advanced technologies for e-manufacturing · a typical manufacturing system consists of a multitude...

CHAPTER 3 Advanced technologies for e-manufacturing C-Y. Huang1 & C. Pattinson2

1School of Computing and Mathematics, Manchester Metropolitan University, UK. 2School of Computing, Leeds Metropolitan University, UK. Abstract The development of Information Technology (IT) has led to increased global competition and rapidly changing customer requirements in the manufacturing environment. It is thus forcing major changes in the production styles and configuration of manufacturing organizations. Increasingly, traditional centralized manufacturing applications, such as manufacturing engineering, design, planning, scheduling, control, etc., are proving to be inflexible and unable to respond to changing production styles and highly dynamic variations in product requirements. Moreover, manufacturing enterprises are experiencing the problem of information overload. Therefore, new techniques and tools are required to achieve new co-operation and applications as well as to extract useful knowledge from the rapidly growing volumes of databases in manufacturing systems. Recently, agent technology has emerged as an important approach for developing distributed intelligent manufacturing systems. It provides a natural way of overcoming the limited expandability, flexibility and reconfiguration capabilities in the centralized paradigm, and to design and implement distributed manufacturing systems. In addition, data mining techniques are used to discover hidden knowledge, unexpected patterns and information from large databases in order to assist efficient decision-making for manufacturing enterprises. This article, firstly, provides a general view of a manufacturing system particularly in the e-environment. Then, the article gives an overview of agents

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and data mining – including their characteristics and applications in the e-manufacturing environment. 1 Introduction Since the concept of Computer Integrated Manufacturing (CIM) was developed in the late 1960’s, manufacturing enterprises have experienced a series of massive global changes. Moreover, the integration of a range of disparate Information Technology (IT) applications and the use of the Internet has dramatically changed the nature of the manufacturing enterprise environment. Production is now characterized by smaller volumes with wider variation; market changes are more rapid; products have shorter lead times; and companies rely on extensive outsourcing instead of internal production. Therefore, manufacturing enterprises are facing increasingly competitive pressures in maintaining their existing market share and shortening the product development lifecycle in order to respond to rapidly changing customer requirements. In addition, a huge amount of data is generated in such an environment, and it is believed that this will continue to grow rapidly [1]. Therefore, it is essential that designers and engineers consider the importance of accessing the desired information in real-time. The recent development of agent technology in the field of manufacturing systems has had a significant impact. Researchers have attempted to apply agent technology to various applications, with examples in Concurrent Engineering [2], Supply Chain Management (SCM) [3],[4], Manufacturing Planning, Scheduling and Control [5], and Holonic Manufacturing Systems (HMS) [6],[7]. The concept of data mining or Knowledge Discovery in Databases (KDD) is emerging as an important field of research. This concept involves a number of processes designed to acquire knowledge from massive datasets. As the knowledge needed to solve each task is usually unique and has its own specific requirements, computational techniques and tools are required to support the extraction of useful knowledge from the rapidly growing volumes of data. Several data mining algorithms are available, such as statistical methods, machine learning, neural networks etc., and these will be discussed later. In Section 2, we will provide a background analysis of the problem. This will include an overview of the e-manufacturing environment and the knowledge required in such an environment. Sections 3 and 4 will demonstrate how agent technology can be facilitated in the e-manufacturing environment. We will then discuss the nature of agent technology including its theories, architectures and applicable languages and finally its applications. In Section 5, we will give an overview of the emerging field of KDD and data mining, including the concepts of data mining, mining processes, and the exploration of data patterns and dependencies of required information, together with various data mining techniques and their applications in the e-manufacturing environment.


2 Industrial and technological drives to e-manufacturing 2.1 An overview of e-manufacturing systems A typical manufacturing system consists of a multitude of functions, including customer ordering and servicing, organizational planning and scheduling, manufacturing planning, scheduling, control and monitoring, engineering and design, and company account functions. Through the use of computer networking technology, manufacturing enterprises are able to form a virtual environment to leverage expertise outside company boundaries. One development in the field of virtual manufacturing systems, is the Virtual Collaborative Environment (VCE), which allows remotely located companies to form virtual partnerships with the goal of collaborative product development through secure webs [8]. This enables manufacturing enterprises to increase their productivity, flexibility and quality, and permits a reduction of design time and work in progress. The manufacturing enterprises of the 21st century will require a high degree of product customization to fulfil market demands; newer technologies will be continuously growing as global competitiveness increases. Therefore, the next generation of e-manufacturing systems needs to fulfil the following requirements:

• Open, dynamic and distributed environment: For effective integration and management across physical and logical distributed domains, the manufacturing system should be able to share the distributed manufacturing data and information via the Internet, Intranet and corporate networks. It should allow manufacturing enterprises to select and adapt different applications and tools easily with little additional integration cost.

• Heterogeneous interoperability: In such an environment, heterogeneous software and hardware applications from different vendors are used to operate together as integrated parts of the system. The manufacturing system will need to accommodate heterogeneous software and hardware resources as well as human resources.

• Enterprise integration and co-operation: Manufacturing enterprises will have to integrate related management systems (ordering, purchasing, design, product, scheduling and planning, manufacturing etc.), and co-operate with their customers, partners and suppliers from material supply, through part fabrication to the final product delivery. In particular, the integrated solution should support runtime dynamic co-ordination and co-operation in dealing with unexpected events.

• Agility: This is the ability to adapt quickly to a manufacturing environment of continuous and unanticipated change (e.g. by reducing the product cycle time, and responding to customers’ desires quickly). In order to achieve agility, manufacturing systems should be able to reconfigure and interact rapidly with heterogeneous systems.


• Scalability: Scalability means that additional resources can be added into the system as required without disrupting other previously established systems. This capability should be available at any working level in the system.

• Fault tolerance: The system should be able to detect system failures and recover from errors at any level to minimize their impact on the working environment.

2.2 Knowledge representations Knowledge usually contains a physical or abstract description of the problem domain. Depending on its context, knowledge can be commonly represented by the following four levels [9],[10]:

• Shallow knowledge: forms relevant information that can be easily retrieved from databases using a query tool such as Structured Query Language (SQL).

• Multi-dimensional knowledge: can be analysed by using online analytical processing tools (OLAP). OLAP tools provide the ability to explore many forms of clustering and different ordering of the data rapidly.

• Hidden knowledge: This type of knowledge must be explored using pattern recognition or machine-learning algorithms.

• Deep knowledge: The information stored in the database can only be located if the miner has a clue to its existence.

In the manufacturing environment, a wide spectrum of knowledge is required to operate a manufacturing system. It includes knowledge of constraints, objectives, procedures, rules and experience related to any relevant manufacturing activities, and organizational structures and techniques. According to Rembold et al. [11], much of the information about a manufacturing operation is contained in descriptive form and is hence difficult to represent by conventional arrays or sets of numbers. Therefore, special presentation mechanisms of knowledge are needed. Rembold et al. suggest that knowledge may be categorized as follows [11]:

• Factual knowledge: It is purely a value with fact. For example, 10+30=40.

