Multi-Agent-Based Production Planning and Control E-Book

Jie Zhang

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 479

Veröffentlichungsjahr: 2017

Beschreibung

At the crossroads of artificial intelligence, manufacturing engineering, operational research and industrial engineering and management, multi-agent based production planning and control is an intelligent and industrially crucial technology with increasing importance. This book provides a complete overview of multi-agent based methods for today's competitive manufacturing environment, including the Job Shop Manufacturing and Re-entrant Manufacturing processes. In addition to the basic control and scheduling systems, the author also highlights advance research in numerical optimization methods and wireless sensor networks and their impact on intelligent production planning and control system operation. * Enables students, researchers and engineers to understand the fundamentals and theories of multi-agent based production planning and control * Written by an author with more than 20 years' experience in studying and formulating a complete theoretical system in production planning technologies * Fully illustrated throughout, the methods for production planning, scheduling and controlling are presented using experiments, numerical simulations and theoretical analysis Comprehensive and concise, Multi-Agent Based Production Planning and Control is aimed at the practicing engineer and graduate student in industrial engineering, operational research, and mechanical engineering. It is also a handy guide for advanced students in artificial intelligence and computer engineering.

Leseprobe

Table of Contents

Cover

Title Page

Preface

About this book

1 Agent Technology in Modern Manufacturing

1.1 Introduction

1.2 Agent and Multi‐Agent System

1.3 Agent Technologies in Manufacturing Systems

1.4 Book Organization

References

2 The Technical Foundation of a Multi‐Agent System

2.1 Introduction

2.2 The Structure of an Agent

2.3 The Structure of a Multi‐Agent System

2.4 Modeling Methods of a Multi‐Agent System

2.5 The Communication and Interaction Model of a Multi‐Agent System

2.6 The Communication Protocol for a Multi‐Agent System

2.7 The Interaction Protocol for a Multi‐Agent System

2.8 Conclusion

References

3 Multi‐Agent‐Based Production Planning and Control

3.1 Introduction

3.2 Manufacturing Systems

3.3 Production Planning and Control

3.4 Multi‐Agent‐Based Push‐Pull Production Planning and Control System (MAP4CS)

3.5 Conclusion

References

4 Multi‐Agent‐Based Production Planning for Distributed Manufacturing Systems

4.1 Introduction

4.2 Production Planning for Distributed Manufacturing Systems

4.3 Multi‐Agent‐Based Production Planning in Distributed Manufacturing Systems

4.4 Agents in Multi‐Agent Production Planning Systems

4.5 Contract Net Protocol‐Based Production Planning Optimization Method

4.6 Bid Auction Protocol‐Based Production Planning Optimization Method

4.7 Conclusion

References

5 Multi‐Agent‐Based Production Scheduling for Job Shop Manufacturing Systems

5.1 Introduction

5.2 Production Scheduling in Job Shop Manufacturing Systems

5.3 Multi‐Agent Double Feedback–Based Production Scheduling in Job Shop Manufacturing Systems

5.4 Agents in the Multi‐Agent Double Feedback–Based Scheduling System

5.5 Positive Feedback–Based Production Scheduling in Job Shop Manufacturing Systems

5.6 Negative Feedback–Based Production Rescheduling in Job Shop Manufacturing Systems

5.7 Conclusion

References

6 Multi‐Agent‐Based Production Scheduling in Re‐Entrant Manufacturing Systems

6.1 Introduction

6.2 Production Scheduling in Re‐Entrant Manufacturing Systems

6.3 Multi‐Agent‐Based Hierarchical Adaptive Production Scheduling in Re‐Entrant Manufacturing Systems

6.4 Agents in a Multi‐Agent Hierarchical Adaptive Production Scheduling System

6.5 Hierarchical Production Scheduling in Re‐Entrant Manufacturing Systems

6.6 Adaptive Rescheduling in Re‐Entrant Manufacturing Systems

6.7 Conclusion

References

7 Multi‐Agent‐Based Production Control

7.1 Introduction

7.2 Multi‐Agent Production Control System

7.3 Agents in Multi‐Agent Production Control Systems

7.3.1 Collaborative Task Management Agent

7.4 Technologies and Methods for Multi‐Agent Production Control Systems

7.5 Conclusion

References

8 Multi‐Agent‐Based Material Data Acquisition

8.1 Introduction

8.2 RFID Technology

8.3 Agent‐Based Material Data Acquisition System

8.4 Agents in Multi‐Agent RFID‐Based Material Data Acquisition Systems

8.5 Multi‐Agent RFID‐Based Material Data Acquisition Systems

8.6 Conclusion

References

9 Multi‐Agent‐Based Equipment Data Acquisition

9.1 Introduction

9.2 Basics of OPC Technology

9.3 Agent‐Based Equipment Data Acquisition System

9.4 Agents in the Multi‐Agent OPC‐Based Equipment Data Acquisition System

9.5 Implementation of a Multi‐Agent OPC‐Based System

9.6 Conclusion

References

10 The Prototype of a Multi‐Agent‐Based Production Planning and Control System

10.1 Introduction

10.2 Architecture of a Prototype System

10.3 Agent Packages and Communication in a Prototype System

10.4 The Manufacturing System Simulation in a Prototype System

10.5 Software Implementation and Application of a Prototype System

10.6 Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 04

Table 4‐1 Resource Information.

