Leitão / Karnouskos Industrial Agents

Emerging Applications of Software Agents in Industry
1. Auflage 2015
ISBN: 978-0-12-800411-1
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 476 Seiten

Industrial Agents explains how multi-agent systems improve collaborative networks to offer dynamic service changes, customization, improved quality and reliability, and flexible infrastructure. Learn how these platforms can offer distributed intelligent management and control functions with communication, cooperation and synchronization capabilities, and also provide for the behavior specifications of the smart components of the system. The book offers not only an introduction to industrial agents, but also clarifies and positions the vision, on-going efforts, example applications, assessment and roadmap applicable to multiple industries. This edited work is guided and co-authored by leaders of the IEEE Technical Committee on Industrial Agents who represent both academic and industry perspectives and share the latest research along with their hands-on experiences prototyping and deploying industrial agents in industrial scenarios. - Learn how new scientific approaches and technologies aggregate resources such next generation intelligent systems, manual workplaces and information and material flow system - Gain insight from experts presenting the latest academic and industry research on multi-agent systems - Explore multiple case studies and example applications showing industrial agents in a variety of scenarios - Understand implementations across the enterprise, from low-level control systems to autonomous and collaborative management units

Autoren/Hrsg.

Leitão, Paulo

Karnouskos, Stamatis

Inhaltsverzeichnis

1;Front Cover;1
2;Industrial Agents: Emerging Applications of Software Agents in Industry;4
3;Copyright;5
4;Contents;6
5;Preface;16
6;List of Contributors;18
7;Part I: Industrial Agents: Concepts Anddefinitions;22
7.1;Chapter 1:
Software Agent Systems;24
7.1.1;1.1. Introduction;24
7.1.2;1.2. Fundamentals of Agents and Agent-Based Systems;24
7.1.2.1;1.2.1. Agents and Agent Properties;24
7.1.2.2;1.2.2. Types of Agents;25
7.1.2.3;1.2.3. Multi-Agent Systems and Their Properties;27
7.1.2.4;1.2.4. Agent Communication;27
7.1.2.5;1.2.6. Development Support for Agent-Based Systems;29
7.1.2.5.1;1.2.6.1. Development Toolkits and Frameworks for MASs;29
7.1.2.5.2;1.2.6.2. Agent-Oriented Programming Languages;31
7.1.2.5.3;1.2.6.3. Agent-Based Software Development Methodologies;31
7.1.2.6;1.2.7. MAS-Based Simulation Environments;32
7.1.3;1.3. Supporting Technologies and Concepts;33
7.1.3.1;1.3.1. Ontologies;33
7.1.3.2;1.3.2. Self-Organization and Emergence;35
7.1.3.2.1;1.3.2.1. Self-Organization;35
7.1.3.2.2;1.3.2.2. Emergence;35
7.1.3.3;1.3.3. Swarm Intelligence and Stigmergy;36
7.1.4;1.4. Conclusions;37
7.1.5;References;38
7.2;Chapter 2:
Industrial Agents;44
7.2.1;2.1. Introduction;44
7.2.2;2.2. Modern Industrial Manufacturing Systems and Their Requirements;45
7.2.3;2.3. Architectural Types of Industrial Manufacturing Systems;48
7.2.4;2.4. The Holonic Paradigm and MAS-Based Holonic Systems;51
7.2.5;2.5. Development Tools for Industrial MASs;56
7.2.6;2.6. How MASs Can Nourish Other Industrial Approaches;56
7.2.7;2.7. Industrial MASs: Challenges and Research Areas;60
7.2.8;2.8. Conclusions;62
7.2.9;References;62
7.3;Chapter 3:
The Design, Deployment, and Assessment of Industrial Agent Systems;66
7.3.1;3.1. Introduction;66
7.3.2;3.2. Distributed versus Self-Organizing Design;67
7.3.3;3.3. Design Challenges and Directions;69
7.3.3.1;3.3.1. Coupled Design;70
