Haasis / Kreowski / Scholz-Reiter Dynamics in Logistics

First International Conference, LDIC 2007, Bremen, Germany, August 2007. Proceedings
1. Auflage 2008
ISBN: 978-3-540-76862-3
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 466 Seiten

Logistic problems can rarely be solved satisfyingly within one single scientific discipline. This cross-sectional character is taken into account by the Research Cluster for Dynamics in Logistics with a combination of economical, information and production technical and enterprise-oriented research approaches. In doing so, the interdisciplinary cooperation between university, research institutes and enterprises for the solution of logistic problems is encouraged. This book comprises the edited proceedings of the first International Conference on Dynamics in Logistics LDIC 2007. The scope of the conference was concerned with the identification, analysis, and description of the dynamics of logistic processes and networks. The spectrum reached from the planning and modelling of processes over innovative methods like autonomous control and knowledge management to the new technologies provided by radio frequency identification, mobile communication, and networking. Two invited papers and of 42 contributed papers on various subjects give an state-of-art overview on dynamics in logistics. They include routing in dynamic logistic networks, RFID in logistics and manufacturing networks, supply chain control policies, sustainable collaboration, knowledge management and service models in logistics, container logistics, autonomous control in logistics, and logistic process modelling.

Hans-Dietrich Haasis is full professor for Business Administration, Production Management and Industrial Economics at the University of Bremen and chairman of Business Administration, Production-Management and Industrial Economics, University of Bremen, and director of the ISL - Institute of Shipping Economics and Logistics, Bremen. He held lectures at the Ecole Nationale Supérieure de Pétrole et des Moteurs, Paris Rueil-Malmaison, at the University Eichstätt-Ingolstadt, and at the Private University Witten-Herdecke. He also was invited to give lectures at the St. Petersburg State University of Economics and Finance, and the Technical University of Changcha, China. Hans-Jörg Kreowski is professor for Theoretical Computer Science at the University of Bremen. His main research topics are graph transformation, formal modelling and their applications in computer science and logistics. He (co)-authored and (co)-edited 15 books and published more than 120 scientific papers. Bernd Scholz-Reiter was founder and head of the Fraunhofer Application Center for Logistics Systems Planning and Information Systems at Cottbus. Since November 2000 he is a full professor and chair holder of the chair of Planning and Control of Production Systems (PSPS) at the University of Bremen where he also serves as director of the Bremen Institute of Industrial Technology and Applied Work Science (BIBA). He was initiator and vice-speaker of the research group on Autonomous Control of Logistic Processes, speaker of the Bremen Research Cluster for Dynamics in Logistics as well as speaker of the International Graduate School for Dynamics in Logistics. Scholz-Reiter is an ordinary member of the Berlin-Brandenburg Academy of Sciences and Humanities, an ordinary member of acatech, the Council for Engineering Sciences at the Union of the German Academies of Sciences and Humanities; BESIDE OTHER NATIONAL MEMBERSHIIPS he is a member of CIRP, the International Institution for Production Engineering Research, a fellow of the European Academy of Industrial Management (AIM) and an Advisory Board member of the Schlesinger Laboratory at TECHNION - Israel Institute of Technology, Haifa, Israel, as well as a member of the Scientific Advisory Board of the German Logistics Association (BVL). Professor Scholz-Reiter serves as vice president of the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG). He is editor of the professional journals Industrie-Management and PPS-Management and member of the editorial board of the scientific International Journal Production Planning & Control. He is author and co-author of more than 250 scientific publications..

Autoren/Hrsg.