Table 4‐2 BOM of product 1.

Table 4‐3 Scenario parameters in production planning optimization.

Table 4‐4 Demand of Product 1.

Table 4‐5 Available production capacity.

Table 4‐6 Available Amount of Raw Material per Time Node.

Table 4‐7 Production Plan of the Contract Net Protocol‐Based Collaboration MAS in a Nondeterministic Environment.

Table 4‐8 production plan of the contract net protocol‐based collaboration MAS in a deterministic environment.

Table 4‐9 Production plan of the bid auction protocol‐based collaboration MAS in nondeterministic environment.

Table 4‐10 Production plan of the bid auction protocol‐based collaboration MAS in deterministic environment.

Chapter 05

Table 5‐1 A list of incoming tasks.

Table 5‐2 Unit operation processing time

(“/” without assembling, min).

Table 5‐3 Unit cost of products

($).

Table 5‐4 The available hours in each machine

(daily working hours, h).

Table 5‐5 The final positive feedback production scheduling results.

Table 5‐6 A list of new incoming tasks.

Table 5‐7 Results of the ant colony auction protocol based negative feedback rescheduling approach.

Chapter 06

Table 6‐1 Configuration of SFL.

Table 6‐2 Lot process stages.

Table 6‐3 Results.

Table 6‐4 Samples of FNN training and testing.

Table 6‐5 Output of FNN‐based rescheduling strategy.

Chapter 08

Table 8‐1 EPC encoding scheme.

Table 8‐2 RFID Operating Frequency Distribution.

[15]

Table 8‐3 RFID equipment basic parameters.

Table 8‐4 EPC‐96I Coding Allocation Scheme.

Chapter 09

Table 9.1 Methods supported by the OPC‐XML‐DA specification.

List of Illustrations

Chapter 01

Figure 1‐1 The content of this book.

Chapter 02

Figure 2‐1 The basic structure of an Agent.

Figure 2‐2 The basic structure of a thinking Agent.

Figure 2‐3 The basic structure of a reactive Agent.

Figure 2‐4 The hierarchical structure of a Multi‐Agent System.

Figure 2‐5 The federal structure of a Multi‐Agent System.

Figure 2‐6 The fully autonomous structure of a Multi‐Agent System.

Figure 2‐7 The use case diagram of accounts receivable and payment in the procurement procedure in the production management process.

Figure 2‐8 The sequence diagram of accounts receivable and payment of procurement procedure in the production management process.

Figure 2‐9 The hierarchical model of the communication and interaction process among Agents.

Chapter 03

Figure 3‐1 The material handling process of a Job Shop manufacturing system.

Figure 3‐2 The material handling process of a Flow Shop manufacturing system.

Figure 3‐3 The material handling process of a RMS.

Figure 3‐4 The push‐based production planning and control strategy.

Figure 3‐5 The pull‐based production planning and control strategy.

Figure 3‐6 A hybrid push‐pull production planning and control system.

Figure 3‐7 Management layers and contents of a production planning and control system.

Figure 3‐8 The structure of a MAP4CS.

Figure 3‐9 The running model of a MAP4CS.

Figure 3‐10 Use case diagram of a Multi‐Agent push production planning system.

Figure 3‐11 Use case diagram of a Multi‐Agent hybrid push‐pull production scheduling system.

Figure 3‐12 Use Case Diagram of a Multi‐Agent Pull Production Control System.

Figure 3‐13 Use case diagram of a Multi‐Agent fundamental data management system.

Figure 3‐14 Resource capacity information blackboard of an Agent.

Figure 3‐15 Resource capacity information.

Figure 3‐16 Unit product processing time information blackboard.

Figure 3‐17 Update resource agent information list.

Chapter 04

Figure 4‐1 A distributed manufacturing system.

Figure 4‐2 Traditional production planning process.

Figure 4‐3 The structure tree of product A.

Figure 4‐4 The structure of a Multi‐Agent production planning system for distributed manufacturing systems.

Figure 4‐5 The running model of a Multi‐Agent‐based production planning system.

Figure 4‐6 The use case diagram of an order/product demand management Agent.

Figure 4‐7 The management flow of the order/product demand management Agent.

Figure 4‐8 The mechanism of the order/product demand management Agent.

Figure 4‐9 The use case diagram of a cooperative planning Agent.

Figure 4‐10 The mechanism of a cooperative planning Agent.

Figure 4‐11 The use case diagram of a critical resource capacity management Agent.

Figure 4‐12 The mechanism of a critical resource capacity management Agent.

Figure 4‐13 The procedure for implementing the contract net protocol.

Figure 4‐14 Structure of the contract net protocol‐based production planning MAS.

Figure 4‐15 The collaborative process of the contract net protocol‐based production planning MAS.