• Heuristic knowledge derived from experience: This type of knowledge is normally presented as a set of rules. For example, IF <the workpiece is rotational> AND <has a length/diameter ratio of x> THEN <use a standard horizontal lathe>.

• Declarative knowledge: For example, all screw machines produce threads.

• Inferred knowledge: This is usually knowledge inferred from applying the first three types of knowledge.


• Meta-knowledge: This is the knowledge needed for processing the information contained in the knowledge bases.

3 Agent technology Agent technology has been applied to manufacturing systems for more than twenty years [12]. Recent developments in the use of multi-agent systems in the manufacturing environment have generated much interest and new research opportunities. Agents can be categorized into various types, such as software agents, mobile agents, reactive agents, cognitive agents, etc. [13],[14]. In this section, we will investigate how mobile agent technology can be used to facilitate manufacturing activities. Then we will go on to discuss the mobile agent, its nature, features and applications in detail. Finally, we will briefly review existing research agent-based applications, including, for each activity, its application domain and main characteristics. 3.1 Facilitations of agent technology Agent technology provides various functions and techniques to facilitate distributed manufacturing activities. Examples include:

• Representation: Agents can be used to represent physical manufacturing resources such as cells, machines, tools, and fixtures as well as products, and parts and operations to facilitate manufacturing resource planning, scheduling and execution control.

• Encapsulation: Agents can be used to encapsulate existing software systems in order to resolve legacy problems and integrate manufacturing activities such as design, planning, scheduling, simulation, and production distribution, with those of their suppliers, customers and partners to form a supply chain or virtual enterprise environment [15], [16].

• Co-ordination: Agents can be used to facilitate co-ordination between several manufacturing activities, for example overall manufacturing planning, scheduling and controlling.

• Integrated Services. Agent technology has been used to integrate various services and applications in manufacturing systems. For example, in the MetaMorph model [2], [17], special agents called mediators are designed for agents’ dynamic co-ordination. In the Holonic Manufacturing System (HMS) [6], holon agents are used to integrate the entire range of manufacturing activities from ordering through design, production and marketing, to realize the agility of the manufacturing enterprise. Jin and Zhou [18] propose KICAD (Knowledge Infrastructure for Collaborative and Agent-based Design), in which a network of intelligent agents is developed to facilitate collaborative engineering.


3.2 Characteristics of a mobile agent A mobile agent is a software program with the capability of moving from one machine to another automatically. Mobile agent systems may support different functionalities and can be implemented in various ways. However, they all possess a similar behaviour and philosophy. According to Bieszczad et al. [19], a mobile agent can be characterized by a life-cycle model, a navigation model, a communication model, a computational model or a security model. These will be discussed below. 3.2.1 Life-cycle Within an open and dynamic system, the availability of the agents and other resources is essential [20]. As the agents are not always directly available, the current state of agents will help to define realistic situations that agent mechanisms (e.g. co-ordination, collaboration) will have to deal with. In general, agents can be active, suspended, migrating or terminated. These aspects of agents are typically translated into an agent life-cycle. Several research projects have defined agent life-cycle models, such as Green et al. [21] and the Foundation for Intelligent Physical Agents (FIPA) [22]. Figure 1 depicts an agent life-cycle model proposed by FIPA, in which five distinct states of an agent with their respective actions are clearly defined.

W aiting Suspended

Active

Transit In itia ted

W ake Up

W ait

Suspend

Resum e

Execute

M ove

D estoryQ uit

C reate

U nknown

Invoke

Figure 1: An agent life-cycle model (source: FIPA [22]).

Similarly, Green et al. [21] propose two life-cycle models; namely, the persistent process based life-cycle, and the task based life-cycle. In the former, an agent possesses three states, which are the start, running and death states. When the agent moves, the running state is activated. Upon the agent’s arrival,


processing can continue until it reaches its death state. In the task based life-cycle, the agent begins in a start state and, depending on a set of conditions, moves through a set of tasks, each having its own state. In this model, the state of an agent will be lost when it moves, in which case a method is required for preserving its context. As multi-agent systems are becoming larger and more dynamic, the availability of resources has to be considered in order to build successful applications. A clear understanding of an agent life-cycle will help in the design and implementation of agent-based systems. 3.2.2 Communication Communication between agents is based on the concept of an association between two or more of them for the purpose of transmitting or exchanging information. An agent needs to communicate with other agents residing within the same domain (intra-domain) or with other agents residing across different domains (inter-domain) using explicit communication mechanisms provided by the underlying mobile agent system. An agent can invoke a method of another agent or send it a message if it is authorized to do so. Further details regarding the agents’ communication will be discussed below in Section 4.2. 3.2.3 Navigation A mobile agent should not be bound by the system in which it is initiated. It is often required to travel between multiple managed resources or computer nodes in order to perform its tasks. Navigation is a function which controls the movement of mobile agents between managed entities containing certain objects with which the agents wish to interact. Mobile agents are expected to have the intelligence to decide when to move as well as the underlying infrastructure to support and execute any request. Nevertheless, it is still challenging to determine the optimal itinerary strategy at the time the agent is designed or instantiated, especially in a dynamic environment. In Section 4.4, we will discuss further the travelling itinerary of mobile agents. 3.2.4 Computation Computation refers to the agent’s capabilities of data manipulation and thread control primitives. This feature can help a human operator filter a vast amount of data. The retrieved data can be compressed and processed to produce high-level usable information. In order to address a large number of requests, the calculation can be distributed to several managed machines. Instead of sending a large amount of raw data through the network, this characteristic eliminates the necessity for heavy network traffic by sending only relevant, high-level information. 3.2.5 Security As a mobile agent is built in a software programming language, security has always been considered the weak point in mobile agent technology [23], [24], [25]. A host may receive a potentially malicious agent. Conversely, an agent


may be transmitted to a potentially malicious host. Closely related to this are the legal aspects of their authentication, verification and authorization of mobile agents, as well as secrecy, privacy, data quality, and other similar matters. Questions such as “Is the agent really what it presents to be?” or “Does it have the right to access certain data?” should have a clear answer, or the host will not know how to react to the agent’s requests. In fact, the necessity for security is recognized as critical to the acceptability of distributed systems based on mobile agents [23], [25]. 3.2.6 Agent languages Since mobile agents are programmable, each agent basically comprises the following components: a code component (the program itself), a data component (the structure used by the program), and a state component (the state of execution of the process). Theoretically, agents must be programmed in a language that is machine-independent and widely available. Popular agent languages include [24], [14]:

• LanguageNoteSpecified • Interpretive scripting language • Java • Tcl • Scheme • Perl • KQML • Smalltalk, Lisp, Telescript etc.

