Dynamic self-organization in holonic multi-agent manufacturing systems: The ADACOR evolution

Dynamic

self-organization

in

holonic

multi-agent

manufacturing

systems:

The

ADACOR

evolution

Jose´ Barbosa

a,c,d,

*,

Paulo

Leita˜o

a,b

,

Emmanuel

Adam

c,e

,

Damien

Trentesaux

c,d

a_{PolytechnicInstituteofBraganc¸a,CampusStaApolo´nia,Apartado1134,5301-857Braganc¸a,Portugal} b

LIACC–ArtificialIntelligenceandComputerScienceLaboratory,R.CampoAlegre102,4169-007Porto,Portugal

c

Univ.LilleNorddeFrance,F-59000Lille,France

d

UVHC,TEMPOResearchCenter,F-59313Valenciennes,France

e

UVHC,LAMIH,F-59313Valenciennes,France

1. Introduction

Nowadays, manufacturingsystems faceunprecedented chal-lenges imposed by highly demanding constraintsranging from highproductcustomizationtothedemandforlowerpricesaswell asincreasingproductqualitytosignificantfluctuationsinmarket demands[1].Currentmanufacturingsystems,basedontraditional hierarchicalandrigidcontrolstructures,cannolongercopewith thesedemanding constraints[2,3], and requirethe useof new manufacturingparadigmsthatmeet theseconstraintsbetter.In

thissense,torespondmorequicklyandefficiently,manufacturing systemsareshiftingtonovelparadigmscomposedoffeaturessuch as modularity, scalability, reconfigurability, robustness and flexibility,tonameafew.Despitethebenefitsthatthesefeatures bring,newcontrolstructuresalsoneedtobedevelopedinparallel andallthepotentialextractedfromthemanufacturingparadigms. Traditionally,manufacturingcontrolsystemsusehierarchical controlstructureswhich concentratetheprocessingpower ofa shop-floorcontrolunderonecentralnode.Theyimprove perfor-manceandoptimization,butrespondinadequatelytochangesin conditions,scalabilityandunpredictability.Thesemonolithic,rigid control structures are insufficientto meet thecurrent require-mentsimposedby manufacturingenvironmentswhichdemand flexibility,robustness,reconfigurabilityandresponsiveness.New manufacturing paradigms have thus emerged of which the commondenominatoristhedecentralizationanddistributionof processing power over several entities,but witha decrease in

ARTICLE INFO Articlehistory:

Received12October2013

Receivedinrevisedform29October2014 Accepted29October2014

Availableonline20November2014 Keywords:

Holonicmanufacturingsystems Multi-agentsystems

Evolutionarysystems Self-organization

ABSTRACT

Nowadays,systemsarebecomingincreasinglycomplex,mainlyduetoanexponentialincreaseinthe numberofentitiesandtheirinterconnections.Examplesofthesecomplexsystemscanbefoundin manufacturing,smart-grids,trafficcontrol,logistics,economicsandbiology,amongothers.Duetothis complexity,particularlyinmanufacturing,alackofresponsivenessincopingwithdemandforhigher quality products, the drastic reduction in productlifecycles and the increasing need forproduct customizationarebeingobserved.Traditionalsolutions,basedoncentralmonolithiccontrolstructures, arebecomingobsoleteastheyarenotsuitableforreactingandadaptingtotheseperturbations.The decentralizationofthecomplexityproblemthroughsimple,intelligentandautonomousentities,suchas thosefoundinmulti-agentsystems,isseenasasuitablemethodologyfortacklingthischallengein industrialscenarios.Additionally,theuseofbiologicallyinspiredself-organizationconceptshasproved tobesuitableforbeingembeddedintheseapproachesenablingbetterperformancestobeachieved. Accordingtotheseprincipals,severalapproacheshavebeenproposedbutnonecanbetrulyembedded andextractallthepotentialofself-organizationmechanisms.Thispaperproposesanevolutiontothe ADACORholoniccontrolarchitectureinspiredbybiologicalandevolutionarytheories.Inparticular,a two-dimensional self-organizationmechanismwasdesigned taking thebehavioural andstructural vectorsintoconsideration,thusallowingtrulyevolutionaryandreconfigurablesystemstobeachieved thatcancopewithemergentrequirements.Theapproachproposedisvalidatedwithtwosimulationuse cases.