Figure 4‐16 Bid auction protocol‐based negotiation process in Multi‐Agent production planning systems.

Chapter 05

Figure 5‐1 The architecture of Multi‐agent double feedback based production scheduling in Job Shop manufacturing systems.

Figure 5‐2 Double feedback production scheduling in Job Shop manufacturing systems.

Figure 5‐3 The use case diagram of a Task Management Agent.

Figure 5‐4 The mechanism of the Task Management Agent.

Figure 5‐5 The use case diagram of a collaborative scheduling Agent.

Figure 5‐6 The mechanism of the collaborative scheduling Agent.

Figure 5‐7 The use case diagram of a resource capacity management Agent.

Figure 5‐8 Multi‐Agent Positive Feedback Scheduling System based on Contract Net Protocol.

Figure 5‐9 The Topology model of resources in the shop floor.

Figure 5‐10 The foraging behaviour of real ants.

Figure 5‐11 The negotiation model among Agents in a Multi‐Agent negative feedback rescheduling system for Job Shop manufacturing systems.

Chapter 06

Figure 6‐1 Front‐end processing of semiconductor manufacturing.

Figure 6‐2 The architecture of Multi‐Agent hierarchical adaptive production scheduling in re‐entrant manufacturing systems.

Figure 6‐3 Hierarchical adaptive production scheduling in re‐entrant manufacturing systems.

Figure 6‐4 The use case diagram of a task management agent.

Figure 6‐5 Structure of task management agent.

Figure 6‐6 The use case diagram of a Collaborative Scheduling Agent.

Figure 6‐7 Mechanism of the Collaborative Scheduling Agent.

Figure 6‐8 The use case diagram of a resource capacity management Agent.

Figure 6‐9 Contact net protocol based collaborative scheduling.

Figure 6‐10 Network flow.

Figure 6‐11 GPGP‐CN based SPM pool Collaborative Scheduling.

Figure 6‐12 Local task view of each SPM Resource Capacity Management Agent.

Figure 6‐13 Non‐Local Task View of each SPM Resource Capacity Management Agent.

Figure 6‐14 GPGP‐CN based BPM group Collaborative Scheduling.

Figure 6‐15 Local task view of each BPM Resource Capacity Management Agent.

Figure 6‐16 Non‐local task view of each BPM Resource Capacity Management Agent.

Figure 6‐17 Result of Auction and Bid. Dual Difference Rate w=|(Feasible Cost—Low Bound Cost)/Low Bound Cost.

Figure 6‐18 Scheduling cost of wafer fabrication (per day).

Figure 6‐19 FNN Structure.

Figure 6‐20 FNN learning process.

Figure 6‐21 Linear regression analysis of FNN’s output.

Chapter 07

Figure 7‐1 The architecture of Multi‐Agent production control system.

Figure 7‐2 Production task allocation in Multi‐Agent production control systems.

Figure 7‐3 Production process management in Multi‐Agent Production Control Systems.

Figure 7‐4 The use case diagram of a collaborative task management Agent.

Figure 7‐5 The mechanism of the collaborative task management Agent.

Figure 7‐6 The use case diagram of a machine management Agent.

Figure 7‐7 The use case diagram of a material management Agent.

Figure 7‐8 The use case diagram of a production monitoring Agent.

Figure 7‐9 The use case diagram of a warning management Agent.

Figure 7‐10 Workflow of abnormal warnings.

Figure 7‐11 The use case diagram of a performance analysis Agent.

Figure 7‐12 Statistical workflow of production process data.

Figure 7‐13 The Use case diagram of a quality management Agent.

Figure 7‐14 The mechanism of the quality management Agent.

Figure 7‐15 The use case diagram of a production process tracking and tracing Agent.

Figure 7‐16 The mechanism of a production process tracking and tracing Agent.

Figure 7‐17 Workflow of real‐time data visualization.

Figure 7‐18 Manchester encoding rule.

Figure 7‐19 Status of differential Manchester encoding rule.

Figure 7‐20 Material loss prevention algorithm.

Figure 7‐21 Tracking information.

Figure 7‐22 The history information inquiry algorithm in the production process.

Figure 7‐23 Inquire about production data and issue backtracking production orders.

Figure 7‐24 Interaction among Agents in the order management process.

Chapter 08

Figure 8‐1 RFID

Figure 8‐2 Multi‐Agent‐based material data acquisition system.

Figure 8‐3 Data processing procedure.

Figure 8‐4 Agent installation and registration.

Figure 8‐5 Information processing flowchart.

Figure 8‐6 The use case diagram of an RFID Middleware Agent.

Figure 8‐7 The Structure of an RFID Middleware Agent.

Figure 8‐8 The reader interface of an RFID middleware Agent.

Figure 8‐9 Algorithm of an event processing module.

Figure 8‐10 Flowchart of the smooth filter module.

Figure 8‐11 Flowchart of the data selection module.

Figure 8‐12 Tag encoding and data analysis.

Figure 8‐13 Flowchart of an RFID middleware Agent.

