multi agent systems in production planning

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Multi-agent systems in production planning andcontrol: an overviewMaria Caridi a & Sergio Cavalieri ba Department of Management , Economics and Industrial Engineering , Milano, Italy E-mail:b Università degli Studi di Bergamo, Department of Industrial Engineering , ItalyPublished online: 21 Feb 2007.

To cite this article: Maria Caridi & Sergio Cavalieri (2004) Multi-agent systems in production planning andcontrol: an overview, Production Planning & Control: The Management of Operations, 15:2, 106-118, DOI:10.1080/09537280410001662556

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Production Planning & Control,Vol. 15, No. 2, March 2004, 106–118

Multi-agent systems in production planningand control: an overview

MARIA CARIDI and SERGIO CAVALIERI

Keywords multi-agent systems, survey, production planningand control

Abstract. The ever fast changes of customers’ needs anddemands ask for reconfigurable and adaptive productionsystems, which can provide companies with the proper levelof agility and effectiveness, without disregarding at the sametime cost factors. In the last decade, a large amount of researchworks on the adoption of multi-agent systems (MAS) in severalindustrial environments has flourished. This approach, unliketraditional centralized or multilevel hierarchical approaches,assumes the presence of several decision-making entities, distrib-uted inside the manufacturing system, interacting and cooperat-ing each other in order to achieve optimal global performance.Aim of this paper is at first to provide readers, which are notexperienced with the multi-agent approach, with some defini-tionsandcategorizationsofthisparadigm.Secondarily,bymakinguse of an extensive database of more than 100 contributions

on this field, authors intend to evaluate how multi-agents sys-tems have really impacted on the industrial practices at anenterprise and at a broader supply chain level. Finally, drivenby the past research experiences of the authors and bythe extensive literature search, considerations and remarks onthe real potential benefits and on the major issues currentlyinhibiting the spread out of this paradigm are reported.

1. Introduction

Manufacturing context is evolving towards global andrelevant changes, due essentially to the ever fast changesof customers’ needs and demand. Highly competitivepressures are pushing manufacturing systems towardsan exasperated reduction of the product lifecycles, leaninventories, high utilization of resources and short lead

Authors: Politecnico di Milano, Department of Management, Economics and IndustrialEngineering, Milano, Italy, E-mail: [email protected] and Sergio Cavalieri, Universitadegli Studi di Bergamo, Department of Industrial Engineering, Italy.

MARIA CARIDI is a researcher in Industrial Production Management at Department ofManagement, Economics and Industrial Engineering of Politecnico di Milano, Italy. She receivedher PhD in Industrial Plants and Production Systems from the University of Parma. Her researchinterests are in different areas of Production Planning and Control: in particular, she has beenstudying different issues concerning materials’ management (e.g. security stocks under uncertainty,managing engineering changes) and the application of Multi-Agent System theory to manufactur-ing systems’ control. Lastly, as regards the Information Systems, she is concerned in how modernAdvanced Planning and Scheduling systems cover manufacturing system requirements and howthey can be effectively integrated with Enterprise Resource Planning systems.

SERGIO CAVALIERI is currently Associate Professor at the Department of Industrial Engineering ofthe University of Bergamo. Graduated in 1994 in Management and Production Engineering,in 1998 he got the PhD title in Management Engineering at the University of Padua. His mainfields of interest are Modelling and Simulation of Manufacturing Systems, Application of Multi-Agent Systems and Soft-computing Techniques (Genetic Algorithms, ANNs, Expert Systems) forOperations and Supply Chain Management. He has been participating to various research projectsat national and international level. He has published two books and about forty papers on nationaland international journals and conference proceedings. He is currently coordinator of the IMSNetwork of Excellence Special Interest Group on Benchmarking of Production Scheduling Systemsand member of the IFAC-TC on Advanced Manufacturing Technology.

Production Planning & Control ISSN 0953–7287 print/ISSN 1366–5871 online # 2004 Taylor & Francis Ltdhttp://www.tandf.co.uk/journals

DOI: 10.1080/09537280410001662556

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times. Manufacturing is irreversibly moving from a massproduction to a mass customization fashion.In order to respond to these requests, production

means need to become reconfigurable and founded onautonomous and intelligent modules, which dynamicallyinteract with each other for the achievement of local andglobal objectives. Production processes must embedadaptivity attributes so to provide a company with therequired level of agility, that is the ability to success ina rapidly changing outer environment. Control systemsshould embed intelligence, flexibility, extensiveness,fault-tolerance and, in order to reduce the amount ofinvestments, reusability (Shen and Norrie 1999, Zhouet al. 1999). Moreover, in order to stand for a globalcompetitiveness and rapid market response, companieshave to abandon their local myopic attitude in favourof integration with other enterprises in terms of com-mon management systems. Collaborative strategies areplacing out traditional antagonistic approaches towardsuppliers or customers. Only through sound partnershipsis it possible to pursue a win-win strategy.From the research side, all these critical factors are