ß2014ElsevierB.V.Allrightsreserved.

* Corresponding author at: Polytechnic Institute of Braganc¸a, Campus Sta Apolo´nia,Apartado1134,5301-857Braganc¸a,Portugal.Tel.:+351918985512.

E-mailaddresses:jbarbosa@ipb.pt(J.Barbosa),pleitao@ipb.pt(P.Leita˜o),

emmanuel.adam@univ-valenciennes.fr(E.Adam),

damien.trentesaux@univ-valenciennes.fr(D.Trentesaux).

ContentslistsavailableatScienceDirect

Computers

in

Industry

j ou rna l h ome p a ge : w ww . e l se v i e r. co m/ l oc a te / c om pi nd

http://dx.doi.org/10.1016/j.compind.2014.10.011

systemperformanceregardingprocessoptimization.Examplesof suchparadigmsareReconfigurableManufacturingSystems(RMS)

[4],Multi-agentSystems(MAS)[5],BionicManufacturingSystems (BMS), Holonic Manufacturing Systems (HMS) [6], and more recently,EvolvableProductionSystems(EPS)[7].

RMSisaconceptthatsuggeststherapidchangeinthefactory’s structureusingchangesinhardwareand/orsoftwaretoadjustthe productioncapacityandfunctionalityquickly[8].ARMSsystem should exhibit the following characteristics [4]: modularity, integrability,customization, convertibilityand diagnosability.A MAS[5,9]isbothaparadigmandtechnologythatadvocatesthe designofsystemsbasedonsocietiesofdecentralized,distributed, autonomous and intelligent entities, called agents. In such systems,eachagenthasapartialviewofthesurroundingworld andmustthereforecooperatewithothersinordertoachievethe globalobjectives;thebehaviouroftheglobalsystememergesfrom the cooperation between individual agents. An HMS [6] is a paradigmthat translates the concepts of living organisms and socialorganizationsdevelopedbyKoestler[10]tothe manufactur-ingworld.Aholon,asKoestlercoinedtheterm,isanidentifiable partofasystemthathasauniqueidentity,yetismadeupof sub-ordinatepartsandisinturnpartofalargerwhole.Koestleralso definesthe termholarchy asa hierarchically organized system populatedwithself-regulatingholons,and thesystemgoalsare achieved by the cooperation between holons. An HMS is the encapsulationoftheentiremanufacturingsysteminaholarchy. Theholonscanrepresentphysicalresourcesandlogicentities.

MAStechnologyand/orHMSconceptshavebeensuccessfully developedandappliedtodifferentdomains(seeforexamplethe reviews (Leitao et al., 2013; [11])). MetaMorph [12] and its successorMetaMorphII[13]wereprojectsthatfirstlyaimedto provideanagent-basedapproach forthecreationand manage-mentofagentcommunitiesin distributedmanufacturing envir-onments,andsecondlytointegratecross-enterpriseactivitiessuch asdesign,planningandscheduling.AARIA(AutonomousAgentsat RockIslandArsenal) wasdevelopedintheearlyyearsof agent-basedarchitecturesformilitaryproduction,withtheparticularity ofusinginternetasameansofcommunicationbetween agents

[14]. One of the best-known practical implementations using multi-agentsystemsis probablyon oneoftheDaimlerChrysler production lines ([15]; [16]). This architectureaimed at using agent technology for both dynamic and flexible transportation systemsandcontrolsystems.

Oneof themostremarkableHMSarchitecturesis thePROSA (Product-Resource-Order-StaffArchitecture)referencearchitecture that defines the main guidelines for developing a generic manufacturingcontrollayer[17].ArealapplicationofPROSAwas conductedat Cambridge Universityusinga packaging cell[18], wherea collaborationwas formed between order and resource holons to accommodate clients’ demands. Order holons use negotiationtechniquestoensurefastandreliableproductionand arealsoresponsiblefortrackingproductionprogress.Ontheother hand,themainaimofresourceholonsistomaximizethereturnon theexecutionoftheirservices,andfinally,productholondealwith thebuyingandsellingofgoods.

