1.1 Introduction 1
1.2 Agent and Multi‐Agent System 1
1.2.1 Agent 2
1.2.2 Multi‐Agent System 4
1.3 Agent Technologies in Manufacturing Systems 7
1.3.1 Contemporary Manufacturing Systems 7
1.3.2 Agents in Production Planning and Control Systems 8
1.3.3 The Existing Requirements 10
1.4 Book Organization 11
1.4.1 Purpose of the Book 11
1.4.2 Scope of the Book 12
1.4.3 Content of the Book 12
References 14
2.1 Introduction 21
2.2 The Structure of an Agent 21
2.2.1 Thinking Agent 23
2.2.2 Reactive Agent 26
2.2.3 Hybrid Agent 28
2.3 The Structure of a Multi‐Agent System 29
2.3.1 The Environment of a Multi‐Agent System 29
2.3.2 The Structure of a Multi‐Agent System 30
2.4 Modeling Methods of a Multi‐Agent System 34
2.4.1 The Behavior Model of a Multi‐Agent System 34
2.4.2 The Running Model of a Multi‐Agent System 35
2.5 The Communication and Interaction Model of a Multi‐Agent System 37
2.6 The Communication Protocol for a Multi‐Agent System 39
2.6.1 Communication Languages for an Agent 40
2.6.2 The Communication Ontology for an Agent 42
2.7 The Interaction Protocol for a Multi‐Agent System 43
2.7.1 Classification of Interaction Protocols 43
2.7.2 Description of Interaction Protocols 45
2.7.3 The Collaboration‐Based Interaction Protocol 47
2.7.4 The Negotiation‐Based Interaction Protocol 48
2.8 Conclusion 50
References 50
3.1 Introduction 55
3.2 Manufacturing Systems 56
3.2.1 Concept 56
3.2.2 Classification 57
3.3 Production Planning and Control 61
3.3.1 Production Planning and Control Activities 61
3.3.2 Production Planning and Control Mode 64
3.3.3 Production Planning and Control Systems 66
3.3.4 Hybrid Push‐Pull Production Planning and Control System 68
3.4 Multi‐Agent‐Based Push‐Pull Production Planning and Control System (MAP4CS) 71
3.4.1 Mapping Methods 72
3.4.2 Functions of a Hybrid Push‐Pull Production Planning and Control System 73
3.4.3 Structures of a MAP4CS 77
3.4.4 The Running Model of a MAP4CS 80
3.4.5 Behavior Models of a MAP4CS 82
3.4.6 The Interactive Model of a MAP4CS 85
3.5 Conclusion 90
References 91
4.1 Introduction 95
4.2 Production Planning for Distributed Manufacturing Systems 96
4.2.1 Distributed Manufacturing Systems 96
4.2.2 Features of Distributed Manufacturing Systems 99
4.2.3 Production Planning Methods for Distributed Manufacturing Systems 102
4.3 Multi‐Agent‐Based Production Planning in Distributed Manufacturing Systems 106
4.3.1 A Production Planning Model for Distributed Manufacturing Systems 107
4.3.2 Production Planning in MASs 112
4.3.3 The Running Model of a Multi‐Agent‐Based Production Planning System 116
4.4 Agents in Multi‐Agent Production Planning Systems 118
4.4.1 Order Demand Management Agent 118
4.4.2 Cooperative Planning Agent 120
4.4.3 Critical Resource Capacity Management Agent 121
4.5 Contract Net Protocol‐Based Production Planning Optimization Method 123
4.5.1 Contract Net Protocol 123
4.5.2 Contract Net Protocol‐Based Collaborative Production Planning Algorithm 126
4.5.3 Case Study 130
4.6 Bid Auction Protocol‐Based Production Planning Optimization Method 133
4.6.1 Bid Auction Protocol 134
4.6.2 The Bid Auction Protocol‐Based Negotiating Production Planning Algorithm 135
4.6.3 Case Study 138
4.7 Conclusion 139
References 140
5.1 Introduction 143
5.2 Production Scheduling in Job Shop Manufacturing Systems 144
5.2.1 Job Shop Manufacturing Systems 144
5.2.2 Production Scheduling in Job Shop Manufacturing Systems 146
5.2.3 The Related Literature Review 148
5.3 Multi‐Agent Double Feedback–Based Production Scheduling in Job Shop Manufacturing Systems 153
5.3.1 Principles of Double Feedback Scheduling Strategy 153
5.3.2 The Architecture of the Multi‐Agent Double Feedback–Based Production Scheduling System 154
5.3.3 The Running Model for the Multi‐Agent Double Feedback–Based Production Scheduling 155
5.4 Agents in the Multi‐Agent Double Feedback–Based Scheduling System 158
5.4.1 Task Management Agent 159
5.4.2 Collaborative Scheduling Agent 160
5.4.3 Resource Capacity Management Agent 161
5.5 Positive Feedback–Based Production Scheduling in Job Shop Manufacturing Systems 162
5.5.1 Problem Description 163