3.2.7 Agent execution environments The implementation of mobile agents must be supported by an integrated platform that provides the mechanisms and functionalities necessary for their proper execution and activities in distributed environments. These are related to support for the execution, management, mobility, security, communication, transaction, and authentication of agents. In general, a mobile agent platform should be in a very flexible programming environment which can be tailored to the individual user’s needs. Currently available platforms for the implementation of mobile agents in the field of manufacturing integration systems include:

• Agent Building Shell (ABS), developed at the University of Toronto for creating co-operative enterprise agents in the integration of a manufacturing enterprise supply chain [16];

• ObjectSpace, which together with AMD (Advanced Micro Devices) created an Agent-Enhanced Manufacturing System (AEMS) using VoyagerTM [26];

• Other general agent development tools include: - IBM’s Aglets SDK [27][28];


- Agent Building Environment (ABM) and General Magic’s OdysseyTM

[29]. 4 Multi-agent systems Multi-agent modelling has emerged as a promising paradigm for dealing with the decision-making or problem solving processes in distributed information system applications [30][31]. In a Multi-Agent System (MAS), several agents co-ordinate their capabilities and knowledge to reason about the processes of co-ordination. Co-operation and co-ordination between agents is probably the most important feature of MAS [32]. A number of research groups are working on the standardization of dynamic collaborative multi-agent systems including the Foundation for Intelligent Physical Agents (FIPA), the Object Management Group (OMG), the Knowledgeable Agent-oriented System (KAoS) and the General Magic Group [33]. In this section, we will look at the basic characteristics of a multi-agent system. We will then discuss agents’ communication and collaboration, and analyze the overall environment in which agents can operate effectively and interact with each other productively. This will include the infrastructure for each interaction to take place and protocols for agents to communicate and interact with each other. 4.1 Characteristics of multi-agent systems In a MAS, an agent may represent, to a varying extent, three important general characteristics: autonomy, adaptation, and co-operation [32][34]. Autonomy means that agents should have their own goals and should exhibit goal-directed behaviour. They are not simply reactive, but pro-active and show initiative. Moreover, agents are capable of adapting themselves to a dynamic environment, which includes other agents and human users. In such an environment, agents can learn experience from others and adapt themselves within the environment. In an MAS, several agents can facilitate collaboration by sharing information, knowledge, and tasks with each other in order to achieve common goals. Therefore, agents should be able to understand each other and to communicate with each other effectively. In the area of manufacturing, the adoption of an MAS brings several advantages and characteristics, which are illustrated below:

• To address manufacturing problems which are too large and complex for a centralized single agent due to concerns about resource limitations and robustness. It involves the ability to recover resources or equipment from fault conditions or unexpected events.


• To allow the interconnection and interoperation of multiple existing legacy systems, e.g. ordering, purchasing, engineering design, manufacturing planning, scheduling and control systems etc.

• To provide improved scalability – the organizational structure of the agent can dynamically change in order to reflect the dynamic environment – i.e. as the scope of the business environment grows in size, the agent-based architecture can re-structure itself through agents modifying the roles, beliefs and actions that they perform.

• To provide solutions to inherent distribution problems, e.g. supply chain management, enterprise resource planning and workflow management.

• To provide solutions that utilize distributed resources (e.g. information source, expertise, outsourcing resources).

4.2 Agent communication Agent communication is one of the most important features of a multi-agent system. It allows agents to interact recurrently to share information, knowledge and tasks to achieve their goals. The infrastructure that supports agents’ communication and co-operation include the following key components: communication mode; communication language and protocol; common format for the content of communication and a shared ontology. With a common communication paradigm, it is expected that agents can communicate with each other in the same manner, in the same syntax, and with the same understanding of the world. 4.2.1 Communication modes Agent communication can be implemented in various ways, depending on the nature of the agent, the architecture of the system and the number of agents involved. In general, agent communication can be presented in different modes, which are described below [14][28]:

• Direct and indirect communication. Mobile agent communication can either be direct, by passing messages, or indirect by exchanging information via a shared data repository.

• Synchronous and asynchronous communication. Communication between agents can be synchronous or asynchronous. In the synchronous mode, agents can work together at the same time, either in the same location or at different sites. In asynchronous mode, agents work at different times without the limitations of their locations.

• Single or multiple agent communication. In agent-based systems, the strategy of communication sometimes depends on the relationship between the sender and the number of receivers involved. There are a number of possibilities: - Point-to-point. One agent sends a message to another specific agent.


- Broadcast. One agent sends out a message to all of the other agents in the system. More details regarding this technique will be discussed later.

- Multicast. One agent sends a message to a selected group of agents. Of the above, the ability to allow a single agent to dispatch a message to a selected group of agents is particularly useful in a multi-agent system. 4.2.2 A common paradigm for communication A communication paradigm consists of a number of components including communication language and protocol, common format for the content of communication and a shared ontology. The relevant issues are discussed below. 4.2.2.1 Types of communication language Genesereth [34] discusses the approaches to designing an agent communication language, which are procedural and declarative. In the procedural approach, communication is based on executable content and this can be implemented by programming languages like Java and Tcl. In the declarative mode, communication is based on a series of declarative statements, such as definitions, assumptions, etc. Declarative languages have been preferred for the design of agent communication language, as executable content is difficult to control, co-ordinate and merge [33]. Currently, research on a common language for agents provides a high degree of interoperability between different architectures. Components of a common language format are explained below. 4.2.2.2 Speech act theory Many Agent Communication Languages (ACLs), like KQML, are based on speech act theory. The concept of speech act theory is primarily based on natural language utterance and the form of spoken human communication to model the communication between computational agents. There are three types of action associated with an utterance [36], which are Locution, Illocution and Perlocution. Most declarative languages are based on illocution acts. A subset of actions, formally called performatives, like REQUEST, REPLY, INFORM, ADVERTISE, TELL, etc., are commonly used. Relevant research regarding the speech acts theory is addressed in Peng et al. [35], Huhns and Stephens [37] and Shen et al. [14]. 4.2.2.3 KQML KQML is a message-based protocol used for exchanging information and knowledge. It supports run-time knowledge sharing among agents. Flores-Mendez [33] and Peng et al. [35] discuss the structure of KQML, which consists of three layers: a communication, a message, and a content layer. The communication layer describes the lower level communication parameters, such as the identity of the sender and recipient, and a unique identifier associated with the communication. The message layer contains the message, including its intention by a chosen performative, and indicates its content language and protocol, and ontology. The content layer contains


information with regards to the performative submitted. Languages used in KQML include KIF, PROLOG, LISP, SQL or other defined agent communication languages. A template of the KQML structure is depicted in Figure 2. (KQML-performative :from <word> :to <word> :sender <word> :receiver <word> :in-reply-to <word> :reply-with <word> :language <word> :ontology <word> :content <expression> ….)

Figure 2: A template of the KQML structure.

The following is an example of an actual KQML message sent by agent “Sales” to agent “Inventory-server”, inquiring about the number of stock for Product A, where ?x is an uninstantiated variable.

(request :from SalesDept :to InventoryDept :sender SalesAgent :receiver Inventory-server :language KIF :ontology :content (= (product.stock (product a) ?x) :reply-with <a unique string as the tag of this message) )

Figure 3: A simple example of using KQML.