7.3.3.2;3.3.2. Embedded Design;72
7.3.3.3;3.3.3. Design Guidelines;75
7.3.4;3.4. Deployment;77
7.3.5;3.5. Assessment;79
7.3.6;3.6. Conclusions;81
7.3.7;References;82
8;Part: Industrial Agents: Related Concepts and Technologies ;86
8.1;Chapter 4:
Industrial Agents in the Era of Service-Oriented Architectures and Cloud-Based Industrial Infrastructures;88
8.1.1;4.1. Introduction;88
8.1.2;4.2. Technologies;90
8.1.2.1;4.2.1. Toward SOAs;90
8.1.2.2;4.2.2. Toward Web Service-Enabled Devices: DPWS, REST, OPC-UA;91
8.1.2.3;4.2.3. Cloud-Based Industrial Systems;92
8.1.3;4.3. Bridging Agents and SOA-Enabled Devices;93
8.1.3.1;4.3.1. Agent and Service Commonalities;94
8.1.3.2;4.3.2. Approaches to Combine Agents and Services;94
8.1.3.3;4.3.3. Enterprise Service Bus-Based Solutions;97
8.1.4;4.4. Use Case: Cyber-Physical Infrastructure Simulation by Coupling Software Agents and Physical Devices;98
8.1.5;4.5. Use Case: Service-Oriented Industrial Automation System;100
8.1.6;4.6. Conclusions and Future Directions;106
8.1.7;Acknowledgments;106
8.1.8;References;106
8.2;Chapter 5:
Distributed Real-Time Automation And Control - Reactive Control Layer For Industrial Agents;110
8.2.1;5.1. Introduction/Motivation;110
8.2.2;5.2. RCLs for Industrial Agents;111
8.2.3;5.3. Standard-Based Realization;113
8.2.3.1;5.3.1. PLC-Based Control with IEC 61131–3;113
8.2.3.1.1;5.3.1.1. Basic principles;113
8.2.3.1.2;5.3.1.2. The execution model;114
8.2.3.1.3;5.3.1.3. Communication interfaces;115
8.2.3.1.4;5.3.1.4. (Re-)configuration services;116
8.2.3.2;5.3.2. Distributed Control with IEC 61499;117
8.2.3.2.1;5.3.2.1. Basic principles;117
8.2.3.2.2;5.3.2.2. The execution model;119
8.2.3.2.3;5.3.2.3. Communication interfaces;120
8.2.3.2.4;5.3.2.4. (Re-)configuration services—IEC 61499 device management;120
8.2.4;5.4. Example;122
8.2.4.1;5.4.1. Selected Use Case;122
8.2.4.2;5.4.2. IEC 61131–3 Interface;123
8.2.4.3;5.4.3. IEC 61499 Interface;124
8.2.5;5.5. Discussion;125
8.2.6;5.6. Conclusions;126
8.2.7;References;127
8.3;Chapter 6:
Industrial Agents Cybersecurity;130
8.3.1;6.1. Introduction;130
8.3.2;6.2. Technology Trends and IAs;130
8.3.3;6.3. Agent Threat Context;133
8.3.3.1;6.3.1. Misuse of Agent(s) by the Host;133
8.3.3.2;6.3.2. Misuse of the Host by Agent(s);134
8.3.3.3;6.3.3. Misuse of an Agent by Another Agent;135
8.3.3.4;6.3.4. Misuse of Agent(s) or Host by Underlying Infrastructure;135
8.3.3.5;6.3.5. Complex Attacks;135
8.3.4;6.4. Requirements on IA Solutions;136
8.3.5;6.5. Discussion;138
8.3.6;6.6. Conclusions;139
8.3.7;References;140
8.4;Chapter 7:
Virtual Enterprises Based on Multiagent Systems;142
8.4.1;7.1. Introduction/Motivation;142
8.4.2;7.2. Characteristics of VEs;143
8.4.3;7.3. The Benefits of Forming VEs;144
8.4.4;7.4. Obstacles and Approaches for VEs;145
8.4.5;7.5. MASs in the Coordination of VEs;146
8.4.6;7.6. MASs in E-commerce Applications;146
8.4.7;7.7. Case Study;147
8.4.7.1;7.7.1. Multiagent VE Systems;149
8.4.7.2;7.7.2. The VE Ontology;150
8.4.8;7.8. Benefits and Assessment;152
8.4.9;7.9. Discussion;153
8.4.10;7.10. Conclusions;155
8.4.11;Acknowledgment;155
8.4.12;References;155
8.5;Chapter 8:
An Assessment of the Potentials and Challenges in Future Approaches for Automation Software;158
8.5.1;8.1. Introduction;158
8.5.2;8.2. Related Work;159
8.5.3;8.3. Assessing the Potentials and Challenges in Industry;161
8.5.3.1;8.3.1. Scenario 1: Agents for the Compensation of Sensor Contamination;161
8.5.3.2;8.3.2. Scenario 2: Agents for the Plug-and-Produce Configuration of Production Units;162
8.5.3.3;8.3.3. The Assessment of Potentials and Challenges in Industry;163
