Cover
Title Page
Preface
About this book
1 Agent Technology in Modern Manufacturing
1. 1.1 Introduction
2. 1.2 Agent and Multi‐Agent System
3. 1.3 Agent Technologies in Manufacturing Systems
4. 1.4 Book Organization
5. References
2 The Technical Foundation of a Multi‐Agent System
1. 2.1 Introduction
2. 2.2 The Structure of an Agent
3. 2.3 The Structure of a Multi‐Agent System
4. 2.4 Modeling Methods of a Multi‐Agent System
5. 2.5 The Communication and Interaction Model of a Multi‐Agent System
6. 2.6 The Communication Protocol for a Multi‐Agent System
7. 2.7 The Interaction Protocol for a Multi‐Agent System
8. 2.8 Conclusion
9. References
3 Multi‐Agent‐Based Production Planning and Control
1. 3.1 Introduction
2. 3.2 Manufacturing Systems
3. 3.3 Production Planning and Control
4. 3.4 Multi‐Agent‐Based Push‐Pull Production Planning and Control System (MAP4CS)
5. 3.5 Conclusion
6. References
4 Multi‐Agent‐Based Production Planning for Distributed Manufacturing Systems
1. 4.1 Introduction
2. 4.2 Production Planning for Distributed Manufacturing Systems
3. 4.3 Multi‐Agent‐Based Production Planning in Distributed Manufacturing Systems
4. 4.4 Agents in Multi‐Agent Production Planning Systems
5. 4.5 Contract Net Protocol‐Based Production Planning Optimization Method
6. 4.6 Bid Auction Protocol‐Based Production Planning Optimization Method
7. 4.7 Conclusion
8. References
5 Multi‐Agent‐Based Production Scheduling for Job Shop Manufacturing Systems
1. 5.1 Introduction
2. 5.2 Production Scheduling in Job Shop Manufacturing Systems
3. 5.3 Multi‐Agent Double Feedback–Based Production Scheduling in Job Shop Manufacturing Systems
4. 5.4 Agents in the Multi‐Agent Double Feedback–Based Scheduling System
5. 5.5 Positive Feedback–Based Production Scheduling in Job Shop Manufacturing Systems
6. 5.6 Negative Feedback–Based Production Rescheduling in Job Shop Manufacturing Systems
7. 5.7 Conclusion
8. References
6 Multi‐Agent‐Based Production Scheduling in Re‐Entrant Manufacturing Systems
1. 6.1 Introduction
2. 6.2 Production Scheduling in Re‐Entrant Manufacturing Systems
3. 6.3 Multi‐Agent‐Based Hierarchical Adaptive Production Scheduling in Re‐Entrant Manufacturing Systems
4. 6.4 Agents in a Multi‐Agent Hierarchical Adaptive Production Scheduling System
5. 6.5 Hierarchical Production Scheduling in Re‐Entrant Manufacturing Systems
6. 6.6 Adaptive Rescheduling in Re‐Entrant Manufacturing Systems
7. 6.7 Conclusion
8. References
7 Multi‐Agent‐Based Production Control
1. 7.1 Introduction
2. 7.2 Multi‐Agent Production Control System
3. 7.3 Agents in Multi‐Agent Production Control Systems
4. 7.3.1 Collaborative Task Management Agent
5. 7.4 Technologies and Methods for Multi‐Agent Production Control Systems
6. 7.5 Conclusion
7. References
8 Multi‐Agent‐Based Material Data Acquisition
1. 8.1 Introduction
2. 8.2 RFID Technology
3. 8.3 Agent‐Based Material Data Acquisition System
4. 8.4 Agents in Multi‐Agent RFID‐Based Material Data Acquisition Systems
5. 8.5 Multi‐Agent RFID‐Based Material Data Acquisition Systems
6. 8.6 Conclusion
7. References
9 Multi‐Agent‐Based Equipment Data Acquisition
1. 9.1 Introduction
2. 9.2 Basics of OPC Technology
3. 9.3 Agent‐Based Equipment Data Acquisition System
4. 9.4 Agents in the Multi‐Agent OPC‐Based Equipment Data Acquisition System
5. 9.5 Implementation of a Multi‐Agent OPC‐Based System
6. 9.6 Conclusion
7. References
10 The Prototype of a Multi‐Agent‐Based Production Planning and Control System
1. 10.1 Introduction
2. 10.2 Architecture of a Prototype System
3. 10.3 Agent Packages and Communication in a Prototype System
4. 10.4 The Manufacturing System Simulation in a Prototype System
5. 10.5 Software Implementation and Application of a Prototype System
6. 10.6 Conclusion
7. References
Index
End User License Agreement