The ADACOR (ADAptive holonic COntrol aRchitecture for distributedmanufacturingsystems)[19]isaholonicarchitecture thatproposesanadaptiveproductioncontrolapproachbalances from a stationary state to a transient state, in normal and unexpected conditions, respectively, combining the benefits of hierarchicalandheterarchicalcontrolstructuresusinganadaptive mechanism.Complementarily,a deeperandup-to-date descrip-tionofthecurrentholonicandmulti-agentsystemssolutionscan befound in the surveys of [20–22,23], where a more detailed description of the aforementioned and other not mentioned solutionsismade.

Tosumup,theexistenceofonecentralnode controllingthe low-level entities in the hierarchical approaches constitutes a drawbackinthesensethatifitfailsthewholesystemmayfail.On theotherhand,decentralizedsystems,suchasthoseelaborated using MAS and HMS concepts, respond bettertoperturbations where the failure of an isolatedentity only affects partof the system,andtheotherpartscancontinueoperatingwithnomajor impact.Despitethebenefitsshown,decentralizedsystemsdonot attainoptimizationlevelsashighasthosedepictedbyhierarchical solutions.AsillustratedinFig.1,undernormalconditions,system performance of hierarchical architectures is better than heter-archicalarchitectures.However,inunexpectedsituations,e.g.due to a resource malfunction or a rush order, the heterarchical architecturesbehavebetterastheyareabletorespondpromptlyto perturbations.

Essentially,thechallengeistocombinethebestofbothworlds, where asystemdisplaystheoptimizationlevelsof hierarchical systemsundernormalconditionsandbehaveslikeheterarchical approachesinunexpectedsituations.Anapproachlikethisbrings hierarchicalfeaturestodistributedentitieswhilstretainingtheir autonomy. For this purpose, some hybrid solutions have been developedexhibitingha(t)behaviourillustratedinFig.1.ADACOR

[19]isawell-knownexampleofsuchanapproachasitconsiders anadaptiveproductioncontrolmechanismthatbalancesbetween two states: a hierarchical stationary state and a heterarchical transientstate.Inspiteoftheimportantprogressmadeinthisfield, furtherdevelopmentsmustbemadetoachieveatrulydynamic andevolvablesystemthatisabletocopewithsystemconstraints, withoutsignificantlyaffectingitsfunctioning.

Biologyandnature,aswellaschaosandevolutionarytheories aresuitablesourcesofinspirationtodesignanddevelopsolutions forsolvingcomplex,large-scaleproblemsaimedatincreasingtheir potentialbyembeddingemergentconceptssuchas self-organiza-tion[24].Oneexampleistheuseofself-organizationprinciples, whichcanbedescribedastheabilityofasystemtoarrangeitself autonomously andspontaneously,mainlydue tointernal inter-actions, and without the need to use a central authority

[25]. Amongthe most known biological sources of inspiration, onecanfindfoodforagingofants[26]orfoodforagingofbees[27]

aswellasfishschoolingorbirds’flyingpattern[28].

Some approacheshave alreadytried touseself-organization conceptsasawaytocopewiththecomplexityand unpredictabili-tyassociatedwithdisturbances thatmayappearinthesystem. Someexamples:thePROSAarchitecturewasextendedusingfood foragingbehaviourofantsasaforecastingmethodology[29];the P2000+[15]usedavirtualbuffermechanisminmachinesthatact astheself-organizationregulator;ADACORwherethe perturba-tion propagation uses a pheromone spreading technique as a warningsignalamongentities,whichcanassesstheimpactofthe perturbationonthemselves[19].

However, these biologically inspired mechanisms are only consideredverylightly,lackingtrulyevolutionaryconceptsasa way to handle complex systems properly, e.g. minimizing the impact of disturbances. In this way, this paper proposes an

evolutiontotheADACORholonicmanufacturingcontrol architec-ture,by takingknowledgeofbiology, thechaostheoryand the evolutionary theory into consideration to unleash the two predefined working states of its previous version by allowing thesystemtodynamicallyevolveusingself-organization princi-ples.For this purpose,ADACOR2_{reusestheADACOR principles,}

mainly the holonic concepts and adaptive production control, enhanced witha two-dimensional self-organizationmodel that allowsintelligentcomplexestosmoothlyordramaticallyrespond tonewsystemconstraintsinsuchawaythattheoverallsystem performanceisnotdegraded.