Figure 8‐14 The use case diagram of an RFID Reader Agent.

Figure 8‐15 The use case diagram of an RFID Tag Agent.

Figure 8‐16 RFID tag Information.

Figure 8‐17 Interface of Hardware Connection and Data Acquisition in an RFID Middleware Agent.

Figure 8‐18 Interface of Real‐Time Processing and Publishing in an RFID Middleware Agent.

Figure 8‐19 Interface of Real‐Time Production Tracking.

Chapter 09

Figure 9‐1 Traditional data integration methods.

Figure 9‐2 OPC‐Based Data Integration Solutions.

Figure 9‐3 The Data Acquisition Based on the OPC Technology.

Figure 9‐4 The Data Acquisition MAS Structure Based on OPC Technology.

Figure 9‐5 The Structure of the Manufacturing Information Integration Network.

Figure 9‐6 The OPC client Agent Creation.

Figure 9‐7 The Equipment Data Acquisition Flow.

Figure 9‐8 The use case diagram of an OPC Agent.

Figure 9‐9 The structure of an OPC Agent.

Figure 9‐10 The use case diagram of an OPC server Agent.

Figure 9‐11 OPC server Agent structure.

Figure 9‐12 The Information flow of an OPC Server Agent.

Figure 9‐13 The use case diagram of an OPC Client Agent.

Figure 9‐14 Structure of OPC client Agent.

Figure 9‐15 The communication timing diagram between an OPC client Agent and an OPC server Agent.

Figure 9‐16 System hardware network architecture.

Figure 9‐17 System network configuration.

Figure 9‐18 Add data to PC Access.

Figure 9‐19 Information process of data acquisition

Figure 9‐20 OPC Agent configuration interface.

Figure 9‐21 Real‐time data acquisition in the remote devices.

Chapter 10

Figure 10‐1 The software architecture of the prototype system.

Figure 10‐2 The hardware architecture of the prototype system.

Figure 10‐3 The communication model for Agents.

Figure 10‐4 The collaborative relationships between Agents in production planning and control.

Figure 10‐5 The basic principle of simulation.

Figure 10‐6 A simulation modeling instance of Re‐entrant manufacturing systems.

Figure 10‐7 The logic architecture of a manufacturing control system.

Figure 10‐8 The SOAP‐based interaction model for Web service.

Figure 10‐9 Basic functional modules.

Figure 10‐10 The running process of a prototype system.

Figure 10‐11 Production planning in the Multi‐Agent production planning system based on a collaborative protocol and a negotiation protocol.

Figure 10‐12 The positive feedback production scheduling process in Job Shop manufacturing systems.

Figure 10‐13 The negative feedback production rescheduling process in Job Shop manufacturing systems.

Figure 10‐14 The system‐layer global collaborative control module based on Combinatorial auction.

Figure 10‐15 Results of the ETAEMS/GPGP‐CN‐based collaborative dynamic control module at the machine layer.

Figure 10‐16 Rescheduling results of the Multi‐Agent production scheduling system.

Figure 10‐17 Multi‐Agent production control system.

Guide

Cover

Table of Contents

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Multi‐Agent‐Based Production Planning and Control

Jie Zhang

Shanghai Jiao Tong UniversityChina

This edition first published 2017 by John Wiley & Sons Singapore Pte. Ltd under exclusive licence granted by National Defense Industry Press for all media and languages (excluding simplified and traditional Chinese) throughout the world (excluding Mainland China), and with non‐exclusive license for electronic versions in Mainland China.

© 2017 National Defense Industry Press

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The right of Jie Zhang to be identified as the author of this work has been asserted in accordance with law.

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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–dc23LC record available at https://lccn.loc.gov/2016044467

Cover image: © enjoynz/GettyimagesCover 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 ZhangDecember 2015

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:

Multi‐Agent‐based hybrid push‐pull production planning and control framework

Multi‐Agent‐based production planning in distributed manufacturing systems

Multi‐Agent‐based production scheduling in Job Shop manufacturing systems

Multi‐Agent‐based production scheduling in Re‐entrant manufacturing systems

Multi‐Agent‐based production control

Multi‐Agent‐based material data acquisition with RFID

Multi‐Agent‐based equipment data acquisition with OPC

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‐year undergraduates in mechanical engineering, industrial engineering, management, automation, and computer engineering and so on.

1Agent 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.[3–5]

1.2.1 Agent

The concepts, properties, and research methods of Agent technology are developed from artificial intelligence. It is difficult to define either artificial intelligence or Agent. Many different definitions have been given by different schools for different requirements. The earliest concept of Agent was defined based on the concurrent actor model proposed by Hewitt in the early 1970s.[6] In the concurrent actor model, Hewitt defined a term—actor with the characteristics of self‐organization, interaction, and parallel execution. The most classic and widely accepted definition was given by Wooldridge, et al.[7] The definition contains “weak definition” and “strong definition”. The weak definition defines an Agent as a hardware and software system with autonomous ability, social skill, and responsive and predictive ability; the strong definition includes the properties of the weak definition and also the properties of knowledge, mobility, veracity, rationality, and so on.