8.5.3.4;8.3.4. Results of the Survey in Machine and Plant Automation;163
8.5.4;8.4. Concepts for Agents in Manufacturing Automation;165
8.5.4.1;8.4.1. An Enhanced Concept for Soft-Sensor Estimation Using KDE;165
8.5.4.2;8.4.2. Model-Based Development of Agents and Soft Sensors;167
8.5.4.3;8.4.3. The Generation of Control Strategies from Agent Models;169
8.5.5;8.5. Summary and Outlook;170
8.5.6;References;171
8.6;Chapter 9:
Agent-Based Control of Production Systems—and Its Architectural Challenges;174
8.6.1;9.1. Introduction;174
8.6.2;9.2. Terms and Definitions;175
8.6.3;9.3. Generic Engineering Processes;176
8.6.4;9.4. Application Cases;179
8.6.4.1;9.4.1. Agent-Based Production Planning;179
8.6.4.2;9.4.2. Agent-Based Machine Configuration;182
8.6.4.3;9.4.3. Agent-Based Field-Level Automation Control Software;183
8.6.5;9.5. Architectural Challenges;186
8.6.6;9.6. Conclusions and Overview;188
8.6.7;Acknowledgments;188
8.6.8;References;188
8.7;Chapter 10:
Identification and Implementation of Agents for Factory Automation Exploiting Mechatronical Concepts for Production;192
8.7.1;10.1. Introduction;192
8.7.2;10.2. Starting Points;194
8.7.2.1;10.2.1. Agent Systems for Production System Automation and Control;194
8.7.2.2;10.2.2. Mechatronical Engineering of Production Systems;198
8.7.2.3;10.2.3. Comparisons of Mechatronic to Other Approaches;202
8.7.3;10.3. Mechatronic-Oriented Agent Systems;202
8.7.3.1;10.3.1. The Structure of Mechatronic-Oriented Agents;203
8.7.3.2;10.3.2. The Engineering Process of Mechatronic-Oriented Agent Systems;204
8.7.3.3;10.3.3. The Advantages of Mechatronic-Oriented Agent Systems;205
8.7.4;10.4. Summary;207
8.7.5;References;208
9;Part III: Industrial Agent Applications;212
9.1;Chapter 11:
Cloud Based Agent Framework For The Industrial Automation Sector;214
9.1.1;11.1. Introduction;214
9.1.2;11.2. Application Overview;215
9.1.3;11.3. Application Details;216
9.1.3.1;11.3.1. System Architecture;216
9.1.3.1.1;11.3.1.1. Controllers;217
9.1.3.1.2;11.3.1.2. Agent modules;217
9.1.3.1.3;11.3.1.3. The analytics orchestrator;217
9.1.3.1.4;11.3.1.4. Web service interface;218
9.1.3.1.5;11.3.1.5. The cloud analytics provider;218
9.1.3.2;11.3.2. Programming Model;218
9.1.3.2.1;11.3.2.1. The design and programming phase;219
9.1.3.2.2;11.3.2.2. The runtime phase;219
9.1.3.2.3;11.3.2.3. The offline processing phase;219
9.1.3.2.4;11.3.2.4. Common API specifications;220
9.1.3.3;11.3.3. Use Case Description;221
9.1.3.3.1;11.3.3.1. Data ingestion into the cloud;222
9.1.3.3.2;11.3.3.2. The worker role;223
9.1.3.3.3;11.3.3.3. Runtime execution;223
9.1.4;11.4. Benefits and Assessment;224
9.1.5;11.5. Discussion;225
9.1.6;11.6. Conclusions;226
9.1.7;References;226
9.2;Chapter 12:
Multi-Agent Systems for Real-Time Adaptive Resource Management;228
9.2.1;12.1. Introduction;228
9.2.2;12.2. The Problem and Solution for Adaptive Scheduling;228
9.2.2.1;12.2.1. The Modern Vision of Resource Scheduling Problem;228
9.2.2.2;12.2.2. Brief Overview of Existing Methods and Tools;229
9.2.2.3;12.2.3. The Multi-Agent Technology for Adaptive Scheduling;229
9.2.2.4;12.2.4. The Concept of Demand-SUPPLY Networks;230
9.2.2.5;12.2.5. The Formal Problem Statement;231
9.2.2.6;12.2.6. The Method of Adaptive Scheduling;232
9.2.2.7;12.2.7. The Basic Multi-Agent Solution for Adaptive Scheduling;234
9.2.2.8;12.2.8. The Multi-Agent Platform for Adaptive Scheduling;236
9.2.3;12.3. Examples of Applications for Industry;238
9.2.3.1;12.3.1. The MAS for Flights and Cargo Scheduling for the International Space Station;238
9.2.3.1.1;12.3.1.1. Application overview;238
9.2.3.1.2;12.3.1.2. Benefits and assessment;239