4.2.2.4 Knowledge Interchange Format (KIF) KIF is used to describe a wide variety of things and situations that happen in the real-world. By using common symbolic logic (e.g. first order predicate calculus), KIF is recognizable by both computer systems and people, and it can serve as a mediator in the translation of other languages. Instead of transferring between different formats in the conversation, each agent only needs to convert the content of the message between its own internal representation and the common format KIF. Due to its


rich expressive power and simple syntax, KIF is probably the most widely used neutral message content format for agent communication [35]. KIF is currently the subject of an ANSI standardization study. 4.2.2.5 A shared ontology In a MAS, individual agents may be conceptualized differently in terms of objects, classes and properties of objects. For example, the same object may be named differently or the same term may have different definitions from different perspectives by individual agents. It is essential to ensure correct mutual understanding of the exchanged messages. Therefore, agents must agree upon the common format which can be used to model the world or part of the world in which they are exchanging information with each other. An ontology is used to define the common concepts, attributes, and relationships for different subsets of world knowledge. It can be represented in the form of a document or a set of machine interpretable specifications [35]. 4.3 Agent collaboration In a MAS, agent collaboration means a group of agents working together to implement tasks of a project. In order to facilitate collaboration between agents, issues of task decomposition and resource allocation need to be addressed. Task decomposition is the process of dividing a task or a big project into sub-tasks. For instance, in a concurrent manufacturing system, a high-level product design task can be decomposed into a number of assembly designs which may be further decomposed into a number of part designs at shop floor level. Once the procedures of task decomposition are completed, available resources need to be properly allocated. Shen et al. [14] suggest several issues which need to be considered when decomposing tasks and allocating available resources. These are summarized below:

• The problem to be resolved should be naturally analyzable by levels of abstraction (from more general to more detailed).

• Constraints involving control, data or resources should be taken into account.

• Decomposed tasks should be as independent as possible. This minimizes the need for co-ordination between tasks.

• The amount of information to be transmitted between tasks should be minimized.

• The decomposed tasks should be able to use their local resources in order to reduce conflicts between resources.

In an agent-based manufacturing system, task decomposition can be carried out by special agents in a distributed and dynamic environment. The relevant knowledge can be stored in knowledge bases (e.g. product modelling databases, design/manufacturing knowledge bases, or rule bases). Several task allocation mechanisms are discussed in Shen et al. [14]. Examples of such systems are the


mediators in MetaMorph I & II [2], [38], and the project manager agent in Distributed Intelligent Design Environment (DIDE) [39]. 4.4 Agent itinerary pattern As discussed above, an MA can migrate from machine to machine in a heterogeneous environment. It should be capable of interacting with other agents where services are provided, moving to another machine while carrying intermediate results, and resuming execution when it reaches its destination. By moving to the location of an information resource, the agent can search the resource locally, which eliminates the transfer of intermediate results across the business network and thus reduces end-to-end latency. The agent itinerary pattern is concerned with routing between multiple destinations. It maintains a list of destinations and defines a routing scheme as well as handling unexceptional cases. It can choose different migration strategies depending on its task and the overall business networking environment, and then change its strategies when necessary. Hence, what is required is a set of planning algorithms that allow an agent or a small group of co-operating agents to identify the best migration path through a corporation network. Researchers working in agent itinerary have developed search algorithms in the various areas. For example, Moizumi & Cybenko [40] and Brewington et al. [41] discussed the Travelling Agent Problem (TAP), which is based on the theory of the Travelling Salesman Problem (TSP), in the distributed information system [40], [41]. It is intended to optimize the search sequence of destinations visited and minimize the total expected searching time. Yokoo & Ishida [42], proposed a number of agent searching algorithms to address the following phenomena - path-finding problems, constraint satisfaction problems and two-player games, etc. 4.5 Agent-based manufacturing applications The following sections demonstrate a number of agent-based applications in areas of manufacturing. 4.5.1 Enterprise integration and supply chain management At the intra-enterprise level, a manufacturing enterprise integrates relevant activities such as purchasing, order acquisition, product design, process planning and scheduling, execution control, etc., within a single enterprise. Increasingly, manufacturing enterprises are facing higher global challenges. Therefore, at the inter-enterprise level, the supply chain of a manufacturing enterprise forms a world-wide network between suppliers, customers, distribution centres, retailers and partners to facilitate the flow from raw materials acquired through its transformation into the customized product. Agent-based technology provides a natural way to encapsulate these activities to form an integrated environment. Research projects in the fields of enterprise integration and supply chain management include Integrated Supply Chain Management – ISCM [15], [16],


in which each agent performs one or more supply chain functions and co-ordinates its actions with other agents; and MetaMorph II [17] which use a hybrid agent-based mediator-centric architecture to integrate partners, suppliers and customers dynamically within a supply chain network. Other researchers have proposed applying mobile agent technologies in industrial situations. Brugali et al. [3], for example, proposed applying mobile agent technology to an industrial process in the field of textile manufacturing; Papaioannou & Edwards [43] illustrate how mobile agent technology can support sales order processing in the virtual enterprise environment. 4.5.2 Concurrent engineering design and manufacturing Concurrent engineering design includes all of the functions from design, manufacturing, assembly, testing and quality to purchasing from suppliers and customer service. It requires the co-operation of multidisciplinary design teams using sophisticated and powerful engineering tools such as Computer Aided Design (CAD) tools, engineering database systems, and knowledge-based systems. It also requires appropriate sharing of data and knowledge among all participants who contribute to the various stages and sub-stages of the design, building or manufacturing of a product [44, 45]. A number of research projects using agent-based approaches in the field of engineering design have been reported, such as Distributed Intelligent Design Environment (DIDE) [17], [2], which is designed for integrating engineering tools and human specialists in an open environment; ActivePROCESS [46] and Knowledge Infrastructure for Collaborative and Agent-based Design (KICAD), which are proposed for supporting collaborative engineering and design, etc. 4.5.3 Manufacturing planning, scheduling and execution control Manufacturing planning is the activity of selecting and sequencing production processes so that they achieve one or more goals and satisfy a set of domain constraints. Manufacturing scheduling is concerned with allocating limited resources (e.g. machines, tools, operators, etc.) and time to the set of production processes in the plan. As these resources are subject to change dynamically, manufacturing scheduling becomes a very complex and difficult problem. Manufacturing control is mainly concerned with manufacturing resources used to deliver the unit-processes (low-level control) as well as the overall co-ordination of the manufacturing resources (high-level control). Manufacturing control should be in accordance with production planning and scheduling to activate a manufacturing plant to create the desired products. Agent-based approaches provide a possible way to integrate product design planning and resource scheduling activities in the enterprise-level. Examples of representative agent-based manufacturing planning and scheduling include MetaMorph I [47], which employed Design Mediators and Resource Mediators to co-ordinate resource agents at shop floor level; MetaMorph II [2], which developed several mechanisms for dynamic scheduling and proposed the use of Execution Mediators to co-ordinate the execution of the machines, AGVs, and workers; Consortium for Intelligent Integrated Manufacturing Planning-


