1 Modeling of Complex Networks
• 2.2 The Structure of Manufacturing Networks and Its Impact on Material Flow
• 2.2.1 Comparison of Manufacturing Networks to Other Flow-Oriented Networks
• 2.2.2 The Relation Between Structure and Performance
• 2.3 Dynamic Processes on Material Flow Networks
• 3 Advanced Network Modeling: Stochastic Block Models
• 4 Identification of Autonomous Clusters Considering the Topological Setting
• 5 Synthetic Material Flow Networks with a Built-In Cluster Structure: A Random Walk-Based Approach
• 6 Summary and Outlook
• References
• Recent Developments in Mathematical Traffic Models
• 1 Introduction
• 2 Continuous Flows: Nash Flows Over Time
• 2.1 Continuous Flows Over Time
• 2.1.1 Connectors of the Edges: Vertices
• 2.2 Mathematical Consistency
• 2.3 The Nash Condition
• 2.4 Constructing Nash Flows Over Time
• 2.5 Analysis of Equilibria
• 2.6 Spillback
• 2.7 Further Notes and Remarks
• 3 Discrete Flows: Competitive Packet Routing
• 3.1 The Mathematical Model
• 3.2 Extensions of the Model
• 3.3 Summary
• References
• Part II Digitalization and Logistics
• Intelligent Agents for Social and Learning Logistics Systems
• 1 Introduction
• 2 Foundations of Multiagent Systems in Logistics
• 3 Advanced Concepts for Multiagent Systems in Logistics
• 4 Case Studies
• 4.1 Case Study 1: MAS for Production Logistics
• 4.2 Case Study 2: MAS-Based Autonomous Logistics Processes
• 4.3 Case Study 3: Multiagent Systems and the IoT: The Intelligent Container
• 5 Discussion and Perspectives.
• References
• Semantic Interoperability for Logistics and Beyond
• 1 Introduction
• 2 Background: The Need for Interoperability in Autonomous Cooperating Logistics Systems
• 2.1 An Updated Look at Data Sources in Logistics
• 2.1.1 Logistics IT Systems
• 2.1.2 Digital Counterparts
• 2.1.3 The Internet of Things
• 2.2 Summarizing the Interoperability Problem in Complex Logistics Systems
• 3 The Problem of Data Heterogeneity
• 3.1 Heterogeneity Classification
• 3.2 Interpretation Levels
• 4 Solution Approach: A Semantic Mediator for Complex Logistics Systems
• 4.1 Data Integration Approach
• 4.1.1 Semantic Mediator Core Component
• 4.1.2 Wrapper
• 5 Data Transformation Examples
• 6 Evolution of Semantic Mediator
• 6.1 Application Scenarios as a Historical Outline
• 6.2 Interoperability for Cyber-Physical Systems
• 6.3 Interoperability of Product Usage Information for Product-Service-System Improvement and Design
• 6.4 Sustainable Manufacturing: Extending the Useful Life of Major Capital Investments and Large Industrial Equipment
• 7 Outlook and Conclusion
• References
• Semantic Digital Twins for Retail Logistics
• 1 Digitization of Stationary Retail
• 2 Optimization of Retail Logistics
• 3 Semantic Digital Twin: A Digital Representation of a Retail Store
• 4 Building Blocks of the Semantic Digital Twin
• 4.1 Knowledge Representation
• 4.2 Scene Graph
• 4.3 Symbolic Knowledge Base
• 4.4 Reasoning
• 5 Semantic Digital Twin Use Cases in Retail Logistics
• 5.1 Use Case 1: Replenishment Process
• 5.1.1 AR Supported Replenishment
• 5.1.2 Robotic Replenishment
• 5.2 Use Case 2: Augmented Reality Shopping Assistant
• 5.2.1 HoloLens Application
• 5.2.2 Mobile Phone Application
• 5.3 Use Case 3: Digital Store Visualization and Robot Simulation
• 5.3.1 Semantic Digital Twin Visualization
• 5.3.2 Robot Simulation.