9.2.3.2;12.3.2. MAS for Scheduling Factory Workshops;241
9.2.3.2.1;12.3.2.1. Application overview;241
9.2.3.2.2;12.3.2.2. Benefits and assessment;244
9.2.3.3;12.3.3. MAS for Mobile Field Services Scheduling;245
9.2.3.3.1;12.3.3.1. Application overview;245
9.2.3.3.2;12.3.3.2. Benefits and assessment;247
9.2.4;12.4. Discussion;248
9.2.5;12.5. Conclusion;249
9.2.6;References;249
9.3;Chapter 13:
Large-Scale Network And Service Management With Wants;252
9.3.1;13.1. Introduction and Motivation;252
9.3.2;13.2. WANTS at a Glance;253
9.3.2.1;13.2.1. Overview of the Network;253
9.3.2.2;13.2.2. Key Aspects of WANTS;253
9.3.3;13.3. WANTS in Details;255
9.3.3.1;13.3.1. A Brief Recall on WADE;255
9.3.3.2;13.3.2. The Architecture of WANTS;257
9.3.3.3;13.3.3. A Service Provision Scenario;259
9.3.4;13.4. Discussion;261
9.3.4.1;13.4.1. Key Benefits;264
9.3.4.2;13.4.2. Lessons Learned;265
9.3.5;13.5. Conclusions;266
9.3.6;References;266
9.4;Chapter 14:
Cross-Domain Energy Savings by Means of Unified Energy Agents;268
9.4.1;14.1. Introduction/Motivation;268
9.4.2;14.2. Application Overview;271
9.4.3;14.3. Application Details;275
9.4.4;14.4. Benefits and Assessment;283
9.4.5;14.5. Discussion;286
9.4.6;14.6. Conclusions;287
9.4.7;References;288
9.5;Chapter 15:
A Multi-Agent System Coordination Approach for Resilient Self-Healing Operations in Multiple Microgrids;290
9.5.1;15.1. Introduction/Motivation;290
9.5.2;15.2. Problem Overview: Coordination and Control of Microgrids;291
9.5.2.1;15.2.1. Needs of Coordination and Control in Microgrids;292
9.5.2.2;15.2.2. Primary and Secondary Controls for Transient Stability;292
9.5.2.3;15.2.3. Secondary and Tertiary Coordination by Multi-Agent Systems;293
9.5.3;15.3. Application Details: The Multi-Agent System Coordination Approach for a Resilient Self-Healing Operation;294
9.5.3.1;15.3.1. Differential Algebraic Equations and the Model Predictive Control Approach;295
9.5.3.2;15.3.2. Mutual Connection Coordination by Heuristics;298
9.5.4;15.4. Benefits and Assessment: Impacts of MAS Coordination on Multiple Microgrid Transient Stability;298
9.5.4.1;15.4.1. Resilience Toward Net Load Variability;299
9.5.4.2;15.4.2. Resilience Toward Net Load Ramping;301
9.5.4.3;15.4.3. Resilience Toward Net Load Changes during High Load Levels;302
9.5.5;15.5. Discussion and Conclusions;303
9.5.6;References;305
9.6;Chapter 16:
Multi-Agent System for Integrating Quality and Process Control in a Home Appliance Production Line;308
9.6.1;16.1. Introduction/Motivation;308
9.6.2;16.2. Application Overview;309
9.6.2.1;16.2.1. Problem Description;309
9.6.2.2;16.2.2. Agent-Based Architecture;309
9.6.2.3;16.2.3. Cooperation Patterns and Ontology;312
9.6.3;16.3. Application Details;314
9.6.3.1;16.3.1. Implementation of the Agent-Based Solution;314
9.6.3.2;16.3.2. Installation in the Factory Plant;315
9.6.4;16.4. Benefits and Assessment;316
9.6.4.1;16.4.1. Qualitative Properties;316
9.6.4.2;16.4.2. Quantitative Impact;318
9.6.5;16.5. Discussion;319
9.6.6;16.6. Conclusions;320
9.6.7;Acknowledgments;320
9.6.8;References;320
9.7;Chapter 17:
Industrial Agents for the Fast Deployment of Evolvable Assembly Systems;322
9.7.1;17.1. Introduction;322
9.7.2;17.2. Problem Overview;323
9.7.3;17.3. IADE —Its Architecture and Associated Concepts;324
9.7.3.1;17.3.1. Process-Oriented Agents;325
9.7.3.1.1;17.3.1.1. Resource agents;325
9.7.3.1.2;17.3.1.2. Coalition leader agents;327
9.7.3.1.3;17.3.1.3. Product agents;329
9.7.3.2;17.3.2. Transport-Oriented Agents;329
9.7.3.2.1;17.3.2.1. Transport entity agents;329
9.7.3.2.2;17.3.2.2. Handover unit agents;330
