Int J Adv Manuf TechnolDOI 10.1007/s00170-005-0115-9

ORIGINAL ARTICLE

Felix T. S. Chan . Rahul Swarnkar . Manoj K. Tiwari

Infrastructure for co-ordination of multi-agents

in a network–based manufacturing system

Received: 18 May 2004 / Accepted: 30 March 2005 / Published online: 12 November 2005# Springer-Verlag London Limited 2005

Abstract Global competition, shorter lead times and cus-tomer demands for increasing product variety have col-lectively forced the manufacturing enterprises to rapidlydevelop and introduce new products to obtain quick returnon their investments. The variations in today’s manufactur-ing scenario are directly driven by the requirements for aproduct’s price, quality, delivery performance, customerchoices, etc. As a consequence, adaptability, reflexivity andresponsiveness are a common denominator for judging thecompetitive advantage of the manufacturing firms. Theinclusion of these characteristics entices the implementationof the concepts of distributed artificial intelligence (DAI)and information-based manufacturing. Thus a synergizeduse of agent-based manufacturing (derived from DAI) andsupporting information architecture is well suited in thecontemporary manufacturing arena for dealing with varia-tions and uncertainties. In this paper, a conceptual infra-structure for information-based control architecture isdiscussed and special emphasis is put on multi–levelco-ordination.

1 Introduction

In the last two decades or so, the manufacturing industryhas undergone various paradigm shifts, e.g. from massproduction through to flexible and lean manufacturingtowards agile manufacturing philosophy [1, 2]. Thesevariations are propelled by the requirements of competitive

product pricing and quality, delivery performance, varia-tions in customer demands, etc. that can be construed as theconsequence of unprecedented changes of the competitivemarket environment, globalization of the market, frequentvariations in customer demand, customer-oriented productdesign and shorter lead times. These factors have greatlyinfluenced all forms of manufacturing–related activitiessuch as order, design, planning, manufacturing, workshopcontrol, assembly, delivery, maintenance and marketing,etc.; hence it is prerequisite for the manufacturers to pro-duce with high productivity and reduced cost but not tocompromise on the quality. Moreover, it is also equallynecessary for the firms to possess the characteristics ofadaptability, reflexivity and responsiveness to encounterthe market, which is becoming more and more diverging,unstable and mutable and customer oriented. To addressthese issues, it is important for the manufacturing industryto inculcate the traits of agile manufacturing by distributingintelligence and decision-making entities as ubiquitouslyas possible, even in the vicinities of the ports of delivery,sale and aftersales services. In this context, the applicationof agent technology, which has evolved from distributedartificial intelligence (DAI) and supporting informationarchitecture, would synergize each other for greater ben-efits of the manufacturing firms. The agents, which areinherently co–operative, autonomous and intelligent, arewell suited to map the diverging functions of manufactur-ing systems. The agents complimented by an informationarchitecture, which would be responsible for disseminationof important information opportunely, would make thesystem more adaptive, responsive and reflexive. In thispaper, an infrastructure is proposed which serves as aguideline for the effective implementation of agents andinformation architecture to address the requirements ofmanufacturing systems of the twenty-first century. The restof the article is presented as follows: Sect. 2 gives a briefdefinition of the term agent and its relevancy in the contextof an agile manufacturing scenario. Sect. 3 discusses theagent structure that is to be implemented in the proposedinfrastructure for supporting the manufacturing enterprise.The subsequent section delves into the role of the information

F. T. S. Chan (*) . R. SwarnkarDepartment of Industrial andManufacturing Systems Engineering,University of Hong Kong,Pokfulam Road, Hong Konge-mail: ftschan@hkucc.hku.hk

M. K. TiwariDepartment of Forge Technology,National Institute of Foundry and Forge Technology,Ranchi, 834 003, India

(2007) 31: 1028–1033

architecture for manufacturing units. The proposed infra-structure is delineated in Sect. 5. The last section deals withthe discussion and conclusion.