motivating the straining search for a new generation ofadvanced production systems which could guarantee theirfulfilment and, as a result, contribute to the strategicsuccess of today’s companies.In the last years, a large amount of research work on the

use of multi-agent systems (MAS) in different industrialenvironments has been produced. Such models, unlikethe traditional centralized or multilevel hierarchical-basedarchitectures,assumethepresenceof severaldecision-making entities, distributed inside the manufacturingsystem, interacting and cooperating each other in orderto achieve optimal global performance. The hypothesisat the basis of these models is that, from the local auto-nomous and often conflicting behaviours of the singledecision-making units, a global behaviour of the manu-facturing system emerges, coherent with the requestedcharacteristics of reactivity and flexibility.Undoubtedly, the advent of multi-agent systems has

represented in the last decade a real breakthrough inthe world of research, involving researchers and practi-tioners coming from heterogeneous and, often, distantfields. The nature of the single agent and, in a morecomplex fashion, the complexity of interaction amongmore agents has in fact attracted, among others, biolo-gists, game theorists, AI researchers, social scientists andmanagement scientists. Sen (1997) provides an interest-ing historical overview on the various research fieldsinvolved in the MAS work.Aim of this paper is to analyse how this promising

paradigm is being adopted in the industrial practice inreality and, in particular, in the production planning andcontrol area. The analysis has been conducted by making

use of a database comprising more than 100 papers clas-sifiable as focusing on MAS applications. In particular,after a brief taxonomy of terms and definition on MAS(section 2), the paper (section 3) will provide an insighton the current known applications of MAS in the supplychain contexts, and, at the enterprise level, in singleproduction systems. Section 4 will report the analysis ofapplication maturity of the surveyed MAS application.Finally, section 5 will draw some conclusions and raisesome further points of investigation on this research topic.

2. Definitions and categorizations of multi-agentsystems

Since the early 1980’s, a flourish of definitions onmulti-agent systems has been proposed in the literature.For an extensive review of agent theories, architecturesand languages, readers can refer to Wooldridge andJennings (1995).

Treating MAS as a monolithic approach is quite pre-tentious. Rather, MAS features depend on the specificrequirements each application field gives more emphasisto. For this reason, literature reports several proposalsof classification of multi-agent systems, in the attempt tomake a clarification on the different definitions research-ers and practitioners have so far provided.

The most interesting taxonomies of MAS found outin literature can be distinguished on the basis of theirfocus on:

. design specifications – the pioneering work of Decker(1987) is recalled, who distinguishes two maindimensions of classification for DistributedProblem Solving, that are control and communication:the former relates to the cooperation degree amongagents, the coordination among cooperating agentsand the dynamics for reaching coordination; thelatter relates to communication paradigm, semanticcontent and protocol. Later, Tchako (1994) extendsthe Decker’s classification by adding the agent

dimension, which describes the characteristics ofthe agents populating the MAS, e.g. adaptivityand autonomy. Keilman (1995) proposes a clas-sification strongly focused on the central role ofcoordination and communication.

. industrial applications – this is mainly represented bythe research separately carried out by Parunak,Jennings and Woolridge. The former proposesa classification of agents according to their applica-

tion functions and observes that most of MASapplications in manufacturing are related to pro-duction and design (Parunak, 1994). Later, thesame Parunak (1998) provides a more extensivetaxonomy of industrial applications of MAS,

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where the dimension of the maturity of applicationappears for the first time. A different segmentationof the application context is provided by Jenningsand Wooldridge (1998), who distinguish betweenindustrial, commercial, entertainment and medical appli-cations. Lastly, it is worth recalling the taxonomyby Weng and Ren (1996), which is more focused onscheduling applications of MAS.

The proposal by Sachdev et al. (1998) represents abridge among the two streams of taxonomy works, sincetheir classification considers the application dimension –encompassing not only industrial applications buteven entertainment, human – computer interaction, etc. –besides the dimensions of agent, organization andinteraction.In this paper, the taxonomy shown in figure 1 is

adopted, which is instrumental in analysing the featuresof MAS applications. A brief description of MAScategories and some related relevant literature referencesfollow:

Application domain – Each literature reference is classifiedaccording to the enterprise activity (e.g. quotation,design, engineering) modelled by the MAS application.Inside the modelled function, each agent is responsiblefor one or more activities and interacts with other agentsin order to fulfil its tasks. In some literature contribu-tions, the application domain is wider than a specificenterprise activity and encompasses a network of organi-zations. In this case, each agent represents an organiza-tion or a macroprocess inside an organization.Agent – An agent is a decisional unit pursuing its ownobjectives by communicating with other decisionalunits of the system. The main attributes of an agentare: adaptivity, that is its capability to adapt to thedynamic evolution of the environment in order tomaintain its role and pursue its objectives; learning cap-

ability, that is the capability to increment dynamically itsown competences (Keilmann 1995); autonomy and proactive-ness, that is the capability to elaborate internally itsown decision-making strategies and, according to them,decide autonomously which behaviour and actions arecarried out on the outer environment (Steels 1995). Inthe proposed taxonomy for industrial MAS applica-tions, agents are classified according to the role theyplay inside the system: it spans from cost managementagents for the quotation process to warehouse agents fordistribution management.