Kreowski, Hans-Jörg

Scholz-Reiter, Bernd

Haasis, Hans-Dietrich

Inhaltsverzeichnis

1;Preface of the Editors;5
2;Committees;7
3;Contents;8
4;Invited Papers;21
4.1;Challenges in Design of Heterarchical Controls for Dynamic Logistic Systems;22
4.1.1;1 Introduction;22
4.1.2;2 Options for Structuring Controls for Logistic Systems;25
4.1.3;3 Design of Heterarchical Control;27
4.1.4;4 Developing and Evolving Organizations;32
4.1.5;5 Design ofWeb Services;36
4.1.6;6 Conclusions;40
4.2;Making the Business Case for RFID;44
4.2.1;1 Introduction;44
4.2.2;2 Model of RFID Assimilation;45
4.2.3;3 Conclusion;53
5;General Aspects of Dynamics in Logistics;55
5.1;Review of Trends in Production and Logistic Networks and Supply Chain Evaluation;56
5.1.1;1 Introduction;56
5.1.2;2 From Supply Chain to Production Networks;57
5.1.3;3 Performance Assessment of Supply Chains and Networks;62
5.1.4;4 Established Benchmarks for Production Networks;67
5.1.5;5 Conclusions;69
5.2;Dynamic Data Mining for Improved Forecasting in Logistics and Supply Chain Management;73
5.2.1;1 Introduction;73
5.2.2;2 Support Vector Regression;73
5.2.3;3 The Proposed Forecasting Methodology;74
5.2.4;4 Experiments and Results;78
5.2.5;5 Conclusions and Future Works;78
5.3;Introducing Bounded Rationality into Self-Organization-Based Semiconductor Manufacturing;80
5.3.1;1 Introduction;80
5.3.2;2 Introducing Bounded-Rational Agents;81
5.3.3;3 Self-Organization-Based Semiconductor Manufacturing Model;82
5.3.4;4 Simulation Results and Discussion;85
5.3.5;5 Conclusion;88
6;Routing in Dynamic Logistics Networks;89
6.1;Travel Time Estimation and Deadlock-free Routing of an AGV System;90
6.1.1;1 Introduction;90
6.1.2;2 AGV Traffic Control;91
6.1.3;3 Travel Time Estimation Algorithm;93
6.1.4;4 Experimental Results;95
6.1.5;5 Conclusions;97
6.2;Integration of Routing and Resource Allocation in Dynamic Logistic Networks;98
6.2.1;1 Introduction;98
6.2.2;2 Problem Description;99
6.2.3;3 Mathematical Model;100
6.2.4;4 Strategy for a Dynamic Environment;103
6.2.5;5 Conclusion;105
6.3;Dynamic Vehicle Routing with Drivers’Working Hours;107
6.3.1;1 Introduction;107
6.3.2;2 Related Literature;108
6.3.3;3 The General Vehicle Routing Problem;108
6.3.4;4 Drivers’ Working Hours;109
6.3.5;5 Solution Approaches;110
6.3.6;6 Evaluation;111
6.3.7;7 Conclusions;113
7;RFID in Logistics and Manufacturing Networks;115
7.1;A Survey of RFID Awareness and Use in the UK Logistics Industry;116
7.1.1;1 Introduction;116
7.1.2;2 Degree of Awareness of RFID;119
7.1.3;3 RFID Adoption and Diffusion;121
7.1.4;4 Modelling RFID Diffusion;122
7.1.5;5 Barriers to RFID Adoption;122
7.1.6;6 Conclusion;125
7.2;RFID-Based Intelligent Logistics for Distributed Production Networks;127
7.2.1;1 Introduction;127
7.2.2;2 Context-Driven Methodology;128
7.2.3;3 Case Study;130
7.2.4;4 Conclusion;133
7.3;Methodology for Development and Objective Comparison of Architectures for Networked RFID;135
7.3.1;1 Introduction;135
7.3.2;2 Problem Definition;136
7.3.3;3 The Design Methodology;137
7.3.4;4 Demonstrative Example;138
7.3.5;5 Conclusions;142
8;Supply Chain Control Policies;143
8.1;Determining Optimal Control Policies for Supply Networks Under Uncertainty;144
8.1.1;1 Introduction;144
8.1.2;2 Optimal Control by Stochastic Dynamic Programming;145
8.1.3;3 Numerical Example;148
8.1.4;4 Conclusions;149
8.2;Adaptive Production and Inventory Control in Supply Chains against Changing Demand Uncertainty;151
8.2.1;1 Introduction;151
8.2.2;2 The production and inventory control policy;152
8.2.3;3 Variance ratios and objective function;153
8.2.4;4 Methodology;154
8.2.5;5 Adaptive policy;155
8.2.6;6 Summary;158
8.3;A Framework of Adaptive Control for Complex Production and Logistics Networks;159
8.3.1;1 Introduction;160
8.3.2;2 State-of-the-art;160
8.3.3;3 Research methodology: MARINA;161
8.3.4;4 Illustration;163
8.3.5;5 Conclusions;166
8.4;Mechanisms of Instability in Small-Scale Manufacturing Networks;168
8.4.1;1 Introduction;168
8.4.2;2 Model Description;169
8.4.3;3 Classification and Quantification of Instabilities;171