2 A brief background of agents

An increasing number of computer systems are beingviewed in terms of autonomous agents [3]. Agents arebeing advocated as a next-generation model for engineer-ing complex distributed systems [4, 5]. Chan et al. [6]utilises the agent technology to improve the intelligence inan application integration platform for an agile manufac-turing environment. However, since the term agent doesnot have a unique definition, there exists a vague notionabout the application of the agents in a manufacturingsystem. A brief definition of agents can be as follows:

An agent is an encapsulated computer system that can besituated in some environment and is capable of performingflexible, autonomous action in that environment in order tomeet its design objectives [7]. Further explanation of anumber of points of this definition is given by Jennings [4]:

1. Clearly identifiable problem solving entities with welldefined boundaries and interfaces.

2. Situated (embedded) in a particular environment; theyreceive input related to the state of their environmentthrough effectors.

3. Designed to fulfil a specific purpose; they haveparticular objectives (goals) to achieve.

4. Autonomous; they have control both over their internalstate and over their own behaviour.

5. Capable of exhibiting flexible problem solving behav-iour in pursuit of their design objectives; they need tobe both responsive (able to respond in a timely fashionto the changes that occur in their environment) andreactive (able to act in anticipation of future goals) [8].

When adopting an agent–oriented view of the world, itsoon becomes apparent that most problems involve multi-agents: to represent the decentralized nature of the problem,the multiple loci of control, the multiple perspective and thecompeting interests [9]. Moreover, the agents will need tointeract with one another, either to achieve their individualobjective or to manage the dependencies that replace frombeing situated in a common environment [10, 11]. Theseinteractions can vary from simple information interchanges,to request for particular actions to be performed and on toco‐operation, co‐ordination and negotiation in order toarrange interdependent activities [7].

3 The structure of agents for multi-agent-basedmanufacturing entities

Multi–agent systems research focuses on “analyzing anddeveloping intelligent communities, which comprise collec-tions of interacting, coordinated knowledge–based process”[12]. Such acknowledge–based processes are typically re-ferred to as intelligent agents, or simply agents and systems

comprised of collections of agents as multi–agent systems.This renders the system a better adaptability, reflexivity andresponsiveness due to the intrinsic characteristics of co-operativeness, intelligence and autonomy of the agents asdiscussed earlier. The structure of a generic agent to be im-plemented in a manufacturing firm is formulated as an auto-nomous, co-operative and intelligent entity, which is capableof managing various resources such as humans, computers,machines, robots, tools, fixtures, etc. as shown in Fig. 1.

Moreover, it is equipped with a communication inter-face, which is conducive for making the agent co-operativewith other agents. However, the agents are specialized forexecuting certain operations or tasks. Therefore, they canbe divided into two primary categories as follows:

High-level agents These agents are equipped with humans,databases, embedded artificial intelligence, decision sup-port facilities and communication interfaces. Their prima-ry objective is related to administrative and policy-makingjobs such as scheduling, planning, cost analysis, processplanning, advertisement, marketing, procurement etc. Spe-cialized agents undertake each of the administrative/poli-cy-making tasks. However, they are enabled to cooperatewith other pertinent agents for better functioning. Thus,the various specialized agents that can be generated arescheduling agent (SC), marketing agent (MK), customerservice agent (CS), designing agent (DG), process engi-neering agent (PE), chief administrative agent (CA), poli-cy-making agent (PM), etc.

Low-level agents These agents are primarily concernedwith execution of manufacturing operations right fromconception till completion of the tasks or jobs. They are

Communication Interface

Corporate

Database

Decision Support Mechanism

Humans, Machines,

Robots, AGVs, Tools,

Fixtures, etc.

Resources

Fig. 1 Structure of a generic agent applicable to a manufacturingfirm

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classified according to the resources utilised by them viz. amachine agent (ma) is equipped with one or more ma-chines of similar type besides computer systems, databasesand communication interfaces. The various agents in thiscategory are tool agent (tl), robot agent (rb), fixture agent(fx), conveyor agent (cv), AGV agent (ag), etc.

Apart from these intrinsic agents, the incoming jobs arealso formulated as part agents. Each job or a batch of job isprovided with a communication interface in order to have abetter traceability and management.

The above discussion lucidly delineates the various con-stituents of a multi–agent-based manufacturing system andalso their internal structure. This structure perfectly suits therequirements of agile manufacturing as it adroitly modelsthe dynamism of agile manufacturing parameters.