Computer science researchers[8] consider that an Agent is a computer system based on software and hardware; it also has autonomy reactivity, socialability, proactiveness, and other properties. From the perspective of the evolution of software design methods, agent‐based software engineering methods are proposed on the basis of object‐oriented software engineering methods. Moreover, decomposition and abstraction methods of complex software systems, distributed computing capabilities, interactive coordination mechanism, calculation model, and software architecture have been proposed.

Researchers in artificial intelligence are more inclined to a narrow point of view, except for the above properties. It is therefore necessary to give a more specific meaning for an Agent. Terms such as belief, intention, and commitment are used to describe an Agent. An Agent tries to mimic a human’s thinking and intelligent behavior: for example, what the Agent is doing, what the Agent knows, what the Agent wants, and so on. This definition is developed on the basis of AI knowledge symbols. Shoham[9] thought an Agent was a symbolic reasoning system, which contained the expression of symbols on environment and expected behavior.

Therefore, an Agent is an intelligent individual. Wooldridge and Jennings[7] proposed that an Agent should have four basic attributes: autonomy, reactivity, social ability, and initiative. Sargent[10] considered that the most basic attributes of an Agent were reactivity, autonomy, goal‐orientation, and environmental resistance. An Agent was defined by Muller[11] as follows: 1) it is necessary to have other Agents and a virtual world where an Agent exists; 2) an Agent can perceive a virtual world and influence the virtual world; 3) an Agent can at least partly represent the virtual world; 4) an Agent is target‐oriented and has the ability to arrange its own activities; 5) an Agent can communicate with other Agents. Most researchers think that an Agent should not only meet basic properties, but should also have other properties according to application requirements: for example, mobility, learning and adaptability, interactivity, planning ability, rationality, persistent or time continuity, and so on. Three directions of current research are intelligence, agency, and mobility.[12] From the intelligence point of view, an Agent is an expert system; agency means that an Agent can be used to represent the role of a man and machine; while mobility means that an Agent can move or run on a different machine on the internet.

As the previous presentation demonstrates, an Agent should have the following properties:[13–21]

Autonomy:

An Agent can control its behavior and internal state by itself, and it cannot be controlled by others. This is used to differentiate an Agent with other concepts such as process and object.

Reactivity:

An Agent can feel the environment and respond appropriately to environment‐related events.

Sociality:

An Agent is in a social environment constituted by multiple Agents. These Agents exchange information with each other in some interactive methods. These Agents collaborate with each other to solve different problems and help other Agents complete related activities. Agents exchange information by a communication language.

Initiative:

The reaction of an Agent to the environment is a goal‐directed initiative behavior. In some cases, the behavior of the Agent is triggered by its own requirements. The reactive behavior is a kind of positive behavior or an active communication with the environment.

Adaptability:

An Agent can respond to environmental changes, adopt a goal‐oriented action at the appropriate time, and learn from its own experience, the environment, and the interaction process with other Agents.

Interoperability:

An Agent can work with other Agents to complete complex tasks, which is a social behavior.

Learning ability:

An Agent can learn from the surrounding environment and cooperative experiences so as to improve its own capability.

Evolutionary development:

An Agent can improve itself through learning, and reproduce and follow Darwin's natural selection rule “survival of the fittest”.

Honesty:

An Agent does not intend to deceive users.

Rationality:

the action taken by an Agent and its consequences will not harm its own interest and other Agents’ interests.

Persistence:

An Agent is ongoing, not temporary, its status should be consistent, which is not in contradiction with property (8).

Mobility:

An Agent should have the ability to move independently in the network, while its status remains unchanged.

Reasoning:

An Agent can reason and forecast in a rational manner according to accumulated past knowledge, states of the current environment, and other Agents.

Others

: philanthropic, adventurous or conservative, helpful or hostile, and so on.

The above attributes show that an Agent is similar to a person, which provides a new method for solving complex problems in computer science and artificial intelligence. Although an Agent may have a variety of properties, researchers and developers do not need to develop one Agent or an Agent system with all the attributes. Agents with several attributes and Multi‐Agent systems with several attributes are developed according to actual requirements.

1.2.2 Multi‐Agent System

Agent Systems can be classified into two classes: Single‐Agent Systems and Multi‐Agent Systems (MASs). The research of a Single‐Agent System focuses on simulating human intelligent behavior; it concentrates on investigating human intelligent behavior such as computing ability, reasoning ability, memory, learning ability and intuition, and so on. The research of a MAS focuses on the collaborative process among autonomous intelligent Agents that generate their corresponding behaviors or solve problems by coordinating Agent goals and planning Agents. In the problem‐solving process, these Agents share all their knowledge about related problems and methods in order to achieve a global objective, or their own local objectives.[22–25]