Control – Distributed decision-making systems are affectedby a complexity of control due to the interdependenciesbetween agents (as effect of the segmentation of the prob-lem). This requires the need to adopt proper coordina-tion mechanisms in order to guarantee a consistencybetween local actions and global objectives of the overallsystem. Coordination mechanisms can be declined in:

. implicit mechanisms, based on behaviour logics ofthe single agents which are exante defined andknown; this can avoid any need of formal commu-nication among agents; typical examples are gametheory (Vamos 1986; Keilmann 1995; Busuioc1996) and the behaviour-based approach (Dorigoet al. 1996);

. explicit or cooperation mechanisms, through whichagents can explicitly express their own intentionsand mutually agree on common action plans; thedegree of cooperation can vary from fully to antag-onistic cooperation (refer to Decker 1987 for moreinsights on cooperation mechanisms); typical exam-ples are the well known contract netmechanism (Smith1980), where the coordination problem is solvedout through a contracting mechanism between asupplier and purchaser agent, and the voting system(Rosenschein and Ephrati 1993), where the coop-eration is reached through a consensual processbased on a voting procedure.

Organization – organization in a MAS results from theway tasks are distributed among the decisional units;the main features of organization are:

. decision-making distribution: it spans from rigidunidirectional control of master/slave organizationto a contracting system among the agents;

. organizational structure: it describes the hierarchi-cal relationship among the agents, which depictsalso the hierarchical relationship among tasksassigned to agents; it spans from centralized organi-zation to heterarchical organization (see figure 2)

Communication – it is a fundamental feature in a MAS,since it enables the explicit coordination among theagents; communication can be classified according to:

. communication vehicle: it is the set of logical-physical structures through which information is

HETERARCHICAL HIERARCHICAL CENTRALIZED

Figure 2. Forms of organizations.

MAS

APPLICATIONDOMAIN

AGENT CONTROL ORGANIZATION COMMUNICATION

Figure 1. Categories of classification.

108 M. Caridi and S. Cavalieri

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interchanged among the agents; examples ofcommunication vehicle are message passing andthe blackboard system (Kuru and Akin 1994,Lefrancois et al. 1996, Kadar et al. 1997);

. protocol: it describes the semantic structure andthe content of the messages exchanged betweenthe agents; first efforts in creating a standardprotocol are the Knowledge Interchange Format(Genesereth and Fikes 1992), with its syntac-tic rules, the Knowledge Query ManipulationLanguage, in its various upgrades (Finin et al.1993), providing a set of semantic performatives,and the COOL protocol proposed by Barbuceanuand Fox (1995).

3. Application of multi-agent systems in themanufacturing context

Literature analysis has led to the identification of morethan 100 contributions dealing with MAS application toproduction planning and control. This section presentsthe results of literature classification on the basis of thetaxonomy described in the previous section. The reviewis mainly finalized to the evaluation of the applicationdomains, the way MAS have been implemented and

their impact on the underlying production systems.Through the description, some relevant examples arealso reported.

3.1. Application domains and role of agents

Table 1 reports the degree of application of MASwith regards to the main application domains: the per-centage of contributions dealing with each domain isreported and the main roles of MAS agents are high-lighted. Finally, the last column highlights some refer-ences to relevant examples available in literature foreach domain. Since most of the contributions are acrossdifferent application domains (e.g. MAS application toscheduling and monitoring), for each domain only theapplications presenting peculiar features have beenreported in the table.

A fertile application is design. Among the contribu-tions related to this specific application domain, it isworth recalling the work by Mori and Cutkosky (1998),which is focused on the development of a MAS for thedesign of electronic board subassemblies. Ozawa et al.(2000) offer another interesting application of concurrentengineering of electromechanical products, where oneof the critical issues resides on the strong need of

Table 1. MAS application fields.

Application domainSpread(%) Role of agents Sources

Order quotation 5 Cost management agents Balasubramanian and Norrie (1996),Parsons et al. (1999)

Design 13 Design agents, Geometricinterface agents, Feature agents

Frost and Cutkosky (1996), Bohez andLimsombutanan (1997), Deshmukh andMiddelkoop (1998), Mori and Cutkosky (1998),Vidal (1998), Ozawa et al. (2000)

Engineering 6 Process design agents,Manufacturing design agents

Muir et al. (1997), Brown et al. (1998),Gowdy and Rizzi (1999)

Demand forecast 5 Sales agents, Marketing agents Parunak (1998), Baker (1996)

Order management 7 Order agents, Order holon Bongaerts and Valckenaers (1995),Papaioannou and Edwards (1998)

Master productionSchedule

6 Production planner agents Maturana et al. (1997), Gupta et al. (1998),Wang and Paredis (1999)

Material requirementsplanning

9 Production planner agents Kanchanasevee and Biswas (1997),Sikora and Shaw (1997)

Scheduling 20 Scheduler agents,Dispatching agents

Baker (1992), Daouas et al. (1995),Maturana and Norrie (1995), Saad et al. (1995),Tharumarajah and Bemelman (1997)