4 The role of an information support systemin a manufacturing scenario

“Information flow is your bloodline” – Bill Gates [15].As discussed earlier, the globalization of the market has

made it prerequisite for companies to produce high-quality,competitively priced products both quickly and efficiently.Within such an environment success heavily depends on acompany’s ability to quickly and effectively respond tocustomers’ requirements and to design, prototype, manu-facture, test and deliver high-quality products to the marketat low cost in the shortest time. According to Toh andHarding [14], advances in technological solutions coupledwith the volatility of economics and social circumstanceshave encouraged enterprises to employ a multiplicity ofcommercially viable computer–based systems to sustaintheir necessary business and manufacturing operations inan efficient and integrated manner. The reduction in pro-cessing time, physical storage and handling of paper work,and easy access to information are some of the manyjustifications for the introduction of computer–based in-formation systems. Moreover, software–based integrationinfrastructure and integration structure can play a signif-icant role respectively in supporting and organizing systembehaviour in a way that facilitates system extension andchange [15]. In this context, Internet technology is anemerging and enabling technology for manufacturing com-panies to achieve such business goals in national or globalcompetition [16]. It can be construed that the informationhas become one of the major missing links between all theentities and activities of manufacturing units and theirproper co-ordination. Therefore an infosphere can be sur-mised as one, which engulfs all activities of manufacturingas depicted in Fig. 2.

In order to synchronize all the activities as shown inFig. 2, manufacturing companies are beginning to employInternet–associated techniques to support their variousbusiness operations. The application may include thefollowing [17]:

1. Creating a corporate presence2. Communication (internal and external)

3. Corporate logistics management4. Globalization via networking5. Collaboration and development6. Information retrieval and utilization7. Marketing and sales8. Transmission of data

However, an approach is necessary for the firms todeploy Internet technology both efficiently and effectively.The use of the technology by humans of decision supportsystems has been hindered by some of the dominant char-acteristics of the global infosphere given as follows [18]:

1. Information available from the net is unorganized,multi–modal and distributed on server sites all over theworld.

2. The number and variety of data sources and servicesare exponentially increasing each day.

3. The same piece of information can be accessible from avariety of different information sources.

Thus it is becoming an exacting task for an individual ora machine system to garner, filter, evaluate and integrateinformation to support decision–making systems. Thenotion of Intelligent Software Agent (e.g. Wooldridge andJennings [8], Rao and Georgeff [19], Lang [20], Sycara andZeng [21]) has been embraced to address these criticalfactors. Although a consensus on definition of an intelligentagent is still absent, the generally accepted notion is wellcanvassed by Sycara and Zeng [18]:

Intelligent software agents are programs that act onbehalf of their human users in order to perform laboriousinformation gathering tasks, such as locating and accessinginformation from various on–line information sources,filter away irrelevant or unwanted information, and adaptovertime to their human user’s information needs and theshape of the infosphere.

Thus it is obvious that a synergized infrastructure of theaforementioned two concepts viz:

1. Multi–agent-based manufacturing system2. Complementary co-ordination information system

would result in:

(a) Better elucidation of the goals and objectives of themanufacturing enterprise and an effective means ofsocial interaction

(b) A more precise configuration for the knowledgeacquisition process

(c) Improved system generality and flexibility in terms ofmechanism

(d) A better co-ordination of various functional units of amanufacturing company

Consequently, the system will be endowed with betteradaptiveness, reflexivity, and responsiveness to face thedemanding requirements of the present manufacturingworld.

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5 The proposed infrastructure for synergizing agenttechnology and information support system

In the context of reasoning capabilities and awareness ofevents, the efficiency of agent technology in the majorityof industrial applications is hindered by the followingimpediments:

1. Bandwidth limitations make it impossible for agents tobe constantly informed of all the developments in thesystem.

2. Agents cannot continuously reason about the ongoingactivities within a community and still carry out theirnecessary local processing [22].

It is necessary to alleviate the control bottlenecks, in-crease the flexibility of co-ordination amongst the sub–components and produce a system whose performanceprogress is satisfactory and reliable. In this context, it hasbeen decided to embrace an approach in which the controlas well as the data are distributed and decentralized. Theregime provides the agents with a degree of autonomy togenerate new activities and to decide which tasks to do next.However, this act makes it consequently more difficult toattain coherent global behaviour (as each individual op-erates on the basis of local and on incomplete information).This difficulty is exacerbated in those cases where percep-tion and actions are fallible and when the environmentevolves dynamically, i.e. typical industrial application! [23].