5.5.2 Multi‐Agent Positive Feedback Scheduling System Based on Contract Net Protocol 167
5.5.3 Positive Feedback Production Scheduling Algorithm Based on the Hierarchical Genetic Algorithm 168
5.5.4 Case Study 174
5.6 Negative Feedback–Based Production Rescheduling in Job Shop Manufacturing Systems 177
5.6.1 Problem Description 177
5.6.2 Multi‐Agent Negative Feedback Rescheduling System Based on Ant Colony Auction Protocol 179
5.6.3 Ant Colony Algorithm–Based Negative Feedback Rescheduling Approach 181
5.6.4 Case Study 188
5.7 Conclusion 188
References 190
6.1 Introduction 197
6.2 Production Scheduling in Re‐Entrant Manufacturing Systems 198
6.2.1 Re‐Entrant Manufacturing Systems 198
6.2.2 Production Scheduling in Re‐Entrant Manufacturing Systems 201
6.2.3 The Related Literature Review 204
6.3 Multi‐Agent‐Based Hierarchical Adaptive Production Scheduling in Re‐Entrant Manufacturing Systems 208
6.3.1 Hierarchical Adaptive Production Scheduling Strategy 208
6.3.2 The Architecture of the Multi‐Agent Hierarchical Adaptive Production Scheduling System 210
6.3.3 The Running Model for a Multi‐Agent Hierarchical Adaptive Production Scheduling System 212
6.4 Agents in a Multi‐Agent Hierarchical Adaptive Production Scheduling System 212
6.4.1 Task Management Agent 214
6.4.2 Collaborative Scheduling Agent 215
6.4.3 Resource Capacity Management Agent 217
6.5 Hierarchical Production Scheduling in Re‐Entrant Manufacturing Systems 218
6.5.1 Problem Description 218
6.5.2 Contact Net Protocol based Production Scheduling in the System Layer 222
6.5.3 GPGP‐CN Protocol Based Production Scheduling in the Machine Layer 226
6.5.4 Case Study 238
6.6 Adaptive Rescheduling in Re‐Entrant Manufacturing Systems 244
6.6.1 Problem Description 244
6.6.2 Rescheduling Strategy 247
6.6.3 FNN‐Based Rescheduling 248
6.6.4 Case Study 253
6.7 Conclusion 253
References 258
7.1 Introduction 263
7.2 Multi‐Agent Production Control System 264
7.2.1 Requirements of Production Control Process 264
7.2.2 The Architecture of a Multi‐Agent Production Control System 265
7.2.3 The Running Model for Multi‐Agent Production Control Systems 268
7.3 Agents in Multi‐Agent Production Control Systems 271
7.3.1 Collaborative Task Management Agent 271
7.3.2 Machine Management Agent 273
7.3.3 Material Management Agent 274
7.3.4 Production Monitoring Agent 275
7.3.5 Warning Management Agent 276
7.3.6 Performance Analysis Agent 277
7.3.7 Quality Management Agent 278
7.3.8 Production Process Tracking and Tracing Agent 280
7.4 Technologies and Methods for Multi‐Agent Production Control Systems 283
7.4.1 XML‐Based Production Monitoring 283
7.4.2 Differential Manchester Encoding Rule‐Based Warning Management 284
7.4.3 Material Identification Technology for Production Process Tracking and Tracing 287
7.5 Conclusion 294
References 295
8.1 Introduction 297
8.2 RFID Technology 297
8.2.1 Development of RFID Technologies 297
8.2.2 RFID Technology Standard 301
8.3 Agent‐Based Material Data Acquisition System 306
8.3.1 Requirement Analysis of Material Data Acquisition 306
8.3.2 Multi‐Agent RFID‐Based Material Data Acquisition Structure 307
8.3.3 The Running Model of a Multi‐Agent Material Data Acquisition System 309
8.4 Agents in Multi‐Agent RFID‐Based Material Data Acquisition Systems 312
8.4.1 RFID Middleware Agent 312
8.4.2 RFID Reader Agent 322
8.4.3 RFID Tag Agent 322
8.5 Multi‐Agent RFID‐Based Material Data Acquisition Systems 326
8.5.1 Hardware and Configuration 326
8.5.2 Material Data Process and Publish 327
8.6 Conclusion 329
References 332
9.1 Introduction 333
9.2 Basics of OPC Technology 334
9.2.1 Development of OPC Technology 334
9.2.2 OPC Technology Overview 335
9.3 Agent‐Based Equipment Data Acquisition System 340
9.3.1 Requirement Analysis of Equipment Data Acquisition 340
9.3.2 The MAS Structure of the OPC‐Based Equipment Data Acquisition 341
9.3.3 The Running Model of the Equipment Data Acquisition MAS 345
9.4 Agents in the Multi‐Agent OPC‐Based Equipment Data Acquisition System 347
9.4.1 OPC Agent 347
9.4.2 OPC Server Agent 349
9.4.3 OPC Client Agent 352
9.5 Implementation of a Multi‐Agent OPC‐Based System 355