9.7.3.3;17.3.3. Deployment Agents;330
9.7.3.4;17.3.4. Reference Agent Interactions;331
9.7.4;17.4. On the Implementation of the IADE Stack;334
9.7.4.1;17.4.1. The Technological Stack;334
9.7.4.2;17.4.2. Test Cases;336
9.7.5;17.5. Benefits and Assessment;340
9.7.6;17.6. Lessons Learned;341
9.7.7;17.7. Conclusions;341
9.7.8;References;342
9.8;Chapter 18:
Automation Agents for Controlling the Physical Components of a Transportation System;344
9.8.1;18.1. Introduction/Motivation;344
9.8.2;18.2. Application Overview;345
9.8.3;18.3. Application Details;346
9.8.3.1;18.3.1. Testbed for Distributed Control;346
9.8.3.2;18.3.2. Architecture of an Automation Agent;347
9.8.3.2.1;18.3.2.1. High-Level Control of an Automation Agent;347
9.8.3.2.2;18.3.2.2. Low-Level Control of an Automation Agent;348
9.8.3.2.3;18.3.2.3. Agent Configuration and Reconfiguration;349
9.8.3.3;18.3.3. System Reconfiguration due to a Component Failure;350
9.8.3.4;18.3.4. Example Process;352
9.8.4;18.4. Benefits and Assessment;354
9.8.4.1;18.4.1. Diagnostic Mechanisms;354
9.8.4.2;18.4.2. Reconfiguration Mechanisms;354
9.8.4.3;18.4.3. Modification of the System;356
9.8.5;18.5. Discussion;356
9.8.6;18.6. Conclusions;357
9.8.7;Acknowledgments;358
9.8.8;References;358
9.9;Chapter 19:
Intelligent Factory Agents with Predictive Analytics for Asset Management;362
9.9.1;19.1. Introduction/Motivation;362
9.9.2;19.2. Application Overview;364
9.9.3;19.3. Application Details;366
9.9.3.1;19.3.1. Strategy for Anomaly Detection and Fault Isolation;368
9.9.3.2;19.3.2. Prognosis and Decision Making with the Self-Recovery Function;369
9.9.3.3;19.3.3. An Industrial Agent Platform Based on an Embedded System;370
9.9.3.4;19.3.4. Industrial Agent Platforms Based on Cloud Infrastructures;371
9.9.4;19.4. Case Study;372
9.9.4.1;19.4.1. Air Compressors;372
9.9.4.2;19.4.2. Horizontal Machining Center;374
9.9.5;19.5. Benefits and Assessment;375
9.9.6;19.6. Discussion;376
9.9.6.1;19.6.1. Smart Factory Transformation From Component Level Point of View;376
9.9.6.2;19.6.2. Smart Factory Transformation from Machine Level Point of View;377
9.9.6.3;19.6.3. Smart Factory Transformation from Factory Level Point of View;379
9.9.7;19.7. Conclusions;379
9.9.8;References;380
9.10;Chapter 20:
A Biomimetic Approach to Distributed Maintenance Management Based on a Multi-Agent System;382
9.10.1;20.1. Motivation;382
9.10.2;20.2. An Overview of the Applications;384
9.10.2.1;20.2.1. IMS-TEMIIS;384
9.10.2.2;20.2.2. Intelligent Maintenance System for Microsatellite;385
9.10.2.3;20.2.3. Intelligent Maintenance Systems for Wind Farms;386
9.10.2.4;20.2.4. Maintenance Schedule for a Bus Fleet;386
9.10.3;20.3. Application Details: AI2MS, a MAS Based on a Biomimetic Approach;387
9.10.3.1;20.3.1. Data Provider Agents;389
9.10.3.2;20.3.2. Diagnostic Agents;390
9.10.3.3;20.3.2.1. Fault detection agents;391
9.10.3.4;20.3.2.2. New fault detection agents;391
9.10.3.5;20.3.2.3. Cooperative detection agents;391
9.10.3.6;20.3.3. Prognostic Agents;391
9.10.3.7;20.3.3.1. Device health assessment agent;391
9.10.3.8;20.3.3.2. Plant health assessment agent;392
9.10.3.9;20.3.4. Service Agents;392
9.10.3.10;20.3.4.1. Update agents;392
9.10.3.11;20.3.4.2. Evolution agents;393
9.10.4;20.4. Benefits and Assessment;393
9.10.5;20.5. Discussion;396
9.10.6;20.6. Conclusion;397
9.10.7;Acknowledgments;398
9.10.8;References;398
9.11;Chapter 21:
Programming of Multiagent Applications with JIAC;402
9.11.1;21.1. Introduction/Motivation;402
9.11.2;21.2. Application Overview;404
9.11.2.1;21.2.1. Functional Components and Standards;405
9.11.2.2;21.2.2. Communication and Messaging;406