In the perspective of these hurdles and shortcomings, theproposed infrastructure is designed to surmount the hin-drances. The infrastructure model is diagrammed in Fig. 3.

The main components of the infrastructure are:

– Global Information System (GIS)– Star network topology connection between the GIS

(which forms the central hub) and other functionalagents appended to it

– Double wire token ring network connection betweenthe agents

5.1 GIS

The basic function of a GIS is to enable integration ofapplications in a distributed computing environment op-erating with multifarious and unique operating systems,network protocols and database management systems,which can be collectively termed as a heterogeneous dis-tributed computing environment. The communication sys-tem renders a set of services, which provides transparentcommunication among all applications. A GIS is con-ducive for accessing data sources by applications through acommon means, which may be placed across a variety ofdatabases and file stores. These functions are implementedin the form of an Application Independent–ApplicationProgramming Interface (AI–API), which, instead of ren-dering the right of communication, data access and filemanagement only to specific applications, confers thesefacilities as general services. Hence, the system aptlymodels the essence of a basic integration mechanism forinformation and application integration.

5.2 Star network topology

The star network topology is the basis for a structuredcabling system. In this network, the various nodes areconnected to the central coordinating entity, e.g. hub. Inthe present context the various functional agents form thenodes and the GIS plays the role of central hub. Despitethe requirements of more cable and a special central co-ordinating entity, this network is simpler and easier tochange and manage nodes. Hence, apparently it may seemto be expensive, but in the long run, the advantage earnedwill be worth the initial investment.

5.3 Token ring network topology

In this topology, the node connections form a closed loop,called ring, through which tokens (i.e. a special packet ofdata) are passed. In this case, the various agents form the

The Manufacturing Infosphere

The Virtual Enterprise

Suppliers Customers Partners

Manufacturing Enterprise

Marketing, Design, Scheduling,

Manufacturing, Disposal

Product Realization Process

Fig. 2 Configuration of themanufacturing infosphere

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nodes, which are connected by two fibre-optic cables in aring.

The components are made full duplex to facilitatesimultaneous transmission and reception of data by thenodes. Further, Asynchronous Transfer Mode (ATM) isjudiciously implemented to integrate the network.

Justifications If all the agents have infinite processingpower and complete knowledge of the beliefs, goals,actions and interactions of their fellow community mem-bers, it would be possible to know exactly what eachindividual is doing at each instant of time and also what it isintending to do in the future. In such circumstances, thesystem could be perfectly coordinated and the cost ofachieving this state would not be prohibitively expensive[24]. This notion is aptly modelled by the proposed in-frastructure. The GIS forms the central hub of the starnetwork topology. As described earlier, it means AI-API,which enables transparent communication and renders acommon means for sharing data sources in a variety ofdatabases and file stores. Moreover, the transmission of dataacross the star network topology enables each node (i.e.agent) to see the data. Further, the network structure enablesany agent to be appended or removed from the systemeasily. The other advantage of this topology is its reliability.If any segment of the network (except the hub) fails, it willonly be an isolated segment, but the rest of the system willbe operational as usual. The token ring connectivity furtherenhances the communication reliability, even in the casewhen the central hub fails. It also reduces the informationtraffic. The dual fibre–optic connectivity is also justifiablein the sense that if one ring fails, the other maintains theconnectivity. However, under normal condition, i.e. whenboth the rings are functional, a higher speed of connectivitycan be maintained. The propriety of using ATM is pro-pounded by its following inherent characteristics:

1. ATM is connection oriented, i.e. a connection isnegotiated before data are sent. After the connection isestablished, all the data packets are sent along the samepath, which reduces the necessity of higher bandwidth.

2. ATM uses a quality–of–service scheme to distinguishbetween cells that require a constant bit stream (forvoice or video) and those that do not (e–mail).