Purchasing 7 Order agents, Purchase orderagents, Supplier agents

Kouba and Lhotska (1998)

Monitoring 17 Controller agents, Monitor agents,Quality control holon

Lin and Solberg (1992), Liu and Sycara (1996),Heikkila et al. (1997), Parunak (1998),Fraile et al. (1999)

Distribution 5 Inventory storage agents,Warehouse holon

Fisher and Muller (1995)


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coordination between mechanical and electronic depart-ments in order to anticipate design infeasibilities. Thework of Deshmukh and Middelkoop (1998) is specificallyapplied to highly sophisticated products.As regards the production scheduling and monitoring

field, further results of classification are reported in table2 with an insight on the kind of production systems.Discrete Manufacturing – Most of the applications are

directed towards discrete manufacturing productionand, in particular, the fabrication domain. Unfortu-nately, most of the reviewed papers, classified as ‘genericshop-floor systems’, do not explicitly indicate the specificapplication environment, since they are mainly focusedto the model definition or to a specific description ofalgorithms at a theoretical state.The statistics confirm that research efforts are mainly

addressed to production systems with non-linear flowsand high workload, as job-shops and assembly shops.Among the most interesting works, it is worth recallingthe study of Baker (1992), which reports a MAS appli-cation to the Small Parts Shop at GE Power GenerationBusiness. Another interesting application to schedulingand monitoring is in Liu and Sycara (1996), whereagents are completely delegated with some schedulingtasks of production jobs.MAS flexibility and effectiveness has been investigated

by many other applications: scheduling and monitoringof a generic shop floor (Lin and Solberg 1992; Maturanaand Norrie 1995; Tharumarajah and Bemelman 1997);flow shop scheduling (Daouas et al. 1995), which com-bines the multi-agent technique with simulated anneal-ing; flexible manual assembly line design (Sprumont andMuller 1997), whose aim is to determine functional specif-ications of components and the least expensive organi-zational structure of an assembly line. Moreover, themulti-agent paradigm appears particularly suitable forinduction engine assembly (Kanchanasevee and Biswas1997) or for scheduling of ships assembly (Choi and Park1997): they are in fact examples of low volume produc-tion and wide product range, which highlight the lack of

flexibility of centralized management. Another interest-ing application to fabrication and assembly is in Sikoraand Shaw (1997), where agents coordinate automatedand manual lines in printed circuits manufacturing.

Finally, it is worth recalling the experiences withthe Minifactory concept (Gowdy and Rizzi 1999).Minifactory is a miniature of an assembly system,which is modular and highly sophisticated, based on aprecise integration between hardware and softwareapplications. The integration is nowadays possible bybuilding an architecture of mechanical and computa-tional agents, which are aware of their capabilities andof the role each of them plays inside the assembly system.They cooperate each other through a peer-to-peernegotiation mechanism.

Continuous processes – Multi-agent paradigm has beenapplied also to continuous processes. It is remarkablethe control system for air supplying to a painting shopdeveloped for the General Motors assembly plant in FortWayne (Parunak 1998): each humidifier, burner, steamgenerator is controlled by an agent which is responsivelyautonomous and reacts to different environment con-figurations. The benefits of this application are: paintsaving, thanks to the lower number of colour setups,40% reduction of software control, setup time reductionand system managing simplification.

Other works deal with applications to semiconductormanufacturing (Parunak 1998), mould designing forplastic injection print (Vidal 1998), sheet metal cuttingand paper cutting design and scheduling (Parunak1998). Gupta et al. (1998) present an interesting applica-tion of distributed artificial intelligence to the planning ofautomated process for sheet metal bending: each compo-nent of the sheet metal bending press-brake is controlledby a specialized agent; the distributed architecture allowsembedding the specific knowledge of each agent in aseparate module and utilizing different problem solvingtechniques and system representations for each module.The modularity of the architecture simplifies the systemupdating as consequence of possible changes, since only

Table 2. Typologies of production systems and frequency of MAS application.

Production system Spread (%)

Discrete manufacturing 94Fabrication Job shop 9 52

Lines 3Flow shop 2FMS 3Generic shop floor 35

Assembly Manual assembly lines 1 42Automated lines 2Generic assembly shop 36Minifactory 3