As regards a MAS, a computing system aims to complete collaboratively certain tasks or achieve objectives by some Agents. The system consists of multiple autonomous or semi‐autonomous Agents.[26] In a MAS, each Agent cooperates with other Agents to complete a complex task that cannot be solved by single Agent. All the Agents are autonomous, running in a distributed mode, or even heterogeneous. The subroutine, function, or process of each Agent are different, its goal and behavior are relatively autonomous and independent. Each Agent cooperates with other Agents in order to deal with conflict among them. A MAS has advantages of traditional distribution concurrent problem, and it runs in an interactive communication mode. Compared with a single Agent, each Agent in a MAS has incomplete information and it is able to solve problems, the data is dispersed or distributed, and the computing process is asynchronous, concurrent, or parallel. A MAS is very suitable to express an environment with a variety of methods and entities. A MAS has the following features:

Sociality

. In a MAS, an Agent may be in a social environment constituted by several Agents and may have the information and knowledge from other Agents. Agents communicate with each other by using a special language to complete the cooperation and negotiation activities. For example, in an Agent‐based manufacturing process production management system, Multi‐Agents representing the roles of customers, sales, production management, material procurement and quality inspection departments cooperate together to complete production tasks.

Autonomy

. In a MAS, when an Agent sends out a service request, other Agents that have this service ability and interest will accept it. One Agent cannot force another Agent to provide service.

Cooperation

. In a MAS system, Agents with different objectives work collaboratively to solve the problem through mutual cooperation and negotiation. The coordination process consists of resource sharing coordination, the producer/consumer relationship collaboration, tasks/subtasks relationship collaboration.

The Multi‐Agent System theory is developed on the basis of the Single‐Agent model and structure, which focuses on investigating interoperability, consultation and cooperation among Agents on the basis of the Single‐Agent theory. The consultation and collaboration activities in a MAS are realized based on social organization theory and modeling and implementing theory. The social organization theory provides a society‐oriented conceptual model about integration, interaction, communication and collaboration; while the modeling and implementing theory is used to eliminate the gap between the society‐oriented conceptual model and the reality. Therefore, the process to develop a MAS consists of the following aspects:[27–30]

Agent model

. An Agent model is developed in order to meet the requirements of individual autonomy, group interaction and the environment. The organizational structure, knowledge composition and operation mechanism of an Agent are described in a certain level of abstraction.

MAS architecture

. The asynchronous, consistency, autonomy and self‐adaptive features of an Agent are affected by selecting an architecture. This will determine information transmission channels and transmission ways for a single Agent internal intelligence collaborative behavior.

Interaction and communication

. The interaction activity is a basic requirement for multiple Agents to collaborate with each other. The communication activity is the basis for the interaction activity. The communication activity includes two aspects: the first is to construct the underlying communication mechanism, and the second is to construct or select an Agent communication language.

Consistency and collaboration

. Consistency describes the overall features of distributed artificial intelligence systems. Collaboration expresses the behavior and interaction patterns among agents. Good collaboration is important to achieve the stability and consistency of the system’s overall behavior. An efficient MAS should trend toward the overall consistency quickly through less learning.

MAS planning

. MAS planning activity is a kind of adaptation planning activity, which reflects the continuous changing process of the environment.

[31]

Conflict management

. Conflict in the collaborative process is very common. Conflict can be classified into three classes: resource conflict, objective conflict, and result conflict.

[32]

1.3 Agent Technologies in Manufacturing Systems

The development of Agent technology borrows many ideas and technological achievements from computer science, sociology, organizational science, economics and ecology and other disciplines, which has obvious advantages in many ways: extensive adaptability for real‐world applications, simplicity of design and good systematicness and others.[33, 34] Agent technology has been widely applied in manufacturing, communications, air traffic control, traffic management, information management systems, business process management, remote diagnostics and education and entertainment and many other fields,[35–39] which had achieved remarkable results. This section focuses on introducing Agent technology application in modern manufacturing.

1.3.1 Contemporary Manufacturing Systems

The third industrial revolution through the development and usage of computer technologies is affecting the manufacturing industry in a serious way. From the early 1970s, several influencing and representative manufacturing modes and relevant technologies had been presented, for example, the Toyota JIT system in 1980s, the Agile manufacturing system and the networked manufacturing system that were famous in 1990s. These advanced manufacturing modes reflect changes in different periods of external demand, manufacturing models, and related technical support, and reflect the evolution process: “information integration – process integration – inter‐enterprise integration”. In this section, we will introduce the current situation and trends of the manufacturing industry, present a literature review concerning production planning and control technologies, and analyze problems and solutions.

Supported by the US Congress and the Defense Department of United States in 1991, the Iacocca institute and the other 13 companies prepared a “21st Century Manufacturing Enterprise Strategy” report, which proposed a new strategy – agile manufacturing to revitalize the manufacturing sector. As a new manufacturing model, agile manufacturing quickly got the recognition and support of US industry, government agencies and the community, and soon became a theoretical research and manufacturing practice hotspot.

Virtual enterprise is an application mode of agile manufacturing. A virtual enterprise is a temporary alliance that comes together to share skills or core competencies and resources in order to respond rapidly to business responsibilities. The whole cooperation is supported by computer networks. Most of the research for virtual manufacturing focuses on organizational management, cooperative partner selection, profit assignment problems, and so on.