Execution (CIIMPLEX) [35] which was primarily proposed to develop technologies for the intelligent enterprise-wide integration of planning and execution for manufacturing; Usher & Wang [48] discussed how agent-based approaches apply to manufacturing control systems and they also proposed negotiation mechanisms for co-ordinating the execution of various decisions. As the manufacturing system is so complicated, a large number of applications applicable to various manufacturing systems have been investigated. A complete survey was conducted by Shen et al. [17], [14]. 5 Data mining and knowledge discovery As manufacturing enterprises move into the age of digital information, the problem of information overload becomes significant. New computational techniques and tools are required to support the extraction of useful knowledge from the rapidly growing volumes of databases. It is hence understandable that great interest is being shown in the new field of “Data Mining” or Knowledge Discovery in Databases (KDD) [49], [50], [51]. The concept of data mining concerns the discovery of hidden knowledge, unexpected patterns and new rules gathered from large databases [9]. It is the process of discovering interesting knowledge, which may be deduced from databases, data warehouses, or other information repositories. Data mining techniques can be used to predict future trends and behaviour, thereby allowing companies or manufacturing enterprises to make proactive and knowledge-driven decisions. It involves the study of statistical measures and the areas of artificial intelligence (e.g. knowledge discovery and machine learning). In this section, we will discuss the emerging concepts of data mining technology and processes of KDD. We will then look at the ideas of data patterns and their dependency. A number of classification methods and data mining algorithms will be illustrated in order to evaluate how they can be used to explore useful knowledge. Finally, we will investigate the major research interests in the area of the manufacturing environment. 5.1 Data mining technology Data mining techniques are applied to the contents of a data warehouse to search for “hidden” information. Several mechanisms are required for the discovery of patterns, associations, changes, anomalies, rules, and statistically significant structures and events in large databases. A major advantage of aggregating data from a diverse range of sources is the facility to look for trends, patterns and correlations in the data and the results can further help improve the decision-making process. Data mining concept often refers to a multi-disciplinary field, which consists of several research areas, including machine learning, statistics, neural networks, etc., to extract knowledge from real-world data sets (see Figure 4). We will discuss a few of these in later sections.


MACHINE LEARNINGNEURAL NETWORK

DATA MINING(KDD) STATISTICAL

MODELS

VISUALISATION

DATABASEMANAGEMENT

Figure 4: A multi-disciplinary field of a data mining paradigm (source: Adriaans & Zantinge [9]).

5.2 Processes of KDD KDD associates a series of processes that an organization can use to explore the hidden knowledge contained within the large volume of databases created in the business environment. KDD comprises six phases; Data Selection, Cleaning, Enrichment, Coding, Data Mining and Reporting [9]. However, the KDD processes should consider knowledge discovery as well as understand environmental constraints, explore and analyze data patterns and features. Therefore, we justify the model by adopting a few more steps in order to provide a more integrated view. This is explained below:

• Prior analysis. This step is to learn relevant knowledge and the goals of the application. It includes the analysis and assessment of problems occurring in the manufacturing and business environment. This will help investigate the types of required data, suitable data mining techniques, and how the results of data mining will be deployed as part of the overall solution.

• Data preparation. This step involves extracting the data and transforming it into the format required for the next step – data mining. It consists of the following phrases [10]: - Data selection. This includes selecting a dataset or focusing on a subset

of variables or data sample on which the discovery application is to be performed. The data selected in the knowledge discovery process is based on an evaluation of its potential to yield knowledge.

- Data cleansing. The data could be regarded as dirty or noisy in cases such as missing field information, duplicate data or details typed slightly differently, etc. This process ensures that all values in a dataset


are consistent and correctly recorded in respect of their types, schema, and the mapping of missing and unknown values.

- Enrichment. In the enrichment phase, data retrieved from an external source is added to the selected and cleaned data to add value to it.

- Coding. During this phase, raw data is converted into a form that lends itself to be more effectively mined.

Database

Database

DatabaseKnowledge

base

TrainingBase

Selection &cleaning &

Enrichment &formatting

data

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.

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Data Preparation

Data Mining

Data

Data

Knowledge

Knowledge Interpretation

KnowledgeRepresentationPrior Analysis

InternationalBusiness and Manufacturing

Marketing

R & D

Warehouse

Manufacturing

Sales

$ $$

Purchasing

Distribution

Decision Tree

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K-NN

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Figure 5: Processes of knowledge discovery in databases.

• Data mining. This step includes searching data patterns in a particular

representational form and deciding suitable mining algorithms. Several approaches and algorithms are available, such as classification rules or trees, regression, clustering, sequence modelling, line analysis, machine learning algorithms, neural networks, etc. Examples will be discussed in Section 5.4.

• Interpretation. This step is to interpret the discovered data patterns. It will need to visualize the extracted patterns and remove redundant or irrelevant patterns. If necessary, it will return back to any of the previous steps.

• Knowledge Representation. During this phase, the data discovered is demonstrated and used. It makes the knowledge obtained from KDD processes available to the decision makers in proper formats.

A diagram showing the processes of KDD is illustrated in Figure 5.


5.3 Data patterns and dependency In order to analyse the behaviour of a system, its data and associated patterns between data elements need to be well understood. Berson & Smith [52] defined “pattern” as an event or combination of events in a database that occurs more often than expected. It is only when this understanding is as comprehensive as possible, that the success of the operation and management of such a system is possible. For example, the format of information will depend on the type of system and can range from a textual database, a graphical representation, hierarchical numeric type or the result of elaborate statistical analysis. Research reveals that it is hard to find the desired dependencies as typically the data set is massive and a lot of irrelevant patterns exist [53]. From this point of view, the methods developed need to be adequate with respect to the patterns and dependencies that are discovered. This means, given a set of data, all possible dependencies or patterns that exist between these values need to be generated and gathered. The resulting set of dependencies will have to be presented in a well-ordered structure. Several methods are proposed to build models and extract data patterns [54], [1], such as:

• Classification. Classification identifies the process and discovers rules that define whether an item or event belongs to a particular subject of data (class). Classification in knowledge systems is referred to as supervised learning.

• Associations or Link Analysis. This usually refers to discovering associations between the various data fields. This type of query requires an associative algorithm to find all the rules that will correlate one set of events or items with another set of events or items. These rules may be adjusted on repetitive executions to find the best possible occurrence.

• Sequential Patterns. The concept of sequential patterns implies that the data is stored over a period of time. This is a historical data store of all the details or transactions from the operational systems, to be searched for patterns that have a high probability of repetition.

• Clustering. Clustering identifies clusters embedded in the data, where a cluster is a collection of data objects that are “similar” to one another. The key concepts of using clustering algorithms are intended to discover data sets, measure their proximity (similarity) and to discover some kind of pattern that specifies, in a generalized abstract form, which data instances belong to each cluster. Theoretically, a clustering algorithm should maximize intra-cluster similarity and minimize inter-cluster similarity [1]. Its basic use is to discover a previously unknown or suspected class of data, for example, defect analysis or affinity group analysis. Examples of clustering algorithms include iterative partitioning


and hierarchical clustering [55]. More examples of clustering algorithms can be found in Jain et al. [56] and Freitas [1].