9.11.2.3;21.2.3. Monitoring;407
9.11.2.4;21.2.4. (Commercial) Distribution;407
9.11.3;21.3. Application Details;407
9.11.3.1;21.3.1. Component Framework: Spring;408
9.11.3.2;21.3.2. Communication: ActiveMQ and JMS;408
9.11.3.3;21.3.3. Logging: Log4J;408
9.11.3.4;21.3.4. Monitoring: Java Management Extensions;409
9.11.3.5;21.3.5. Third-Party Interaction: Web Services;409
9.11.4;21.4. Tools;409
9.11.4.1;21.4.1. JIAC and Eclipse;410
9.11.4.2;21.4.2. The Agent World Editor;410
9.11.4.3;21.4.3. The Visual Service Design Tool;410
9.11.4.4;21.4.4. ASGARD;412
9.11.4.5;21.4.5. The Semantic Service Manager;413
9.11.4.6;21.4.6. The Agent Store;413
9.11.5;21.5. Benefits and Assessment;413
9.11.5.1;21.5.1. Applications;414
9.11.6;21.6. Discussion;415
9.11.7;21.7. Conclusion;417
9.11.8;Acknowledgments;418
9.11.9;References;418
10;Part IV: A Survey on Factors that Impact Industrial Agent Acceptance;420
10.1;Chapter 22:
A Survey on Factors that Impact Industrial Agent Acceptance;422
10.1.1;22.1. Introduction;422
10.1.2;22.2. Factors for Industrial Agent Acceptance;423
10.1.3;22.3. Methodology, Data Collection, and Demographics;425
10.1.4;22.4. Survey Results and Analysis;428
10.1.4.1;22.4.1. Industrial Agent Acceptance;429
10.1.4.2;22.4.2. Design;431
10.1.4.3;22.4.3. Technology;433
10.1.4.4;22.4.4. Hardware;435
10.1.4.5;22.4.5. Intelligence/Algorithms;437
10.1.4.6;22.4.6. Cost Tackling;439
10.1.4.7;22.4.7. Standardization;441
10.1.4.8;22.4.8. Application;443
10.1.4.9;22.4.9. Challenges;445
10.1.5;22.5. Conclusions;447
10.1.6;Acknowledgments;449
10.1.7;References;449
11;Reference Index;452
12;Author Index;464
13;Subject Index;466

Leseproben

Chapter 2 Industrial Agents
Rainer Unland    Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Essen, Germany
Department of Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand Abstract
Industrial applications have been going through significant changes in recent times. In particular, the trend toward globalization has changed the game significantly. Global business means global competition, which requires shorter product life cycles. In consumer-oriented businesses especially, this leads to a trend toward highly customized and individualized products. This imposes a number of profound and far-reaching demands on modern industrial manufacturing systems such as adaptability, agility, responsiveness, robustness, flexibility/reconfigurability, dynamic optimization, openness to new innovations, and, in some environments, continuously varying collaborations. Such goals can only be achieved if massively software-controlled integrated industrial manufacturing tools, machines, and environments become the default. In many cases, typical standalone, compartmentalized operations need to move toward decentralized, distributed, and networked manufacturing system architectures with intensive communication and collaboration, especially over long distances. For such complex systems, in order to work efficiently, a high level of understanding is necessary, which translates into a reasonable understanding of domain-specific semantics. Multi-agent-based application systems seem to be a promising and natural realization choice. Multi-agent systems (MASs) provide, among other things, decentralized architecture and decision making, modularity, robustness, flexibility, and adaptability to changes. This chapter provides a concise introduction into agent technology for industrial applications that rely on decentralized decision making and control. It concentrates on industrial applications, its evolvement, the consequences of this evolvement on modern industrial application systems, and the specific aspects and requirements on (multi-)agent-based industrial application systems. It, especially, also discusses the holonic paradigm and challenges and research areas for industrial MASs. Keywords Industrial agents Industrial manufacturing systems Holonic manufacturing systems Virtual ­organizations Smart grids Service-oriented architecture 2.1 Introduction