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the involved modules are updated. The coordinationamong the specialized agents is obtained throughconstraint sharing.Supply chain level – Traditionally, MAS have been

applied at enterprise level in order to solve issues addres-sing specific functional needs or involving a specificdecisional-making activity. Then, coherently with thenew managerial practices consolidated in the 1990s,which have shifted the competitive edge to a process-based organization and to an integrated perspective,MAS approaches have flourished in supporting supplychain management and, in general, in aiding decision-making activities among external organizations. As thewell known MIT Beer Game (Sterman 1989) shows, theinterdependencies between the single tiers affect the over-all outcome of the logistics chain from the final retailerto the manufacturer (bull-whip effect). So, supply chainmanagement does not strive for internal efficiency ofoperations (as logistics aimed in the past), but ratherfor the management and coordination of the activitiesthroughout the whole supply chain.As Hinkkannen et al. (1997) and Strader et al. (1998)

maintain, the Supply Chain model naturally suggests thedecomposition approach that, in turn, allows for thedesign of a multi-agent organization. Within an organi-zation, agents can support human decision-makersin monitoring and controlling time consuming andhighly computing activities, as for example inventorymanagement, and assisting them in sending out ordersor carrying out negotiation activities without the needfor unwieldy centralized or top-down managementschemes. This would relieve humans from routinaryand programmable tasks.Various proposals dealing with two or more logistics

tiers are retrievable in literature. Among the mostremarkable, the ISCM (Integrated Supply ChainManagement) agent-based model (Fox et al. 1993) canbe considered one of the pioneers in this context. TheISCM is composed of a set of cooperating agents,where each agent performs one or more supply chainmanagement functions, and coordinates its decisionswith other relevant agents. Agents are expected to per-form different roles; in particular, functional agents areentitled to manage the relationships with the downstreamcustomers (by acquiring and managing orders) and tocarry out all the subsequent related tasks, starting fromre-supply orders to the production and transportationplanning; information agents support functional agentsby providing information and communication services.Sauter and Parunak (1999) propose the ANTS (AgentNetwork for Task Scheduling) architecture that decom-poses each firm into a fictitious miniaturized supplychain, made up of producers and consumers. As a result,the interfaces between agents within a firm are the same

as those among the firms inside the real supply chain; theresult is that the integration among the firms becomesmore natural.

Strader et al. (1998) develop a multi-agent simulationplatform, which supports decision-making of supplychain managers. Their model is used to study the impactof information sharing on order fulfilment in divergentassembly supply chain; their main conclusion is thatthrough information sharing among actors, uncertaintycan be reduced thus decreasing overall inventory costs.

Several other multi-agent architectures for supplychain management have been proposed in literature;here are recalled: MASCOT (Multi-Agent Supply ChainCoordination Tool), based on a blackboard commu-nication paradigm, whose aim is to support supply chainkey functionalities (Sadeh et al. 1999); the supply chaindynamics modelling approach based on software compo-nents, proposed by Swaminathan et al. (1998); the appli-cation of agent technology to decision support systemsfor supply chain real-time management, proposed byHinkkanen et al. (1997).

3.2. Organization and control

Table 3 reports the diffusion of different agent archi-tectures into the analysed literature.

Heterarchical architecture – In heterarchical architectures,no hierarchical relationship among agents takes place.It is worth recalling the well-known model by Lin andSolberg (1994), who propose a heterarchical architecturefor adaptive scheduling and monitoring in a dynamicmanufacturing environment. Heterarchical architectureshave been applied in several other works: a market-driven approach for planning and control (Baker 1992);the above-introduced Minifactory system (Gowdy andRizzi 1999); scheduling of job shops (Saad et al. 1995),generic shop floor (Tharumarajah and Bemelman 1997,Krothapalli and Deshmukh 1999), flow shop (Daouaset al. 1995); control systems for multimanipulation assem-bly (Fraile et al. 1999); the above-introduced ISCM(Integrated Supply Chain Management) by Fox et al.(1993); the organization inside the design agents teamfor ship manufacturing proposed by Choi and Park(1997). Literature review shows that, though multi-agent systems based on heterarchical architectures arethe most widespread, they hardly turn out at prototypicalor production phase. This is expression of the fact thattheir level of maturity is quite low. Industry is still faraway from the idea of realizing completely distributedsystems, with loose or null connections among the auton-omous agents. Moreover, this kind of architecture is char-acterized by communication overload and, consequently,


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high implementation costs, being the overall optimumnot guaranteed.Heterarchical architecture with coordinators – In this type

of architecture, even if no hierarchical relationshipamong the agents takes place, there are particular agents(e.g. facilitators, mediators, brokers) which help coordi-nation and communication among agents and settle pos-sible disputes in order to assure system stability. Anexample of heterachical architecture with mediators isMetaMorph II (Shehory and Kraus 1998), whose aimis the integration of a company’s operations (e.g. design,planning, scheduling, execution, distribution) with theones of its suppliers, customers and partners, in an openand distributed system: it is an hybrid architecture,whose higher level is made up of different interconnectedsubsystems, which are integrated to the main system viainternet/intranet through mediators. In ANTS architec-ture, presented by Sauter and Parunak (1999) for supplychain management, coordinators play the role of brokers.Brokers are also modelled in Frost and Cutkosky (1996),where they represent the interface between systemagents and service agents. In Maturana and Norrie(1995), mediators are organized in a distributed structurefor supporting and coordinating system activities,whereas in Sun et al. (1999) they facilitate planningand scheduling process.Hierarchical architectures – In hierarchical architectures,

lower levels depend on higher levels, which completelyor partially control them. An example of this type ofarchitecture is provided by the multi-agent system forplanning resources allocation in a manufacturingenvironment, proposed by Bastos and Oliveira (1998).Keilmann and Conen (1996) present an hierarchicalarchitecture, where each agent task is decomposed intasks of lower level agents. The PROCURA model(Project Management Model of Concurrent Planning

and Design) integrates tactical and execution planningthrough a top-down hierarchical approach (Golfarelliand Maio 1997).