5.4 Data mining algorithms Several examples of data mining algorithms, such as decision trees, rule inductions, evolutionary algorithms, neural networks, nearest neighbour algorithms, Bayesian learning, machine learning, genetic algorithms, inductive logic programming and various kinds of statistical algorithms have been well investigated [52], [53], [9], [57], [58], [1]. It is difficult to make precise statements and comparisons about the effectiveness of each of these algorithms. Here we only discuss a few:

• Statistical Algorithms. These are based on an underlying probability model, which provides an estimate of the probability of being a member of a class rather than a simple classification. An example of a statistical algorithm is k-nn or nearest neighbour algorithm [1]. - Nearest neighbour algorithm: This is a technique that classifies each

record based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k is greater than or equal to 1).

• Machine Learning Algorithms. Machine learning algorithms use automatic procedures based upon logical or binary operations in order to learn a task from a series of examples. The majority of these algorithms employ decision-tree and rule induction approaches in order to generate classifying expressions simple enough to be understood. - Decision tree. This is a predictive model that makes a prediction on the

basis of a series of decisions/deductions. The leaves of the tree are partitions of the data set with their classification. The branch of the tree is a classification question. This method can be used to represent predictors and goals of knowledge. It is usually built by a top-down, “divide-and-conquer” algorithm [59], or other methods such as genetic programming. The algorithms used tend to automate the process of the hypothesis generation and then validate more thoroughly and in a much more integrated way than any other data mining techniques. Further details about how to build a decision tree can be found in [1].

- Rule induction algorithm. This discovers knowledge by using the expression of IF-THEN prediction rules. The semantic of the IF-THEN rules is that the rule antecedent (IF) consists of a conjunction of conditions and the rule consequent (THEN) predicts a certain goal-attribute value for a data instance that satisfies the rule antecedent. Normally the algorithm keeps performing an iterative process until a satisfactory set of rules is found.

In fact, a rule induction algorithm is an application of decision-tree building, although the rule induction algorithm is more flexible than a decision tree. Decision tree and rule induction algorithms are regarded


as “goal driven” data mining techniques in that a business (or data mining) goal is defined and rule induction is used to generate patterns that relate to that goal [10].

• Neural Networks. The concept of neural networks is intended to model the learning patterns of the human brain. This model employs a network of nodes in Input and Output (I/O) layers separated by one or more hidden layers. Each I/O layer is associated with a variable in the dataset and has connections to all nodes in adjacent layers. The complete network represents a complex set of interdependencies, which may include a degree of non-linearity. These algorithms combine the complexity of statistical techniques with machine learning. They consist of multiple, simple processing units connected by adaptive weights among layers. Adjusting the weights on the hidden-layer nodes trains the network to minimize error in the output across a set of training data. The trained network can then be used to make predictions for new data, called supervised learning. Research into neural networks methods can be found in [62], [63]. Neural networks are used as sophisticated pattern detection and machine learning algorithms to build predictive models from large historical databases. This technique is probably the best known for data mining, partly because of its ability to provide highly accurate models in a variety of real-world problem situations. However, important design considerations, such as its set-up and the data it processes, need to be tackled in order to use this technique effectively. In some cases, the models developed are so complicated even experts fail to understand them. Therefore, it still remains a challenge for researchers.

5.5 Major research areas of data mining Applications using data mining techniques in the manufacturing environment cover nearly every aspect of a manufacturing system. Much research work has been conducted [49], [50], [51]. The areas involved can be divided into the following fields:

• Product development • Data representation/visualization • Concurrent engineering and design • Manufacturing • Process analysis and design • Process and quality control • Process planning and scheduling • Fault diagnosis • Learning, design and control in robotics • Integrated systems

- Data mining for Material Requirements Planning (MRP) - Enterprise Resource Planning (ERP) and workflow management


Moreover, in Braha’s book [49], several researchers bring together several research ideas and applications for data mining within design and manufacturing. It is intended to present the formal tools required to extract valuable information from design and manufacturing data, and facilitate interdisciplinary problem solving for enhanced decision making.

Table 1: Summary of data mining applications in manufacturing systems.

Enterprise Planning • Enterprise learning • Marketing and logistics • Supply and delivery forecasting • Learning suppliers, customers, partners involved in supply chain

management • Fraud detection and management • Time series analysis with neural networks for inventory application, etc. Customer Management • Extracting patterns from customer needs • Demand forecasting • Service management • Learning interrelationships between customer needs and design

specifications, etc. Product Development • Visualizing relationships in large product development databases • Concept Management (clustering of design concepts; indexing and retrieval

of design concepts in knowledge bases; data mining procedure for concept selection)

• System-level design • Prediction of product development (e.g. span time and cost) • Project evaluation, etc. Engineering Design • Dynamic indexing and retrieval of design information in knowledge bases • Creative design using genetic algorithms and evolutionary programming • Concurrent engineering (e.g. extracting interrelationships between design

requirements and manufacturing specifications; exploring tradeoffs between overlapping activities and co-ordination costs; combining expert knowledge)

• Cost evolution systems (design for assembly and manufacturing) • Industrial design integrated data mining and design of experiments, etc. Manufacturing planning and scheduling • Selection of material and manufacturing processes • Time series analysis


• Manufacturing knowledge acquisition • Process analysis • Dynamic scheduling • Real-time inventory control • Operational manufacturing control • Dynamic indexing and retrieval of high-dimensional manufacturing

information • Feature selection and dimensionality reduction of manufacturing data • VLSI implementation of neural network and fuzzy systems, etc. Shop Floor Monitoring and Control • Adaptive machine operation and control • Process/equipment monitoring • Cutting tool state classification for tool condition monitoring • Fault diagnosis and detection • Remote repair • Predictive quality models and process and quality control • Preventive machine maintenance • Predicting assembly errors • Learning in the context of robotics (e.g. navigation and exploration,

mapping, extracting knowledge from numerical and graphical sensor data), etc.

(Table 1: continued)

Other research work investigates the use of data mining in the manufacturing system. Yoshisa & Hideyuki [60] employed a “rule mining” algorithm to find the association rules to resolve the manufacturing scheduling problems and improve its performance; Kusiak [50] and [58] discussed a number of rule extraction algorithms, particularly the Rough Set Theory, to predict the data requirement in a semiconductor industry; The Manufacturing Engineering Centre (MEC) supported by ESPRC, EC and industry has devoted significant efforts in the research of intelligent quality systems and process modelling and control systems by adopting numbers of data mining techniques, fuzzy logic, neural networks, genetic algorithms, etc. [64]; a list of industrial projects summarized by Institute for Operations Research and the Management Science (INFORMS) can be found at [65]. In Table 1, possible applications of using data mining algorithms in the field are summarized [12], [49], [51]. There are still many challenges to overcome in terms of mining useful knowledge in the area of manufacturing systems. These challenges include massive amounts of information, missing information, high dimensionality of data structure, user knowledge, integrity of data, a large amount of nonstandard, multimedia, and object-oriented data, etc., in manufacturing systems. Moreover, a great challenge is how to avoid some kinds of misinterpretation and false expectations. It will be the responsibility of