Thus each agent is provided with a complete picture ofthe system holistically. This enables a proper co-ordinationof the various pertinent agents. Moreover, these clusterscan be reorganized or dissolved in the perspective of thejobs to be processed. This ability makes the system moredynamic and this renders it with greater flexibility torespond to any emergencies and bottlenecks (which mayarise on account of urgent orders to resource failure). Insuch cases the concerned jobs are given preference overothers or re–scheduled in a real–time fashion (on–line).Moreover the effects of such changes are promulgatedthroughout the system, so that all the concerned agents viz.policy maker, marketing, customer service, cost accoun-tant, etc. become responsive and are able to take necessarysteps to encounter any repercussion that may arise fromsuch changes. Thus the implementation of both the high-level and low-level agents in the network topology andrendering them with a comprehensive view of the totalmanufacturing activities not only helps seamless integra-tion and co-ordination but also makes the system moreadaptive, reflexive and responsive.

6 Discussion and conclusion

Various researchers such as Jennings [25], Huang and Nof[23], and Maturana and Norrie [26] have formulatedvarious architectures and infrastructures for implementingagent technology and complimentary information frame-work in a manufacturing scenario. In this context, Huangand Nof [23] proposed a genetic model for an agent anddelineated the many of its constituent functional entities.Jennings [25] addressed these issues with a proposedprototype system called GRATE (Generic Rules and Agentmodel Testbed Environment). Maturana and Norrie [26]formulated a generic Mediator for multi–agent co-ordina-tion in a distributed manufacturing system where he pro-posed a hierarchical structure constituted from TemplateMediator, DAM and Active Mediator. Maturana et al. [27]present a distributed multi–agent architecture for autono-mous systems, while a co-ordination infrastructure formobile agents is presented in Cabri et al. [28]. A CORBA-based multi–agent framework is discussed in Chan andZhang [29].

PM

MK man

SC tln

DG agn

PE

Global Information System STAR

Network

Topology

TOKEN RING

Connectivity

CS

CA

SC: scheduling agent

MK: marketing agent

CS: customer service agent

DG: designing agent

PE: process engineering agent

CA: chief administrative agent

PM: policy making agent

ma, tl, ag: resource agents

Fig. 3 The schematic model ofthe proposed framework

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The hallmarks of the infrastructure proposed here aredelineated as follows:

1. It provides a comprehensive framework for imple-menting agent technology and information supportsystem to address the various aspects of a manufactur-ing enterprise.

2. It provides a global perspective to each of the con-stituent agents so that both the hierarchical andheterarchical control mechanism can be adopted.Moreover, it deftly addresses the issues of coopera-tiveness and autonomy of the agents. This is conducivefor the entire system to achieve not only local optimumprocess parameters but also global optimal operationalparameters.

3. The system is made sufficiently dynamic to address thevarious process requirements and process disturbancesin real time. Moreover the system is so poised that theeffect of any disturbances is felt throughout the systemirrespective of the position of the epicentre of thedisturbance. Thus any disturbance can be felt withreference to all related parameters.

4. It reduces the bandwidth requirement for communica-tion, and thus the information flow is more reliable andadequately fast to achieve the process requirements.Further, the network structure makes the communica-tion links more reliable.

5. Since each agent is enabled to have a holistic view ofthe whole system, they are suitably poised to attainglobally optimized process parameters.

6. The infrastructure is framed in a manner which wouldenable agents to be easily appended or attached to thesystem.

Hence it is evident that the proposed infrastructure ishighly susceptible to address the uncertainties and dy-namism of the present manufacturing scenario with properadaptiveness, reflexivity and responsiveness. Moreover thesystem transparency not only enables each agent toascertain the demands it is expected to meet, but alsothey are empowered to avow their own efficiency and as-sess the capabilities and incapabilities of other agentsconstituting the manufacturing system. Thus the proposedinfrastructure efficiently models a powerful team ratio-nality [25], which can be defined as an embodiment ofintuitive notions such as “cooperativeness”, “team spirit”and being “a good team member”. In this connection, it canbe concluded that the proposed infrastructure makes themulti–agent-based manufacturing system amenable to therequirements of twenty-first century manufacturing firms.