From 1996–2000, funds became available to support the agile manufacturing program, for example, the national industrial information system program of United States, the virtual manufacturing network program of Russia and the United States, the European information technology development program, the Japanese smart manufacturing system plan. These research projects focus on investigating virtual enterprises that run related enabling technologies, commercial operation infrastructure, collaborative design, and information integration platforms. The Ecolead program formulated a cooperative organization network including 28 units in 15 countries.

1.3.2 Agents in Production Planning and Control Systems

A production planning and control system is the core and key technology of production management systems. An excellent production planning and control system is an important tool to improve the overall automation level of enterprises and provide significant economic benefits for enterprises. A production‐planning and control system can directly determine whether the manufacturer can complete specific tasks in accordance with the expected demand. Its core function is to manage production tasks and resource allocation and utilization in manufacturing systems, and to meet customers’ demands in the best possible way. A production planning and control system should contain the following processes: decomposing product tasks, analyzing resource demands, determining the operation sequence of a job, allocating machines, and monitoring real‐time task progress. Meanwhile, this system should be able to deal with sudden changes in the actual manufacturing environment, such as lack of material, random machine breakdown, order changeover and rush orders, and so on. Even though production planning and control problems are complex, Agent technology has been introduced in this field. A MAS has a certain adaptability in a dynamic environment; it can independently adjust the behavior of individuals in order to respond rapidly to sudden changes in manufacturing systems.

Parunak, et al.[40] developed a production planning and shop floor control system based on Agent technology. Shen et al.[41] studied the integrated modeling framework for business‐oriented mixed Agents. Lin and Solberg[42] proposed an autonomous Agent‐based integrated production planning and control framework. In their study, a general methodology that consists of a market‐based model, a job priority strategy, a multi‐stage negotiation technology were developed to adapt to changes in the manufacturing environment. Hadavi et al.[43] proposed a Multi‐Agent distributed dynamic planning, scheduling and control system. Applications of MASs to production planning and control problems were summarized by Maria and Sergio.[44] It was noted that its related research tended to be more diverse. Baker[45] studied a MAS‐based shop scheduling algorithm. Shaw[46] proposed Agent‐based production scheduling and control strategies. In his study, a manufacturing unit could subcontract its task as a subcontract to other manufacturing units by using a bidding mechanism. In the study of Wang et al.,[47] the Agent technology was used to solve real‐time distributed intelligent manufacturing control problems. Wiendahl et al.[48] studied a self‐organizing production control system based on Agents. Butler[49] proposed a Multi‐Agent system architecture to solve distributed dynamic scheduling problems. In his study, the scheduling process was divided into two levels: the first layer was used to assign manufacturing units to jobs by using an Agent‐based consultation mechanism, the second layer was adopted to allocate dynamically shared manufacturing resources. Shen and Norri[50] proposed a hybrid Agent system architecture to solve scheduling and rescheduling problems.

Many Chinese researchers have also proposed different MAS solutions for production planning and control systems. In the study by Zhang Jie, Li Penggen et al.,[51] virtual manufacturing cells were introduced to deal with production planning and control process. The system consisted of a shop floor layer, a virtual cell layer and a resource layer. On the basis of this study, some MAS‐based production planning and control solutions have been developed for solving Job Shop,[52–60] reentrant Manufacturing System,[61–66] agile manufacturing systems,[67–72] and other planning and control problems for complex manufacturing systems. Zhu Qiong, et al.[73] proposed a Multi‐Agent‐based collaborative negotiation mechanism for solving dynamic Job Shop scheduling problems. Zeng Bo, Yang Jianjun, et al.[74, 75] proposed a Job Shop scheduling and control system that hybridizes a MAS‐based Generalized Partial Global Planning (GPGP) mechanism with a Task Analysis, Environment Modeling and Simulation (TAEMS) language. Liao Qiang et al.[76] proposed a Multi‐Agent‐based Job Shop dynamic scheduling model by using field bus. Gao Guojun et al.[77] used Agent technology to developed a reconfigurable enterprise information system by using Agent technology. Liu Jinkun et al.[78] proposed an Agent‐based steel industrial production process control system.

1.3.3 The Existing Requirements

An Agent system can be used to coordinate production planning dynamically and control activities, adjust individual behavior, and respond rapidly to deal with product changes, machine breakdown, and other incidents. Therefore, Agent technology is suitable for the production planning and control process in manufacturing systems to make decisions. Agent‐based production planning and control methods and techniques are presented in detail in this book. A Multi‐Agent‐based hierarchical adaptive, intelligent, collaborative production planning and control system is developed to investigate some Multi‐Agent‐based planning, scheduling, and control problems. Moreover, a MAS‐based data acquisition technology is proposed for complex production processes in order to collect real‐time data and track real‐time production processes. The MAS‐based technology provides a set of optimal solutions and ideas for the production planning and control process in manufacturing systems.

