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|a Freitag, Michael.
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|a Dynamics in Logistics :
|b Twenty-Five Years of Interdisciplinary Logistics Research in Bremen, Germany.
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|a 1st ed.
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|a Cham :
|b Springer International Publishing AG,
|c 2021.
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|c Ã2021.
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|a 1 online resource (322 pages)
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|a text
|b txt
|2 rdacontent
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|a online resource
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|a 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.
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|a 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.
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|a 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.
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|a 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.
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|a 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.
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|a 3.1 Necessity to Adapt to ULCV Requirements.
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|a Description based on publisher supplied metadata and other sources.
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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|a Electronic books.
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| 700 |
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|a Kotzab, Herbert.
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| 700 |
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|a Megow, Nicole.
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|i Print version:
|a Freitag, Michael
|t Dynamics in Logistics
|d Cham : Springer International Publishing AG,c2021
|z 9783030886615
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| 797 |
2 |
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|a ProQuest (Firm)
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| 856 |
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|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=6827720
|z Click to View
|