Modified hierarchical architectures – In spite of the highdegree of autonomy of each agent, these forms of archi-tecture preserve a hierarchical level in order to guaranteesystem stability. Kouiss and Pierreval (1997) propose amodified hierarchical architecture for dynamic schedul-ing in a FMS for real-time job allocation to resources: theallocation depends on the shop floor status (e.g. machinebreakdowns, resource availability, bottleneck position)and on manufacturing objectives (e.g. WIP reduction,lateness minimization). In Fisher et al. (1993) a ship-ping company agent allocates transportation orders totrucks agents, in compliance with customer requirements,and it can cooperate or compete with other ship com-pany agents for the acquisition of transportation orders.Park et al. (1994) presents an architecture for the con-current design of industrial cables, where system decom-position reflects the hierarchical approach, since fourperipheral agents, each endowed with specific tasksand a certain degree of autonomy, are interfaced with acentral node.

Holonic architecture – In holonic architectures, the dis-tributed system is made up of holons which dynamicallyadapt to the life cycle of the manufacturing system(Bongaerts and Valckenaers 1995). Holonic manufactur-ing systems summarize the best properties of hierarchicaland heterarchical ones: high quality and predictability ofresults, soundness to possible disturbs. They have beenapplied to planning and scheduling of an assembly shop(Biswas et al. 1995), to scheduling and monitoring of ageneric shop floor (Zhang and Norrie 1999), to engineassembly scheduling (Kanchanasevee and Biswas 1997),for managing and coordinating manufacturing activities(Deen 1994).

Table 3. Architectures in surveyed MAS.

Multiagent architecture Spread (%) Sources

Heterarchical 48 Baker (1992), Fox et al. (1993), Lin and Solberg (1994),Daouas et al. (1995), Saad et al. (1995),Choi and Park (1997), Tharumarajah and Bemelman (1997),Fraile et al. (1999), Gowdy and Rizzi (1999),Krothapalli and Deshmukh (1999),

Heterarchical with coordinators 26 Maturana and Norrie (1995), Frost and Cutkosky (1996),Shehory and Kraus (1998), Sauter and Parunak (1999),Sun et al. (1999)

Hierarchical 4 Keilmann and Conen (1996), Golfarelli and Maio (1997),Bastos and Oliveira (1998)

Modified hierarchical 12 Fisher et al. (1993), Park et al. (1994),Kouiss and Pierreval (1997)

Holonic 10 Deen (1994), Biswas et al. (1995), Kanchanasevee andBiswas (1997), Zhang and Norrie (1999)


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3.3. Communication

Table 4 reports the level of diffusion of the two maincommunications paradigms (i.e. message passing andblackboard) being adopted in the reviewed works.Communication is one of the most relevant featureswhen developing a multi-agent system. It aims at updat-ing in real-time the agents about the evolutions of envi-ronment, so that agents can promptly react. Moreover,it supports the agent’s forecast capability, so that uncer-tainty is reduced. Finally, it enables agents to haveknowledge about other agents’ behaviour and tocooperate in order to pursue agent’s objectives.Communication can be classified according to the

paradigm and the protocol. The paradigm defines theway the communication takes place (i.e. shared globalmemory or blackboard, and message passing). The pro-tocol specifies: the structure of the dialogue among theagents (i.e. reactive protocol, voting protocol, contractnet, constraint propagation, speech acts), the form ofaddressee selecting (selective communication, multicastcommunication, broadcast communication); finally, ata higher level, the semantic structure and the contentof exchanged messages.All the above-stated communication components are

variously combined in the surveyed applications. Themost adopted communication paradigm is messagepassing, with contract net as explicit mechanism.In MASCOT (Sadeh et al. 1999), communication is

based on the blackboard paradigm: it is an effectivemeans for integrating multiple sources of knowledge; infact it allows to embed the problem solving knowledgeof different knowledge sources, which develop solutionsto problems by communicating through a layered black-board; each layer corresponds to a specific ‘context’, thatis a particular status of the environment (e.g. productionorders to be planned or scheduled, available resources,

agreements with suppliers). Other interesting examples ofa blackboard paradigm are available in Liu and Sycara(1996), Vidal (1998) and Fraile et al. (1999).

As for message passing, the work by Saad et al. (1995)is recalled, dealing with the job-shop dynamic schedul-ing: in this application, agents do not share a fix memorylocation where communication is stored, on the contrarythey send and receive messages according to severalforms; the contract net protocol is utilized in this case.Applications of message passing and contract net proto-col can be found in Lin and Solberg (1994), the marketdriven system by Baker (1992), the distributed resourceallocation by Bastos and Oliveira (1998), the holonicmanufacturing system by Kanchanasevee and Biswas(1997).