References

1. Cheng K, Harrison DK, Pan PY (1998) An internet basedarchitecture of implementing design and manufacturing agilityfor rolling bearings. J Mater Process Technol 76:96–101

2. Booth R (1996) Agile manufacturing. Eng Manag J April:105–112

3. Jennings NR (1999) Agent–based computing: promise and perils.Proceedings of IJCAI’99, Stockholm, Sweden, pp 1429–1436

4. Jennings NR (2000) On agent–based software engineering.Artif Intell 117:277–296

5. Wooldridge M (1997) Agent–based software engineering. IEEEProc Softw Eng 144(1):26–37

6. Chan FTS, Zhang J, Li P (2003) Agent– and CORBA–basedapplication integration platform for an agile manufacturingenvironment. Int J Adv Manuf Technol 21:460–468

7. Jennings NR, Wooldridge M (2000) Agent–oriented softwareengineering. In: Bradshaw J (ed) Handbook of agent technol-ogy. AAAI/MIT Press, Cambridge, MA

8. Wooldridge M, Jennings NR (1995) Intelligent agents: theoryand practice. Knowl Eng Rev 10(2):15–152

9. Bond AH (1988) Readings on distributed artificial intelligence.Morgan Kaufmann, San Mateo, CA

10. Castelfranchi C (1990) A point missed in multi–agent, DAI andHCI. Decentralised AI, pp 49–62

11. Jennings NR (1993) Commission and conventions: the foun-dation of co–ordination in multi–agent systems. Knowl EngRev 8(3):223–250

12. Gasser L (1991) Social conceptions of knowledge and actions:DAI foundations and open system semantics. Artif Intell 47:107–138

13. Gates WH (1999) Business at the speed of thought, using adigital nervous system. Warner Books, New York

14. Toh KTK, Harding JA (1999) An enterprise modelling CASEtool and data schema requirements for the selection of softwaresupport. Int J Prod Res 37(18):4079–4104

15. Weston RH (1998) Integration infrastructure requirements foragile manufacturing systems. Proc Inst Mech Eng B J EngManuf 212(B6):423–427

16. Cheng K, Pan PY, Harrison DK (2000) The internet as a toolwith application to agile manufacturing: a web–based engineer-ing approach and its implementation issues. Int J Prod Res 38(12):2743–2759

17. Ellsworth JH, Ellsworth MV (1996) The new internet businessbook. Wiley, New York

18. Sycara K, Zeng D (1996) Multi–agent integration of informa-tion gathering and decision support. 12th European Conferenceof Artificial Intelligence, John Wiley and Sons, pp 549–553

19. Rao AS, Georgeff MP (1993) A model–theoretic approach tothe verification of situated reasoning systems. Proceedings ofIJCAI’93, Chambery, France, 28 August–3 September 1993, pp318–324

20. Lang K (1995) Newsweeder: learning to filter netnews.Proceedings of Machine Learning Conference

21. Sycara K, Zeng D (1994) Towards an intelligent electronicsecretary. Proceedings of CIKM’94 (International Conferenceon Information and Knowledge Management); Workshop onIntelligent Information Agents, National Institute of Standardsand Technology, Gaithersburg, MD, December

22. Simon HA (1957) Models of man, social and rational:mathematical essays on rational human behaviour in a socialsetting. Chapman and Hall, London

23. Huang CY, Nof SY (2000) Autonomy and viability–measuresfor agent–based manufacturing systems. Int J Prod Res 38(17):4129–4148

24. Malone TW (1987) Modelling coordination in organization andmarkets. Manage Sci 33:1317–1332

25. Jennings NR (1992) Controlling cooperation problems solvingin industrial multi–agent systems. Knowl Eng Rev 7:19–33

26. Maturana FP, Norrie DH (1995) A generic mediator for multi–agent coordination in a distributed manufacturing system,http://ksi.cpsc.ucalgary.ca/DME/Generic.html, 13 April 1995

27. Maturana FP, Tichý P, Šlechta P, Discenzo F, Staron RJ, Hall K(2004) Distributed multi–agent architecture for autonomoussystems. Expert Syst Appl 26:49–56

28. Cabri G, Leonardi L, Zambonelli F (2001) Coordination ofinfrastructures for mobile agents. Microprocess Microsyst 25:85–92

29. Chan FTS, Zhang J (2002) A multi–agent–based agile shopfloor control system. Int J Adv Manuf Technol 19:764–774

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