Industrial applications have been going through significant changes in recent years. Indeed, the trend toward globalization has changed the game significantly. While it offers huge opportunities, it also comes with severe challenges (cf. Marík and McFarlane, 2005; Marík et al., 2007). Global business means global competition, which requires shorter product life cycles. Particularly in consumer-oriented businesses, this leads to a trend toward highly customized and individualized products, especially when produced in high-wage countries. For companies, this may mean they need to join forces in order to develop and market trendy or niche products. As a consequence, the trend toward virtual enterprises (see, e.g., Camarinha and Afsarmanesh (1999) for a nice overview) and short-term collaborations will continue to grow. This imposes a number of profound and far-reaching demands on modern industrial manufacturing systems. The former principle goal of the manufacturing industry, namely optimization of the scheduling algorithm, has to take a back seat for the time being and has been replaced by several new goals such as adaptability, agility, responsiveness, robustness, flexibility/reconfigurability, dynamic optimization, openness to new innovations, and in some environments, to continuously varying collaborations. Such goals can only be achieved if massively software-controlled integrated industrial manufacturing tools, machines, and environments become the default (cf., e.g., Pechoucek and Marík, 2008; Mendes et al., 2009). In many cases, typical standalone, compartmentalized operations need to move toward decentralized, distributed, and networked manufacturing system architectures with intensive communication and collaboration, especially over long distances. For such complex systems to work efficiently, a high level of understanding is necessary, which translates into a reasonable understanding of domain-specific semantics (cf., e.g., Vittikh et al., 2013; Leitão et al., 2013a,b). This chapter only concentrates on industrial application systems that rely on decentralized decision making and control. This is what is usually meant from here on when the term industrial application is used. Against this background, multi-agent-based industrial application systems seem to be a promising and natural realization choice. Multi-agent systems (MASs) provide, among other things, decentralized architecture and decision making, modularity, robustness, flexibility, and adaptability to changes (cf., e.g., Zimmermann and Mönch, 2007; Leitão, 2009; Bratukhin et al., 2011; Sayda, 2011; Vrba et al., 2011). This chapter is meant to provide an introduction to industrial agent technology for industrial application systems that rely on decentralized decision making and control. Thus, the remainder of this chapter is organized as follows. Section 2.2 concentrates on industrial applications, its evolution, the consequences of this evolution on modern industrial application systems, and the specific aspects and requirements on (multi-)agent-based industrial application systems. It also especially discusses the holonic paradigm. Finally, Section 2.8 summarizes the conclusions from this chapter. 2.2 Modern Industrial Manufacturing Systems and Their Requirements