researchers and practitioners in the field to deliver the requirements against these limitations. 6 Conclusions With the emergence of Internet technologies, business and manufacturing enterprises are now facing difficult challenges to maintain their competitive market position. An e-business strategy and e-manufacturing system requires high flexibility and good responses in both business operations and manufacturing activities. Hence, timely decision-making in all manufacturing activities plays a vital role in the achievement of optimal and cost-effective manufacturing. In this article, we discussed two important techniques, agent-based technology and data mining technology, which assist manufacturing enterprises to face competitive challenges and retain high efficiency. This article describes a number of fundamental concepts and theories, and demonstrates the efforts made to develop architectures that fulfill the main requirements presented by such concepts. Agent technology has been widely applied to various areas in manufacturing systems such as concurrent engineering, collaborative engineering design, supply chain management, manufacturing enterprise integration, manufacturing planning, scheduling and control, etc., which we briefly discussed above. Agent-based technology creates a virtual environment in which the tasks can be carried out in an autonomous, co-operative, reactive and proactive way under an open, dynamic and distributed environment. Data-mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated by the manufacturing process can be translated into useful knowledge. Combining the above two techniques will provide highly specialized, flexible and integrated techniques in various application domains. There are however still many challenges to overcome in the e-manufacturing environment due to its highly dynamic, distributed and robust nature. The specific knowledge and tasks still need to be reviewed and interpreted prior to direct application in the form of a process change or a new control algorithm for a tool. Increasingly, methods will be developed to reduce the limitations and complexity of the system itself. References [1] Freitas, A.A., Data Mining and Knowledge Discovery with Evolutionary

Algorithms, Springer-Verlag: Heidelberg, Germany, 2002. [2] Maturana, F.W., Shen, W., Hong, M. & Norries, D.H., Multi-Agent

Architectures for Concurrent Design and Manufacturing. Proceedings of the ISAS’ 97, 1997.


[3] Brugali, D., Menga, G. & Galarraga, S., Inter-Company Supply Chains Integration via Mobile Agents. Proceedings of the PROLAMAT’ 98, Trento, Italy, 1998.

[4] Wu, J., Cobzaru, M., Ulieru, M. & Norrie, D.H., SC-web-CS: Supply Chain Web-Centric Systems. Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing (ASC2000), 2000.

[5] Bohnenberger, T., Fischer, K. & Gerber, C., Agents in Manufacturing: Online Scheduling and Production Plant Configuration. Proceedings of the ASA/MA’ 99, 1999.

[6] Christensen, J.H., Holonic manufacturing systems: initial architecture and standards directions. Proceedings of the First European Conference on Holonic Manufacturing Systems, Hanover, Germany, 1994.

[7] Thompson, D. & Hughes, D.R., Holonic Modelling. Manufacturing Engineering, pp. 116–119, 1998.

[8] Iakovidis, G. & Buchal, R.O., Development of a Virtual Collaboration Environment for Concurrent Product Development using the Internet. Proceedings of the Tenth International IFIP WG 5.2/5.3 Conference PROLAMAT’ 98, 1998.

[9] Adriaans, P. & Zantinge, D., Data Mining. Addison-Wesley Longman: Harlow, England, 1998.

[10] Miller, L.R., Knowledge Discovery in Databases. http://www.chips.navy.mil/archives/00_oct/database.html Accessed: 29th May, 2003.

[11] Rembold, U., Nnaji, B.O. & Storr, A., Computer Integrated Manufacturing and Engineering, Addison-Wesley: UK, 1994.

[12] Shen, W. & Norrie, D.H., Agent-based systems for intelligent manufacturing: a state-of-the-art survey. Knowledge and Information Systems, pp. 129–156, 1999.

[13] Jennings, N.R. & Wooldridge, M.J., Applications of intelligent agents. Agent Technology: Foundations, Applications, and Marketing, ed. N.R. Jennings, & M.J. Wooldridge, Springer-Verlag: New York, 1998.

[14] Shen, W., Norrie, D.H. & Barthes, A. Jean-Paul, Multi-Agent Systems for Concurrent Intelligent Design and Manufacturing. Taylor & Francis: London, UK, 2001.

[15] Fox, M.S., Chionglo, J.F. & Baruceanu, M., The Integrated Supply Chain Management System, Dept. of Industrial Engineering, University of Toronto, 1993.

[16] Barbuceanu, M. & Fox, M.S., The information agent: an infrastructure agent supporting collaborative enterprise architectures. Proceedings of the 3rd Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprise, Morgantown, West Virginia, 1994.

[17] Shen, W. & Norries, D.H., A Hybrid Agent-oriented Infrastructure for Modeling Manufacturing Enterprises, 1999. http://ksi.cpsu.ucalgary.ca/KAW/KAW98/shen/ Accessed: 12 August 1999.


[18] Jin, Y. & Zhou, W., Agent-based knowledge management for collaborative engineering. Proceedings of the ASME Design Engineering Technical Conferences, Las Vegas, Nevada, 1999.

[19] Bieszczad A., Pagurek B., and White T., Mobile Agents for Network Management, IEEE Communications Surveys, 1998, http://www.comsoc.org/pubs/surveys/4q98issue/bies.html Accessed 04 May, 2001.

[20] Brazier, F.M.T., Mobach, D.G.A., Overeinder, B.J. & Wijngaards, N.J.E., Supporting Life Cycle Coordination in Open Agent Systems, www.iids.org/publications/aamas02_problem_spaces.pdf Accessed 29th April, 2003.

[21] Green, S., Hurst, L., Nangle, B., Cunningham, P., Somers, F. & Evans. R., Software agents: A review. Technical Report, Intelligent Agents Group, 1997.

[22] FIPA, Specification: Agent Management Support for Mobility, part 11: Version 0.4, October, 1998.

[23] Assis-Silva, F.M. & Popescu-Zeletin, R., An approach for providing mobile agent fault tolerance. Proceedings of the Mobile Agents International Workshop-MA’98, 1998.

[24] Object Management Group, Mobile Agent Facility Specification Draft Version 7 (Joint Submission). OMG Common Facilities Request for Proposal 3, June 1997.

[25] Vogler, H., Kunkelmann, T., & Moschgath, M.L., An approach for mobile agent security and fault tolerance using distributed transactions. Proceedings of the 1997 International Conference on Parallel and Distributed Systems, 1997.

[26] ObjectSpace, Agent-Enhanced Manufacturing System Initiative: Project Brief. ObjectSpace, 1997.

[27] IBM., Aglets Software Development Kit, http://www.trl.ibm.com/aglets/ (Web site with information about an agent development kit called Aglets), Accessed: 29th May, 2003.

[28] Lange, D.B. & Oshima, M., Programming and Deploying JavaTM Mobile Agents with AgletsTM, Addison-Wesley: USA, 1998.

[29] General Magic, Introduction to the Odyssey API, 1998. [30] Papaioannou, T. & Edwards, J., Using mobile agents to improve the

alignment between manufacturing and its support systems. Journal of Robotics and Autonomous Systems, 27, pp. 45–57, 1999.