8.4.4;4 Conclusions;174
9;Decentralized Decision-making in Supply Chains;176
9.1;Aspects of Agent Based Planning in the Demand Driven Railcab Scenario;177
9.1.1;1 Introduction;177
9.1.2;2 Problem Description;178
9.1.3;3 Asynchronous Coordination and Synchronous Optimization;179
9.1.4;4 Decentralized Optimization;180
9.1.5;5 Consideration of Uncertain Travel Times;182
9.1.6;6 Conclusion;183
9.2;Merging Time of Random Mobile Agents;185
9.2.1;1 Introduction;185
9.2.2;2 A Genral Markov Chain Formulation;188
9.2.3;3 Hypercubes;192
9.2.4;4 Conclusion and Perspectives;195
9.3;Dynamic Decision Making on Embedded Platforms in Transport Logistics – A Case Study;197
9.3.1;1 Introduction;197
9.3.2;2 Autonomous Decision Making in Transport Logistics;198
9.3.3;3 Implementation in Embedded Systems;199
9.3.4;4 Distributed Solution of Route Planning Problems;200
9.3.5;5 Conclusion;203
10;The Global RF Lab Alliance: Research and Applications;205
10.1;The Value of RF Based Information;206
10.1.1;1 Introduction;206
10.1.2;2 Value of RF based information;210
10.1.3;3 Solution Model – The “Billing Integrated Internet-of-Things”;210
10.1.4;4 Business Scenarios;212
10.1.5;5 Conclusion and future work;214
10.2;Reengineering and Simulation of an RFID Manufacturing System;216
10.2.1;1 Introduction;216
10.2.2;2 RFID Lab at the University of Parma;217
10.2.3;3 Reengineering and simulation of logistics processes;218
10.2.4;4 Development of BIMs and results;222
10.2.5;5 Future research directions and conclusions;224
10.3;LIT Middleware: Design and Implementation of RFID Middleware Based on the EPC Network Architecture;225
10.3.1;1 Introduction;225
10.3.2;2 Overview of EPC Network Architecture;226
10.3.3;3 Features of LIT Middleware;227
10.3.4;4 Design and Implementation of LIT Middleware;230
10.3.5;5 Conclusions;232
10.4;Shelf Life Prediction by Intelligent RFID – Technical Limits of Model Accuracy;234
10.4.1;1 Introduction;234
10.4.2;2 Intelligent RFID as enabling technology;235
10.4.3;3 Modelling approaches;236
10.4.4;4 Software simulation for the table-shift approach;237
10.4.5;5 Implementation;238
10.4.6;6 Summary and outlook;240
11;Sustainable Collaboration;242
11.1;Effects of Autonomous Cooperation on the Robustness of International Supply Networks – Contributions and Limitations for the Management of External Dynamics in Complex Systems;243
11.1.1;1 Risks of External Dynamics for the Robustness of Complex International Supply Networks;243
11.1.2;2 Autonomous Cooperation as an Approach to Increase the Robustness of ISN;245
11.1.3;4 Conclusions;250
11.2;Sustainability and Effectiveness in Global Supply Chains: Toward an Approach Based on a Long-term Learning Process;253
11.2.1;1 Introduction;253
11.2.2;2 Logistic Systems;254
11.2.3;3 Logistic Systems’ Potential Absorptive Capacity;256
11.2.4;4 Preliminary Conclusions and Prospective Research;258
11.3;Risk Management in Dynamic Logistic Systems by Agent Based Autonomous Objects;261
11.3.1;1 Introduction;261
11.3.2;2 Complexity and Dynamic in Logistic Systems;262
11.3.3;3 Control of a Dynamic System by Online Risk Management;264
11.3.4;4 Risk Management of Autonomous Objects;265
11.3.5;5 Technical Risk Aware Decision-Making;266
11.3.6;6 Conclusion;267
12;Knowledge Management and Service Models in Logistics;269
12.1;Knowledge Management in Intermodal Logistics Networks;270
12.1.1;1 Intermodal logistics networks;270
12.1.2;2 Challenges in Knowledge Management;271
12.1.3;4 Conclusions;275
12.2;Knowledge Management in Food Supply Chains;277
12.2.1;1 Introduction;277
12.2.2;2 Knowledge Management Processes;278
12.2.3;3 Organizational Approach to Knowledge Management;279
12.2.4;4 Conclusion;282
12.3;Service Models for a Small-sized Logistics Service Provider – A Case Study from Finland;285
12.3.1;1 Introduction;285
12.3.2;2 Service development steps;288
12.3.3;3 Services for small sized LSP;289
12.3.4;4 Conclusions;290
13;Container Logistics;292
13.1;A Framework for Integrating Planning Activities in Container Terminals;293