List of Tables

Chapter 04
1. Table 4‐1 Resource Information.
2. Table 4‐2 BOM of product 1.
3. Table 4‐3 Scenario parameters in production planning optimization.
4. Table 4‐4 Demand of Product 1.
5. Table 4‐5 Available production capacity.
6. Table 4‐6 Available Amount of Raw Material per Time Node.
7. Table 4‐7 Production Plan of the Contract Net Protocol‐Based Collaboration MAS in a Nondeterministic Environment.
8. Table 4‐8 production plan of the contract net protocol‐based collaboration MAS in a deterministic environment.
9. Table 4‐9 Production plan of the bid auction protocol‐based collaboration MAS in nondeterministic environment.
10. Table 4‐10 Production plan of the bid auction protocol‐based collaboration MAS in deterministic environment.
Chapter 05
1. Table 5‐1 A list of incoming tasks.
2. Table 5‐2 Unit operation processing time (“/” without assembling, min).
3. Table 5‐3 Unit cost of products ($).
4. Table 5‐4 The available hours in each machine (daily working hours, h).
5. Table 5‐5 The final positive feedback production scheduling results.
6. Table 5‐6 A list of new incoming tasks.
7. Table 5‐7 Results of the ant colony auction protocol based negative feedback rescheduling approach.
Chapter 06
1. Table 6‐1 Configuration of SFL.
2. Table 6‐2 Lot process stages.
3. Table 6‐3 Results.
4. Table 6‐4 Samples of FNN training and testing.
5. Table 6‐5 Output of FNN‐based rescheduling strategy.
Chapter 08
1. Table 8‐1 EPC encoding scheme.
2. Table 8‐2 RFID Operating Frequency Distribution.^[15]
3. Table 8‐3 RFID equipment basic parameters.
4. Table 8‐4 EPC‐96I Coding Allocation Scheme.
Chapter 09
1. Table 9.1 Methods supported by the OPC‐XML‐DA specification.