9.5.1 System Hardware and System Network Architecture 355
9.5.2 Data Integration Based on OPC Technology 356
9.6 Conclusion 361
References 361
10.1 Introduction 363
10.2 Architecture of a Prototype System 363
10.2.1 The Software Architecture 363
10.2.2 The Hardware Architecture 366
10.3 Agent Packages and Communication in a Prototype System 366
10.3.1 The Agent Package Method 368
10.3.2 The Communication Implementation Model of Agents 370
10.3.3 The Message Classification of Agents 372
10.3.4 Realization of the Communication Mechanism of Agents 374
10.4 The Manufacturing System Simulation in a Prototype System 375
10.4.1 The Manufacturing System Simulation 376
10.4.2 The Information Interaction Logic Architecture between the Prototype System and the Simulation Model 381
10.5 Software Implementation and Application of a Prototype System 383
10.5.1 Function Design of a Prototype System 383
10.5.2 The Running Process of a Prototype System 386
10.5.3 Production Planning in Distributed Manufacturing Systems 388
10.5.4 Production Scheduling in Job Shop Manufacturing Systems 390
10.5.5 Production Scheduling in Re‐Entrant Manufacturing Systems 392
10.5.6 Production Control in the Manufacturing Process 395
10.6 Conclusion 399
References 399
Index 401
Preface
With the rapid development of advanced manufacturing technology, manufacturing models have developed: from singlepiece 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 bookalso 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.
About this book
This book introduces methods and technologies of Agent‐based production planning, scheduling and control on the basis of Job Shop manufacturing systems and Re‐entrant manufacturing systems. It consists of eight aspects as follows:
1) Multi‐Agent‐based hybrid push‐pull production planning and control framework
2) Multi‐Agent‐based production planning in distributed manufacturing systems
3) Multi‐Agent‐based production scheduling in Job Shop manufacturing systems
4) Multi‐Agent‐based production scheduling in Re‐entrant manufacturing systems
5) Multi‐Agent‐based production control
6) Multi‐Agent‐based material data acquisition with RFID
7) Multi‐Agent‐based equipment data acquisition with OPC
8) Multi‐Agent‐based production planning and control prototype system
The purpose of this book is to track and trace the real‐time production data, and to make real‐time decisions in the production scheduling and control process.
The book is intended primarily for academic researchers in Agent‐based manufacturing, and industry managers willing to develop a new manufacturing management model. This book is also a textbook and reference book for graduates and last‐yearundergraduates in mechanical engineering, industrial engineering, management, automation, and computer engineering and so on.
Agent Technology in Modern Manufacturing:
1.1 Introduction
With the development of internet, computer, management, and manufacturing technologies, the manufacturing industry is undergoing a huge transformation from traditional manufacturing to agile manufacturing, networked manufacturing, virtual manufacturing, service‐based manufacturing, and cloud manufacturing. These new manufacturing systems are characterized by smartness, integration, and flexibility, and can be well described as Agent technology. The cooperation and communication of multiple agents can be adopted to improve the performance of manufacturing systems.
1.2 Agent and Multi‐Agent System
Research and application of Agent technology stem from a series of studies on distributed artificial intelligence conducted by MIT researchers in the 1970s.[1] Distributed artificial intelligence mainly focuses on solving distributed agent problems. There are two important branches:[2] distributed problems and Multi‐Agent Systems (MASs). The distributed problems were conducted at an early stage in the distributed artificial intelligence area. The distributed problems have been extended to Multi‐Agent Systems. The Multi‐Agent System is a system with Agents of different abilities to complete collaboratively certain tasks or achieve certain objectives.