1.4 Book Organization

1.4.1 Purpose of the Book

Production planning, scheduling, and control optimization have become urgent demands and a trend in modern complex manufacturing systems such as weapon manufacturing systems and semiconductor manufacturing systems. In this book, weapon manufacturing systems are characterized as small‐batch manufacturing systems, which are similar to typical Job Shop Manufacturing systems. The semiconductor manufacturing systems are characterized as typical re‐entrant manufacturing systems. The book will focus on developing a hybrid push‐pull production planning and control system architecture based on MASs to describe the characteristics of Job Shop and re‐entrant manufacturing systems. The overall system objectives are completed by communicating and collaborating amongst Agents to manage and make decisions so as to respond rapidly to internal and external changes in the manufacturing environment. The production planning and control process presented in this book consists of three layers: the production planning layer, the production scheduling layer, and the production control layer. In the production planning layer, MAS‐based production planning methods for distributed manufacturing systems are given. In the production scheduling layer, a Multi‐Agent double feedback strategy‐based scheduling method is developed for Job Shop Manufacturing systems, and a Multi‐Agent hierarchical adaptive production scheduling method is proposed for Re‐entrant manufacturing systems; in the production control layer, the radio frequency identification (RFID) technology and OLE for Process Control (OPC) technology and Multi‐Agent Systems are integrated, and a material and equipment data acquisition method is then designed for manufacturing systems. This method is able to collect heterogeneous device data and integrate information between heterogeneous networks. This method also provides a new way for tracking real‐time production processes in complex manufacturing systems and a foundation for real‐time production decision‐making in manufacturing systems.

1.4.2 Scope of the Book

This book systematically presents methods and technologies concerning Agent‐based production planning systems. The content of this book is illustrated in Figure 1‐1. Chapter 1 and Chapter 2 introduce advantages and applications of Agent technology. Chapter 3 presents requirements of production planning and control systems. An Agent‐based push‐pull production planning and control system is developed. Agent‐based production planning and control technologies for distributed production systems are introduced in Chapter 4 to Chapter 9. Chapter 4 proposes a Multi‐Agent contract net protocol and bid auction protocol based production planning approach for distributed production systems. Chapter 5 develops a Multi‐Agent double feedback–strategy based production scheduling method for Job Shop production systems. Chapter 6 proposes a Multi‐Agent hierarchical adaptive production scheduling architecture for Re‐entrant manufacturing systems. Chapter 7 presents a Multi‐Agent production control system. Chapters 8 and 9 present data acquisition technology based on RFID technology and OPC technology, and construct a Multi‐Agent material data acquisition system and a Multi‐Agent equipment data acquisition system. Chapter 10 presents the prototype of an Agent‐based production planning and control system.

Figure 1‐1 The content of this book.

1.4.3 Content of the Book

Chapter 1 introduces Agent technologies in modern manufacturing. A short review concerning Agent technology is given to provide the background for investigating production planning, scheduling, and control approaches.

Chapter 2 presents Agent technologies used in this book, which includes three aspects, that is the structure of an Agent and a Multi‐Agent System, the interaction model of a Multi‐Agent System, and communication protocols and interaction protocols of Multi‐Agent Systems.

Chapter 3 analyzes production planning and control activities and operation modes, and requirements of a production planning and control system. A hybrid Agent‐based push‐pull production planning and control system is developed.

Chapter 4 presents Agent‐based production planning methods for distributed manufacturing systems. First, the production planning process in distributed manufacturing systems is investigated. Second, a production planning model and a MAS structure are developed. Third, a contract net protocol–based MAS collaborative production planning method is proposed in which plants collaborate in a distributed manufacturing system with complete information sharing. Finally, a bidding auction protocol–based MAS collaborative production planning method is proposed where plants collaborate in a distributed manufacturing system with incomplete information sharing.

Chapter 5 presents Agent‐based production scheduling methods for Job Shop manufacturing systems according to the basic principles of push and pull modes. In particular, a multi‐Agent dual feedback–based production scheduling method is developed. Then, a hierarchical optimization theory–based positive feedback job scheduling method, and an ant colony negotiation mechanism–based negative feedback rescheduling method are proposed.

Chapter 6 develops a Multi‐Agent‐based hierarchical adaptive production scheduling architecture to describe the characteristics of re‐entrant manufacturing system. A combinatorial auction–based method is developed in the system layer; a GPGP‐CN based method is developed for hierarchical production scheduling processes in the machine layer; and a fuzzy neural network–based adaptive rescheduling method is developed for re‐entrant manufacturing systems.

Chapter 7 proposes a Multi‐Agent‐based production control method by analyzing requirements of production control activities. Several important business Agents and related methods in the production control process are presented.

Chapter 8 presents the basic concepts and function requirements of material data acquisition, and develops a Multi‐Agent RFID technology–based material data acquisition system.

Chapter 9 presents a Multi‐Agent OPC technology based equipment data acquisition system.

Chapter 10 presents both hardware architecture and software architecture of an Agent‐based production planning and control prototype system.

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