List of Illustrations

Chapter 01
1. Figure 1‐1 The content of this book.
Chapter 02
1. Figure 2‐1 The basic structure of an Agent.
2. Figure 2‐2 The basic structure of a thinking Agent.
3. Figure 2‐3 The basic structure of a reactive Agent.
4. Figure 2‐4 The hierarchical structure of a Multi‐Agent System.
5. Figure 2‐5 The federal structure of a Multi‐Agent System.
6. Figure 2‐6 The fully autonomous structure of a Multi‐Agent System.
7. Figure 2‐7 The use case diagram of accounts receivable and payment in the procurement procedure in the production management process.
8. Figure 2‐8 The sequence diagram of accounts receivable and payment of procurement procedure in the production management process.
9. Figure 2‐9 The hierarchical model of the communication and interaction process among Agents.
Chapter 03
1. Figure 3‐1 The material handling process of a Job Shop manufacturing system.
2. Figure 3‐2 The material handling process of a Flow Shop manufacturing system.
3. Figure 3‐3 The material handling process of a RMS.
4. Figure 3‐4 The push‐based production planning and control strategy.
5. Figure 3‐5 The pull‐based production planning and control strategy.
6. Figure 3‐6 A hybrid push‐pull production planning and control system.
7. Figure 3‐7 Management layers and contents of a production planning and control system.
8. Figure 3‐8 The structure of a MAP4CS.
9. Figure 3‐9 The running model of a MAP4CS.
10. Figure 3‐10 Use case diagram of a Multi‐Agent push production planning system.
11. Figure 3‐11 Use case diagram of a Multi‐Agent hybrid push‐pull production scheduling system.
12. Figure 3‐12 Use Case Diagram of a Multi‐Agent Pull Production Control System.
13. Figure 3‐13 Use case diagram of a Multi‐Agent fundamental data management system.
14. Figure 3‐14 Resource capacity information blackboard of an Agent.
15. Figure 3‐15 Resource capacity information.
16. Figure 3‐16 Unit product processing time information blackboard.
17. Figure 3‐17 Update resource agent information list.
Chapter 04
1. Figure 4‐1 A distributed manufacturing system.
2. Figure 4‐2 Traditional production planning process.
3. Figure 4‐3 The structure tree of product A.
4. Figure 4‐4 The structure of a Multi‐Agent production planning system for distributed manufacturing systems.
5. Figure 4‐5 The running model of a Multi‐Agent‐based production planning system.
6. Figure 4‐6 The use case diagram of an order/product demand management Agent.
7. Figure 4‐7 The management flow of the order/product demand management Agent.
8. Figure 4‐8 The mechanism of the order/product demand management Agent.
9. Figure 4‐9 The use case diagram of a cooperative planning Agent.
10. Figure 4‐10 The mechanism of a cooperative planning Agent.
11. Figure 4‐11 The use case diagram of a critical resource capacity management Agent.
12. Figure 4‐12 The mechanism of a critical resource capacity management Agent.
13. Figure 4‐13 The procedure for implementing the contract net protocol.
14. Figure 4‐14 Structure of the contract net protocol‐based production planning MAS.
15. Figure 4‐15 The collaborative process of the contract net protocol‐based production planning MAS.
16. Figure 4‐16 Bid auction protocol‐based negotiation process in Multi‐Agent production planning systems.
Chapter 05
1. Figure 5‐1 The architecture of Multi‐agent double feedback based production scheduling in Job Shop manufacturing systems.
2. Figure 5‐2 Double feedback production scheduling in Job Shop manufacturing systems.
3. Figure 5‐3 The use case diagram of a Task Management Agent.
4. Figure 5‐4 The mechanism of the Task Management Agent.
5. Figure 5‐5 The use case diagram of a collaborative scheduling Agent.
6. Figure 5‐6 The mechanism of the collaborative scheduling Agent.
7. Figure 5‐7 The use case diagram of a resource capacity management Agent.
8. Figure 5‐8 Multi‐Agent Positive Feedback Scheduling System based on Contract Net Protocol.
9. Figure 5‐9 The Topology model of resources in the shop floor.
10. Figure 5‐10 The foraging behaviour of real ants.
11. Figure 5‐11 The negotiation model among Agents in a Multi‐Agent negative feedback rescheduling system for Job Shop manufacturing systems.
Chapter 06
1. Figure 6‐1 Front‐end processing of semiconductor manufacturing.
2. Figure 6‐2 The architecture of Multi‐Agent hierarchical adaptive production scheduling in re‐entrant manufacturing systems.
3. Figure 6‐3 Hierarchical adaptive production scheduling in re‐entrant manufacturing systems.
4. Figure 6‐4 The use case diagram of a task management agent.
5. Figure 6‐5 Structure of task management agent.
6. Figure 6‐6 The use case diagram of a Collaborative Scheduling Agent.
7. Figure 6‐7 Mechanism of the Collaborative Scheduling Agent.
8. Figure 6‐8 The use case diagram of a resource capacity management Agent.
9. Figure 6‐9 Contact net protocol based collaborative scheduling.
10. Figure 6‐10 Network flow.
11. Figure 6‐11 GPGP‐CN based SPM pool Collaborative Scheduling.
12. Figure 6‐12 Local task view of each SPM Resource Capacity Management Agent.
13. Figure 6‐13 Non‐Local Task View of each SPM Resource Capacity Management Agent.
14. Figure 6‐14 GPGP‐CN based BPM group Collaborative Scheduling.
15. Figure 6‐15 Local task view of each BPM Resource Capacity Management Agent.
16. Figure 6‐16 Non‐local task view of each BPM Resource Capacity Management Agent.
17. Figure 6‐17 Result of Auction and Bid. Dual Difference Rate w=|(Feasible Cost—Low Bound Cost)/Low Bound Cost.
18. Figure 6‐18 Scheduling cost of wafer fabrication (per day).