13.1.1;1 Introduction;293
13.1.2;2 The framework for a planning procedure;294
13.1.3;3 Resource profiles for various activities;296
13.1.4;4 Conclusion;300
13.2;Electronic Seals for Efficient Container Logistics;302
13.2.1;1 Introduction;302
13.2.2;2 Container Electronic Seals;303
13.2.3;3 Cost-Effective Investments and Returns on ESeals;304
13.2.4;4 Conclusions;308
13.3;Towards Autonomous Logistics: Conceptual, Spatial and Temporal Criteria for Container Cooperation;310
13.3.1;1 Introduction;310
13.3.2;2 Criteria for Cooperation;311
13.3.3;3 Case Study;315
13.3.4;4 Discussion;316
13.4;Distributed Process Control by Smart Containers;318
13.4.1;1 Introduction;318
13.4.2;2 Problem;319
13.4.3;3 Solution ideas;320
13.4.4;4 Technical aspects;320
13.4.5;5 Communicational aspects;321
13.4.6;6 Modern Information processing;323
13.4.7;7 Related work;324
13.4.8;8 Future work;325
14;Autonomous Control in Logistics;326
14.1;Autonomous Units for Communication-based Dynamic Scheduling;327
14.1.1;1 Introduction;327
14.1.2;2 Autonomous Units;328
14.1.3;3 Communication-based Dynamic Scheduling;329
14.1.4;4 Conclusion;333
14.2;Autonomously Controlled Adaptation of Formal Decision Models – Comparison of Generic Approaches;336
14.2.1;1 Introduction;336
14.2.2;2 Vehicle Scheduling Problem;336
14.2.3;3 Online Decision Strategies;338
14.2.4;4 Numerical Experiments;339
14.2.5;5 Conclusions;343
14.3;Clustering in Autonomous Cooperating Logistic Processes;344
14.3.1;1 Introduction;344
14.3.2;2 Routing and Clustering Approach;345
14.3.3;3 Scenario Description;346
14.3.4;4 Communication Traffic for Clustering;347
14.3.5;5 Results;350
14.3.6;6 Summary and Outlook;351
14.4;Application of Small Gain Type Theorems in Logistics of Autonomous Processes;353
14.4.1;1 Introduction;353
14.4.2;2 Motivating example;354
14.4.3;3 Feedback loop as a two nodes network;355
14.4.4;4 Conclusions;358
15;Next Generation Supply Chain Concepts;361
15.1;Web-service Based Integration of Multi-organizational Logistic Process;362
15.1.1;1 Introduction;362
15.1.2;2 Backgrounds;363
15.1.3;3 Web service and BPEL4WS;365
15.1.4;4 Workflow integration using BPEL4WS;366
15.1.5;5 System implementation: uEngine;370
15.1.6;6 Conclusions;371
15.2;An Approach for the Integration of Data Within Complex Logistics Systems;373
15.2.1;1 Motivation;373
15.2.2;2 Challenge;375
15.2.3;3 State of the Art;376
15.2.4;4 Approach to Data Integration;377
15.2.5;5 Approach and Methodology;379
15.2.6;6 Conclusion;381
15.3;Developing a Measurement Instrument for Supply Chain Event Management-Adoption;383
15.3.1;1 Introduction;383
15.3.2;2 Methodology;384
15.3.3;3 Implications;392
15.3.4;4 Outlook;394
15.4;Developing a Security Event Management System for Intermodal Transport;397
15.4.1;1 Introduction;397
15.4.2;2 The SCEM approach;398
15.4.3;3 Logistics Event Manager;399
15.4.4;4 Security Event Manager;400
15.4.5;5 Conclusions;404
16;Logistic Processes Modelling;405
16.1;Autonomous Control of a Shop Floor Based on Bee’s Foraging Behaviour;406
16.1.1;1 Introduction;406
16.1.2;2 Autonomy in production logistics;407
16.1.3;3 Shop floor scenario;407
16.1.4;4 Autonomous control based on bee’s foraging behaviour;408
16.1.5;5 Simulation Results;410
16.1.6;6 Conclusion;413
16.2;Proof Principles of CSP – CSP-Prover in Practice;415
16.2.1;1 Introduction;415
16.2.2;2 The process algebra CSP in CSP-Prover;417
16.2.3;3 Algebraic Laws;419
16.2.4;4 Fixed point analysis;423
16.2.5;5 Deadlock analysis;427
16.2.6;6 Summary and Future work;431
16.3;Application of Markov Drift Processes to Logistical Systems Modeling;433
16.3.1;1 Definition of Markov Drift Process and its Properties;433
16.3.2;2 Production Line with Unreliable Units;436
16.3.3;3 Interaction of Two Transport Units Via Warehouse;438
16.3.4;4 Optimal Cargo-Flows Distribution among a Set of Transshipment Points;441
16.3.5;5 Conclusion;445
16.4;Analysis of Decentral Order-picking Control Concepts;446
16.4.1;1 Introduction;446
16.4.2;2 Application;447
16.4.3;3 Control strategies;448
16.4.4;5 Conclusion;453