• 6 Conclusion
• 7 Future Directions
• References
• A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning
• 1 Introduction
• 2 Related Works
• 2.1 Ontologies and Linked Data
• 2.2 Machine Learning and Forecasting Methods
• 3 Overview of the Concept
• 4 Ontology Development
• 4.1 Ontology Construction Methodology
• 4.2 Database to Ontology Mapping
• 4.3 Ontology Alignment
• 4.4 Ontology Candidates and Potential Mapping
• 5 Forecast Methods
• 5.1 Dataset Description, Data Preprocessing, and Exploratory Data Analysis
• 5.2 Experiment Results
• 6 Conclusions and Outlook
• References
• The Influence of Cognitive Biases in Production Logistics
• 1 Introduction
• 2 Literature Review
• 2.1 The Human Decision-Making Process and Cognitive Biases
• 2.2 Decisions in Production Logistics
• 3 Conceptual Framework of Distorted Human Decision-Making in Production Logistics
• 3.1 Strategic Decisions
• 3.2 Tactical Decisions
• 3.3 Operational Decisions
• 4 Conclusion
• References
• Part III Fields of Application in Logistics
• Automobile Logistics 4.0: Advances Through Digitalization
• 1 Introduction and Motivation
• 1.1 Research Motivation
• 1.2 Research Contribution
• 2 Finished Vehicle Logistics
• 2.1 Tasks of Distribution Logistics
• 2.2 Planning and Control Within Finished Vehicle Logistics
• 3 Technological Basis for Generating Transparency
• 3.1 Automatic Identification Technologies
• 3.1.1 Barcode
• 3.1.2 Optical Character Recognition
• 3.1.3 Radio-Frequency Identification
• 3.2 Location (Geopositioning) Technologies
• 3.2.1 Global Navigation Satellite Systems
• 3.2.2 Terrestrial Systems
• 3.3 Communication Technologies
• 3.3.1 Wireless Local Area Network
• 3.3.2 Mobile Communications
• 3.4 Technologies and Standards for Data Exchange.
• 4 Developed Assistance and Control Systems
• 4.1 Possible Functionalities and Levels of Automation
• 4.2 Assistance Systems
• 4.2.1 Assistance Systems at Distribution and Handling Points
• 4.2.2 Assistance Systems for Use Across the Distribution Chain
• 4.3 Control Algorithms for Fully Automated Systems
• 4.3.1 Distinguishing Features
• 4.3.2 Control Algorithms at Distribution and Handling Points
• 5 Towards Automobile Logistics 4.0
• 5.1 Vision 1: Fully Transparent Vehicle Distribution Chain
• 5.2 Vision 2: Adaptive Planning and Control of Vehicle Distribution Chains
• 5.3 Retrospective on the Research and Future Prospects
• 6 Summary and Outlook
• References
• 15 Years of Intelligent Container Research
• 1 Introduction
• 1.1 Outline
• 1.2 Project History
• 1.3 The Banana Chain
• 2 Findings
• 2.1 Omnipresence of Temperature Deviations
• 2.2 Necessity of Sub-GHz Communication and Gateway
• 2.3 Shelf- and Green-Life Models
• 2.4 Ethylene Detection
• 2.5 Models for Heat Removal and Generation
• 2.6 Case Study on Cool Chain Logistics
• 2.7 Detection of Fungus Spores
• 2.8 Difficulties in Quality Measurement and Prediction for Green Bananas
• 3 Current Developments and Trends
• 3.1 Communication
• 3.2 Standards
• 3.3 Modelling
• 3.4 Modelling Platforms, Cloud Computing and Digital Twins
• 4 Conclusions and Action Points
• 4.1 Obstacles for FEFO Implementation
• 4.2 Recommended Practical Actions
• 4.3 Research on New Sensor Types
• References
• The Rise of Ultra Large Container Vessels: Implications for Seaport Systems and Environmental Considerations
• 1 Introduction
• 2 The Rise of ULCVs
• 2.1 Towards Gigantism and Segmentation in Container Shipping
• 2.2 Too Big for the Panama Canal
• 2.3 Where Do we Grow from Here: Bigger Vessels Yet to Come?
• 3 Implications for Seaport Systems.
• 3.1 Necessity to Adapt to ULCV Requirements.