19. Figure 6‐19 FNN Structure.
20. Figure 6‐20 FNN learning process.
21. Figure 6‐21 Linear regression analysis of FNN’s output.
Chapter 07
1. Figure 7‐1 The architecture of Multi‐Agent production control system.
2. Figure 7‐2 Production task allocation in Multi‐Agent production control systems.
3. Figure 7‐3 Production process management in Multi‐Agent Production Control Systems.
4. Figure 7‐4 The use case diagram of a collaborative task management Agent.
5. Figure 7‐5 The mechanism of the collaborative task management Agent.
6. Figure 7‐6 The use case diagram of a machine management Agent.
7. Figure 7‐7 The use case diagram of a material management Agent.
8. Figure 7‐8 The use case diagram of a production monitoring Agent.
9. Figure 7‐9 The use case diagram of a warning management Agent.
10. Figure 7‐10 Workflow of abnormal warnings.
11. Figure 7‐11 The use case diagram of a performance analysis Agent.
12. Figure 7‐12 Statistical workflow of production process data.
13. Figure 7‐13 The Use case diagram of a quality management Agent.
14. Figure 7‐14 The mechanism of the quality management Agent.
15. Figure 7‐15 The use case diagram of a production process tracking and tracing Agent.
16. Figure 7‐16 The mechanism of a production process tracking and tracing Agent.
17. Figure 7‐17 Workflow of real‐time data visualization.
18. Figure 7‐18 Manchester encoding rule.
19. Figure 7‐19 Status of differential Manchester encoding rule.
20. Figure 7‐20 Material loss prevention algorithm.
21. Figure 7‐21 Tracking information.
22. Figure 7‐22 The history information inquiry algorithm in the production process.
23. Figure 7‐23 Inquire about production data and issue backtracking production orders.
24. Figure 7‐24 Interaction among Agents in the order management process.
Chapter 08
1. Figure 8‐1 RFID
2. Figure 8‐2 Multi‐Agent‐based material data acquisition system.
3. Figure 8‐3 Data processing procedure.
4. Figure 8‐4 Agent installation and registration.
5. Figure 8‐5 Information processing flowchart.
6. Figure 8‐6 The use case diagram of an RFID Middleware Agent.
7. Figure 8‐7 The Structure of an RFID Middleware Agent.
8. Figure 8‐8 The reader interface of an RFID middleware Agent.
9. Figure 8‐9 Algorithm of an event processing module.
10. Figure 8‐10 Flowchart of the smooth filter module.
11. Figure 8‐11 Flowchart of the data selection module.
12. Figure 8‐12 Tag encoding and data analysis.
13. Figure 8‐13 Flowchart of an RFID middleware Agent.
14. Figure 8‐14 The use case diagram of an RFID Reader Agent.
15. Figure 8‐15 The use case diagram of an RFID Tag Agent.
16. Figure 8‐16 RFID tag Information.
17. Figure 8‐17 Interface of Hardware Connection and Data Acquisition in an RFID Middleware Agent.
18. Figure 8‐18 Interface of Real‐Time Processing and Publishing in an RFID Middleware Agent.
19. Figure 8‐19 Interface of Real‐Time Production Tracking.
Chapter 09
1. Figure 9‐1 Traditional data integration methods.
2. Figure 9‐2 OPC‐Based Data Integration Solutions.
3. Figure 9‐3 The Data Acquisition Based on the OPC Technology.
4. Figure 9‐4 The Data Acquisition MAS Structure Based on OPC Technology.
5. Figure 9‐5 The Structure of the Manufacturing Information Integration Network.
6. Figure 9‐6 The OPC client Agent Creation.
7. Figure 9‐7 The Equipment Data Acquisition Flow.
8. Figure 9‐8 The use case diagram of an OPC Agent.
9. Figure 9‐9 The structure of an OPC Agent.
10. Figure 9‐10 The use case diagram of an OPC server Agent.
11. Figure 9‐11 OPC server Agent structure.
12. Figure 9‐12 The Information flow of an OPC Server Agent.
13. Figure 9‐13 The use case diagram of an OPC Client Agent.
14. Figure 9‐14 Structure of OPC client Agent.
15. Figure 9‐15 The communication timing diagram between an OPC client Agent and an OPC server Agent.
16. Figure 9‐16 System hardware network architecture.
17. Figure 9‐17 System network configuration.
18. Figure 9‐18 Add data to PC Access.
19. Figure 9‐19 Information process of data acquisition
20. Figure 9‐20 OPC Agent configuration interface.
21. Figure 9‐21 Real‐time data acquisition in the remote devices.
Chapter 10
1. Figure 10‐1 The software architecture of the prototype system.
2. Figure 10‐2 The hardware architecture of the prototype system.
3. Figure 10‐3 The communication model for Agents.
4. Figure 10‐4 The collaborative relationships between Agents in production planning and control.
5. Figure 10‐5 The basic principle of simulation.
6. Figure 10‐6 A simulation modeling instance of Re‐entrant manufacturing systems.
7. Figure 10‐7 The logic architecture of a manufacturing control system.
8. Figure 10‐8 The SOAP‐based interaction model for Web service.
9. Figure 10‐9 Basic functional modules.
10. Figure 10‐10 The running process of a prototype system.
11. Figure 10‐11 Production planning in the Multi‐Agent production planning system based on a collaborative protocol and a negotiation protocol.
12. Figure 10‐12 The positive feedback production scheduling process in Job Shop manufacturing systems.
13. Figure 10‐13 The negative feedback production rescheduling process in Job Shop manufacturing systems.
14. Figure 10‐14 The system‐layer global collaborative control module based on Combinatorial auction.
15. Figure 10‐15 Results of the ETAEMS/GPGP‐CN‐based collaborative dynamic control module at the machine layer.
16. Figure 10‐16 Rescheduling results of the Multi‐Agent production scheduling system.
17. Figure 10‐17 Multi‐Agent production control system.