[31] Lee, W.B. & Lau, H.C.W., Multi-agent modelling of dispersed manufacturing networks. Expert System with Applications, 16, pp. 297–306, 1999.

[32] Nwana, H.S. & Ndumu, D.T., ZEUS: An advanced tool-kit for engineering distributed multi-agent systems. Proceedings of the PAAM’98, London, 1998.

[33] Flores-Mendez, R.A., Towards the Standardization of Multi-Agent System Architectures: An Overview. Association for Computer Machinery (ACM) Crossroads, Special Issue on Intelligent Agents, 5.4, pp. 18–24, 1998.


[34] Genesereth, M., An Agent-Based Framework for Interoperability. Software Agents, ed. J.M. Bradshaw. Menlo Park, Calif., AAAI Press, pp. 317–345, 1997.

[35] Peng, Y., Finin, T., Labrou, Y., Cost, R.S., Chu, B., Long, J., Tolone, W.J., & Boughannam, A., An agent-based approach for manufacturing integration gent - the CIIMPLEX experience, International Journal of Applied Artificial Intelligence, 13(1–2), pp. 39–64, 1999.

[36] Searle, J. R., Speech Acts, 1969, in Multi-Agent Systems for Concurrent Intelligent Design and Manufacturing. eds. W. Shen, D.H. Norrie and B.J. Pattinson, Taylor & Francis: London, UK, 2001.

[37] Huhns, M.N. & Stephens, L.M., Multiagent Systems and Societies of Agents, Multiagent Systems – A Modern Approach to Distributed Artificial Intelligence. ed. G. Weiss., Massachusetts Institute of Technology: Cambridge, Massachusetts, pp. 80–120, 2000.

[38] Maturana, F., Shen W. & Norrie, D.H., Metamorph: an adaptive agent-based architecture for intelligent manufacturing. International Journal of Production Research, 1999.

[39] Shen, W. & Barthes, A.J., An experimental multi-agent environment for engineering design. International Journal of Cooperative Information Systems, 2&3(5), pp. 131–151, 1996.

[40] Moizumi, K. & Cybenko, G., The Travelling Agent Problem. Mathematics of Control, Signals and Systems, 1998.

[41] Brewington, B., Gray, R. & Moisumi, K., Mobile agents in distributed information retrieval. Intelligent Information Agents, ed. M. Klusch: pp. 335–395, 1999.

[42] Yokoo, M. & Ishida, T., Search Algorithms for Agents. Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, ed. G. Weiss, The MIT Press: Cambridge, Massachusetts, pp. 165–199, 2000.

[43] Papaioannou, T. & Edwards, J., Mobile Agent Technology in Support of Sales Order Processing in the Virtual Enterprise, Kogan Page Science, London, 1999

[44] Pham, D.T. & Dimov, S.S., An approach to concurrent engineering. Proceedings of the IMechE: Part B – Journal of Engineering Manufacture, 212, pp. 13–27, 1998.

[45] Cutkosky, M.R., Engelmore, R.S., Fikes, R.E., Genesereth, M.R., Gruber, T.R., Mark, W.S., Tenenbaum, J.M. & Weber, J.C., PACT: an Experiment in Integrating Concurrent Engineering Systems. IEEE Computer, 26(1), pp. 28-37, 1993.

[46] Jin, Y., Zhao, L. & Raghunath, A., Activeprocess: A process-driven and agent-based approach to supporting collaborative engineering. Proc. of the DETC’99 - ASME Design Engineering Technical Conferences, Las Vegas, Nevada, 1999.

[47] Maturana, F. & Norrie, D.H., Multi-agent mediator architecture for distributed manufacturing. Journal for Intelligent Manufacturing, 7, pp. 257–270, 1996.


[48] Usher, J.M. & Wang, Y.C. Negotiation between Intelligent Agents for Manufacturing Control. Proceedings of the EDA 2000 Conference, Orlando, Florida, USA, 2000.

[49] Braha, D., ed. Data Mining for Design and Manufacturing: Methods and Applications, Kluwer Academic Publishers: Dordrecht Hardbound, 2001.

[50] Kusiak, A., Rough Set Theory: A Data Mining Tool for Semiconductor Manufacturing. IEEE Transactions on Electronics Packaging Manufacturing, 24(1), pp. 44–50, 2001.

[51] Lin, R.-H., Introduction CIM or/and DM, http://www.engineering.uiowa.edu/~ie_231/DatMine.pdf Accessed: 29th May, 2003.

[52] Berson, A. & Smith S.J., Data Warehousing, Data Mining, and OLAP, McGraw-Hill: USA, 1997.

[53] Venter, F.J., Oosthuizen, G.D. & Roos, J.D., Lattice-based knowledge discovery in network management data. Informatica, 21(2), June, pp. 227–238, 1997.

[54] Yevich, R., Data Mining. Data Warehouse: Practical Advice from the Experts, eds Bischoff, J. & Alexander, T., Prentice-Hall: New Jersey, USA, 1997.

[55] Backer, E., Computer-Assisted Reasoning in Cluster Analysis, Prentice-Hall, 1995.

[56] Jain, A.K., Murty, M.N. & Flynn P.J., Data clustering: a review. ACM Computing Surveys, 31(3), pp. 264–323, 1999.

[57] Burn-Thornton, K.E., Garibaldi, J. & Mahdi, A.E., Pro-Active Network Management Using Data Mining, IEEE GLOBECOM 1998, November, pp. 1208–1211, 1998.

[58] Kusiak, A., Decomposition in data mining: an industrial case study. IEEE Transactions on Electronics Packaging Manufacturing, 23(4), pp. 345–353, 2000.

[59] Quinlan, J.R., ed., C4.5: Programs for Machine Learning. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Morgan Kaufmann: San Mateo, 1993.

[60] Yoshisa, T. & Hideyuki, T., A Study on Association among Dispatching Rules in Manufacturing Scheduling Problems. Proceedings of 7th IEEE Int. Conf. on Emerging Technologies and Factory Automation (ETFA’99), pp. 1355–1360, Barcelona, Spain, October 1999.

[61] Kusiak, A., Evolutionary computation and data mining. Proceedings of the SPIE Conference on Intelligent Systems and Advanced Manufacturing, Boston, MA, USA, 2000.

[62] Li, E.Y., Artificial neural networks and their business applications. Information & Management, 27(5), pp. 303–313, 1994.

[63] Walczak, S. & Cerpa, N., Heuristic principles for the design of artificial neural networks. Information & Software Technology, 41(2), pp. 107–117, 1999.


[64] The Manufacturing Engineering Centre (MEC), Intelligent Manufacturing, http://www.mec.cf.ac.uk/research/?view=intel_manufac&style=plain Accessed date: 29th May, 2003.

[65] Institute for Operations Research and the Management Science (INFORMS), Applications of Neural Nets & Genetic Algorithms, http://www.informs.org/Conf/Cincinnati99/TALKS/Cluster-3.html Accessed date: 29th May, 2003.


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