Other implemented protocols are: the voting schemefor communication among agents for scheduling ofresources in a semiconductor fabrication (Parunak1998); the speech acts protocol in the ISCM by Foxet al. (1993); the constraint propagation in the concurrentengineering application by Petrie (1997) and in Sachdevet al. (1998); the reactive protocol of the Minifactoryassembly system (Gowdy and Rizzi 1999) and of theCASPER project (Sohier et al. 1998).

4. Maturity degree of surveyed MAS applications

In this section, the maturity degree of multi-agentsystem applications found in literature is analysed. Theaim is to identify the application domains where actual(not emulated) MAS applications provide better perfor-mances in comparison with the traditional approach.

The surveyed literature suggests that the MAS modelsthat are operatively utilized by a company (‘Production’column in Table 5) or have been translated into a com-mercial product (‘Product’ column in Table 5) are few,

Table 4. Communication paradigms and protocols in surveyed MAS.

Communication

Paradigm Protocol Spread (%) Sources

Message passing Reactive protocol 8 86 Sohier et al. (1998), Gowdy andRizzi (1999)

Voting protocol 2 Parunak (1998)Contract net 53 Baker (1992), Lin and Solberg (1994),

Saad et al. (1995), Kanchanasevee andBiswas (1997), Bastos and Oliveira (1998)

Constraint propagation 1 Petrie (1997), Sachdev et al. (1998)Speech Acts 6 Fox et al. (1993)Not specified 16 –

Blackboard 14 13 Liu and Sycara (1996), Vidal (1998),Fraile et al. (1999), Sadeh et al. (1999)


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whereas most of the applications are at an emulatedstage. Notice that the most mature applications(‘product’ stage) apply to scheduling and monitoring:in particular, they apply to the scheduling of continuousprocesses (GM paint shop, and paper and steel mill,in Parunak (1998)) and to transfer lines monitoring(Zone Logic, in Parunak 1998).Among the applications in the ‘production’ stage,

Sadeh et al. (1999) is recalled where a managementsystem for a generic supply chain is presented; Parunak(1998) reports several applications such as: AMROSE,for the scheduling of the assembly of ocean-going vessels;ADS (Autonomous Distributed System) for sheet steelprocessing lines control; Daewoo Scheduling Systemfor the press shop at Daewoo Motors; LMS (LogisticsManagement System) for tool managing in semicon-ductor fabrication.Promising applications relate to the design domain,

although they are still at an ‘emulated’, ‘prototype’ or‘pilot’ stage. As far the other application domainsare concerned, most of the MAS remain at a ‘modelled’stage (Deen 1994, Golfarelli and Maio 1997,Swaminathan et al. 1998, Bastos and Oliveira 1998) or‘emulated’ stage (Lin and Solberg 1994, Heikkila et al.1997, Kouiss and Pierreval 1997, Papaioannou andEdwards 1998, Baker et al. 2001) or ‘prototype’ stage(Balasubramanian and Norrie 1996, Hollis and Quaid1995, Sprumont and Muller 1997, Valckenaers et al.1997, Barbuceanu et al. 1999).

5. Final remarks

Given the flourish of proposals of MAS-based modelsin heterogeneous application fields, the literature surveyreported in this paper, though thorough and extensive,

cannot certainly aim to be comprehensive. During the1990’s, the multi-agent approach has been a fashionabletopic, where numerous researchers have contributed inthe common effort to derive industrial applications.

However, despite the density of efforts and projectscarried out, there is still no clear understanding whereand how multi-agent systems can provide better resultsthan ‘traditional’ models. Authors often dwell on thetheoretical description of design hypotheses and struc-tural characteristics, but do not provide satisfactory indi-cations on their level of applicability. As a result, it isevident that without giving a clear answer to this funda-mental question, the technology gap between researchand industrial application would dramatically widen.

From the present perspective, the MAS paradigm ischaracterized by some general properties which, givena specific context where to be applied, may be inhibitingor, vice versa, enhancing their applicability.

As for the strength points, five basic features can beidentified: multi-agent systems are suitable for applica-tions which are modular, decentralized, complex, timevarying, ill-structured (Parunak 1998).

. Modularity allows the system to be modified, mod-ule by module, so that reconfiguration costs aredrastically reduced and system reusability increases.

. Decentralisation minimizes the impact of local mod-ifications on other system modules. In fact, in adecentralized system, the behaviour of a single mod-ule influences only the modules that are interactingwith it, whereas the remaining part of the system isnot affected. This feature is important when dealingwith production systems characterized by a phy-sical distribution of production and logistic unitsand often affected by local disturbs (e.g. machinebreakdown, material shortage) which require localre-planning.

Table 5. Degree of maturity of MAS applications classified according to the application domain.