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Library of Congress Cataloging‐in‐Publication Data

Names: Zhang, Jie, 1963 September 21– author.
Title: Multi‐agent‐based production planning and control / Jie Zhang, Shanghai Jiao Tong University, China.
Description: First edition. | Hoboken, NJ, USA : John Wiley & Sons, Inc., [2017] | Includes bibliographical references and index.
Identifiers: LCCN 2016044467 (print) | LCCN 2017002169 (ebook) | ISBN 9781118890066 (cloth) | ISBN 9781118890080 (pdf) | ISBN 9781118890097 (epub)
Subjects: LCSH: Production planning. | Production control.
Classification: LCC TS155 .Z4349 2017 (print) | LCC TS155 (ebook) | DDC 658.5–dc23
LC record available at https://lccn.loc.gov/2016044467

Cover image: © enjoynz/Gettyimages
Cover design by Wiley

Preface

With the rapid development of advanced manufacturing technology, manufacturing models have developed: from single‐piece production, mass production, small batch production with large product variation to customized production. More challenges lie ahead in this global manufacturing era, such as rapidly changing consumer demands, increased product varieties and shortened product life cycles and increasingly fluctuating markets, to name a few. Traditional push or pull production management methods have become more and more unsuited to the dynamic environment. In order to be more efficient in such an environment, flexibility, intelligence and self‐adaptation have become the rule‐of‐thumb criteria for the evolution of new manufacturing systems. Therefore, a new hybrid push‐pull production planning, scheduling and control system has been proposed.

Since 1992, Agent technology has gradually become a hot topic for Japanese and American research. In 1992, the Japanese Intelligent Manufacturing System Program concentrated on the invention of new Agent‐based manufacturing methods as one of its main research areas. In 1993, the U.S. National Center for Manufacturing Science started a number of projects related to Agent‐based manufacturing. Agent technology has been taken into consideration as a promising technique to solve production planning, scheduling and control problems in complex manufacturing systems so as to effectively enhance system flexibility, to improve product quality and to reduce production costs.

I have worked on investigating theories and techniques of production planning, scheduling and control in advanced manufacturing systems. In particular, I have completed National Natural Science Foundation Programs of China and National High Technology Research and Development Programs of China based on Agent technology. With the support of these projects, I have published a large number of papers in the field of Agent technology. This book is a systematic summary of these research results. The focus of this book is on Agent‐based adaptive, intelligent, collaborative methods and technologies related to production planning, scheduling and control systems. The book also presents data acquisition systems based on RFID technology and OPC technology.

I am grateful to Xiaoxi Wang, Wei Qin, and Qiong Zhu for their assistances in the preparation of this book. Meanwhile, Cong Pan, Junliang Wang, Peng Zhang and Jungang Yang completed many auxiliary works. Lihui Wu, Gong Zhang, Shiyong Tian, Yijun Dong, Lei Sun, Guobao Liu and Zhi Xia have provided relevant documents, I thank all of them. I wish to acknowledge a large number of references in the completion of the manuscript. The responsibility is mine alone for any errors.

The writing of this book has been supported by the National Nature Science Foundation of China under Grant No. 51435009, Grant No. 51275307, and Grant No. 50875172, and by the National High Technology Research and Development Program (863 Program) of China under Grant No. 2007AA04Z019.

Theories, methods and applications of production planning, scheduling and control in modern manufacturing systems are rapidly developing. Agent technology has become a hot topic in the field of production planning, scheduling and control. If you have any questions about shortcomings and mistakes of this book, please do not hesitate to contact me.

Jie Zhang
December 2015