The last decade has seen a massive trend toward the computerization of nearly everything we have to deal with in our life. This, especially, also applies to machines and tools in industry. Computerization here means that hardware is equipped with some kind of software-controlled intelligence. It permits getting feedback and improving the flexibility, adaptability, and robustness of the hardware and the system as a whole. Additionally, more automatic machine-related communication can take place. Recent technology inventions such as RFID, smart cards, embedded systems, Wi-Fi, and Bluetooth communication have accelerated this process because they extend the communication possibilities significantly and may even allow products to become active decisional entities that react in real time to the actual state of the production system (cf. Zbib et al., 2012). In parallel to the digitalization of our world, a globalization of production, as well as competition, has taken place. With it comes the demand for shorter life cycles and individualized products. For industry, this implies a shift from static optimization for long production cycles to dynamic optimization for short product cycles (cf. Trentesaux, 2009). However, dynamic optimization has a long way to go before it will be as efficient and effective as static optimization. These market-driven requirements are often referred to as agility requirements. Gunasekaran (1999) defines agility “as the capability of surviving and prospering in a competitive environment of continuous and unpredictable change by reacting quickly and effectively to changing markets, driven by customer-designed products and services.” Trentesaux (2009) differentiates between business and technical agility. The first means aligning the production process toward continuously evolving economic as well as financial objectives and concentrates as such on the extra-production issues. In contrast, technical agility concentrates on intra-production issues (i.e., on its efficiency and effectiveness; cf. Bousbia et al., 2005). Efficiency stands for maximal exploitation of resources and hardware. Effectiveness translates to the capability of achieving the expected goal as well as possible, especially in situations of disturbances (e.g., order changes, machine breakdowns, production problems). It is widely studied in the industrial as well as the scientific communities (cf., e.g., Leitão and Restivo, 2008). Efficiency and effectiveness are related, but there are also differences. A system will be working with high efficiency if it exploits a given input of resources and machine availability and capability as much as possible regardless of whether the output is completely needed at the time. From the effectiveness point of view, the goal is achieved if production stops as soon as the target is reached, instead of producing any (unwanted) surplus. A very good example of these partially drastic changes in industry is the smart grid, also called the future energy grid (FEG) (cf. Trentesaux, 2009; Ramchurn et al., 2012). In a FEG, communication needs to switch from an order-based, one-way hierarchical flow to cooperative decision making based on two-way communication. Households that own renewable energy generators will become so-called prosumers because they produce as well as consume electricity. The term prosumer was coined around 1970 by Toffler in his book Future Shock. It describes actors in the marketplace who not only consume but also actively...