Degree of application’s maturity

Application domainModelled

(%)Emulated

(%)Prototype

(%)Pilot(%)

Production(%)

Product(%)

Total(%)

Order quotation 1 2 2 – – – 5Design 2 4 5 1 1 – 13Engineering 1 3 2 – – – 6Demand forecast 1 2 1 – 1 – 5Order management 1 3 3 – – – 7Master production Schedule 1 3 1 – 1 – 6Material requirements planning 1 3 3 1 1 – 9Scheduling 2 10 5 1 1 1 20Purchasing 1 3 2 – 1 – 7Monitoring 2 8 4 – 1 2 17Distribution 1 3 1 – – – 5

Total 14 44 29 3 7 3 100


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. The capability to embed multiobjective functionsand multiple constraints and variables to be con-trolled provides a reasonable trade-off in approach-ing complex problem solving environments.

. The multi-agent concept allows to effectively modeltime-varying physical systems. This is a very impor-tant feature when applied to production systems,which frequently modify their configuration, dueto market requirements or to internal resourcesendowment.

. Finally, when designing a new production system,not all the requirements are available at the begin-ning of the design phase: for instance, which entitieshave to communicate, how the interfaces among thecommunicating entities should be designed. As aconsequence, the designed system encounters therisk of resulting ill-structure when all requirementsare clearly stated, which implies extra costs anddelays in project release. Multi-agent systems cancontribute in avoiding these pitfalls. Agents mayinteract with agents endowed with the role ofmodifying the environment within ranges whichcan be managed by the other agents.

On the contrary, as regards the critical issues inhibit-ing a widespread application of multi-agent systems inproduction and control domain, the following can berecalled.

. First of all, agent-based problem-solving does notalways succeed in optimally solving a problem andmay result computationally unstable, that is it maynot reach a feasible solution within a given compu-tational time.

. MAS approach fails in modelling physical systemsthat cannot be decomposed into sub-problemsand subobjectives. In order to quantify agent-based model exposition to the above-stated limita-tions, it is necessary to fairly design appropriate teststo measure the quality of system performance.

. Agent-based systems require large investment inmonitor equipment and support equipment. Infact, testing and tuning this equipment result hardand expensive. Traditionally, simulation is utilizedin order to test MAS under various operative con-ditions; unfortunately, simulation experiments can-not cover large ranges of operative conditions (aslarge as those that will actually stress the systemduring the real utilization) without underminingthe computational efficiency of the test.

In the opinion of the authors, when specifically dealingwith application of multi-agent systems to productionplanning and control, nowadays the trade-off betweenpros and cons of MAS applications is unbalanced

towards the cons: the high investment and the risk relatedto system effectiveness still act as a disincentive to thedevelopment of real industrial applications based onthis paradigm. Moreover, it is observed that industrialcompanies and software houses are not yet receptive:the few applications referenced in literature are mainlyspecific outcomes of research programmes, with a certaindifficulty to be generally extended to a wider industrialcommunity in the form of a commercial on-the-shelfsoftware.

Considering the wide experiences carried out in theseyears by the research community, multi-agent approachcan turn out to be effective in all those fields where muchof the efforts and time are spent in carrying out collab-oration tasks among a definite and limited numberof actors. This is typical of processes like ConcurrentEngineering (Wheelwright and Clark 1992), SCEM(Supply Chain Event Management) (Marabotti 2002)and CPFR (Collaborative Planning Forecasting andReplenishment) (Vics 2002), where decision-makingactivities are naturally distributed among more partners.In such environments, it is more feasible to elicit theknowledge bases of the single actors and transfer theminto automatic decision-making activities which canrelieve humans from carrying out routinary tasks, bysupporting collaboration, providing high-speed comput-ing and guaranteeing a solution convergence.

On the contrary, whereas the problem is hardlydecomposable or it is quite difficult to provide agentswith knowledge representation (since no direct elicitationfrom humans to agents is possible), some critical issuescome out, reducing the chance to evolve from conceptual(or emulated) models to industrial products. This is thecase of multi-agent systems applied to job-shop schedul-ing which, despite the wide spectrum of proposals avail-able in literature, have not yet turned out in an industrialphase. Two main factors affect their applicability on thisdomain:

. the extensive number of agents required – in thecase of heterarchical models equal to the numberof jobs and resources populating the productionsystems – which can strongly inhibit a convergenceof the solution and a computational efficiency;

. the high dependence of the design parameters ofa MAS-based scheduler on the production systemand scenarios to be controlled. It is still missing asystematic and quantitative analysis demonstratingthat, given a production system with specific fea-tures (e.g. plant lay-out, process routings, loadingdistributions, . . .) it is possible to identify whichmulti-agent architecture and which set of rules ofprotocols outperform other competing (distributedor not) control algorithms.


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One possible solution for addressing better which pro-duction domains can benefit from the application of adistributed decision-making approach is the developmentof a benchmarking service (Cavalieri et al. 2000, Cavalieriand Macchi 2001). The service will allow carrying out amutual comparison of novel and traditional approachesunder different production systems and a variety ofmanufacturing scenarios. Such a benchmarking serviceis one of the main activities currently developed underthe framework of the IMS-Network of Excellence (IMSNoE 2002), actually funded by the EuropeanCommission.

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