Thepaperisorganizedasfollows:Section2overviewsthemain conceptsoftheADACORholonicmanufacturingcontrol architec-ture,andSection3presentstheinspirationandcorefoundationsof theADACOR2holoniccontrolarchitecture,focusingmainlyonthe two-dimensional self-organization model. Sections 3.1 and 3.2

detailthetwoself-organizationcomponents,firstatmicro-level, named behavioural self-organization, and then at macro-level, calledstructuralself-organization.Section4presentsthedetailsof self-organization model. Section 5 presents and discusses the experimental resultsfromtheapplicationof theproposed self-organizedholonic approachtorealexperimentalusecases,and finallySection6rounds-upthepaperwithconclusionsandfuture work.

2. BasicsoftheADACORholonicmanufacturingcontrol architecture

TheADACORadaptiveholonicarchitectureintendstocombine the best practices of hierarchical and heterarchical control approaches,beingasdecentralizedaspossibleandascentralized asnecessary,i.e.usingacentralizedapproachwhentheobjectiveis optimization,andamoreheterarchicalapproachinthepresenceof unexpectedeventsandmodifications[19].Inthesecircumstances, ADACOR proposes the decomposition of manufacturing control functions into a community of autonomous and cooperative holons,takingadvantageofmodularity,decentralization,agility,

flexibility, robustness and scalability. To achieve this, ADACOR proposesfourtypesofholons:

Product holon(PH),representingtheproducts available inthe factoryplantcatalogueandtheknowledgetoproducethem. Task holon (TH), responsible for managing the real-time

executionofproductionordersontheshopfloor.

Operationalholon(OH), representingthesystemresources,e.g. robotsandoperators,responsibleforgoverningitsownagenda as well as managing the physical connection with the real resource.

Supervisorholon(SH),responsibleforintroducingoptimization intothesystem.

ADACOR clearly defines the behaviour of individual holons using the Petri nets [30] formalism and also the interaction patternsbetweenthemusingAgentUML[31]sequencediagrams. ThisisofgreatimportanceinthewaythatADACORproposesa binary-statebalancefortheadaptiveproductioncontrol depend-ingon thesystemperturbationlevel,combiningthebenefitsof hierarchicalandheterarchicalcontrolstructuresusinganadaptive mechanism.

As illustrated in Fig. 2, in stationary state, the holons are organized in a hierarchical structure, with supervisor holons playingtheroleofcoordinationandoptimizingtheschedulesof theirsubordinatesorganizedinclusters.Thissystemrunsinthis configuration until a perturbation is detected. The operational holonthatdetectsthedisturbance(inthiscaseOH1)thentriesto

recoverlocallybycarryingoutaself-diagnosis.Ifrecoveryfromthe failure is unsuccessful, its autonomy factor is increased and propagatestheneedforre-organizationtotheotherholonsinthe system. The propagation mechanism involves depositing a pheromone on the neighbouring supervisor holon and its subsequentspreadtootherSHs.

Theotherholons thatsensethepheromonefromsupervisor holons, increase their autonomy factorsaccording tothe pher-omone’sintensityandtheirlocalknowledge,andpropagatethe

emergentre-organizationtotheneighbouringsupervisorholons. The intensity of the odour associated with the pheromone decreasesasthelevelsofsupervisorholonsincreases(itissimilar tothedistanceintheoriginalpheromonetechniques),accordingto a defined flow field gradient. Each individual holon, taking its autonomyfactor, learningcapabilities andpheromoneintensity intoconsideration,decidesifitshouldre-organizeornot.InFig.2, theholonsOH1toOH5choosetore-organize,whileholonsOH6and

OH7donotre-organizeasthepheromone’sintensityisnothigh

enough,i.e.theepicentreofthedisturbanceisfarawayandthe impactistooloworzero.

Inthistransitorystate,thetaskholonsinteractdirectlywiththe operationalholonstoachieveanalternativescheduleinashort time,gaininginresponsiveness.Duringthisstate,thesupervisor holonscontinueelaborating optimizedschedules,but nowonly theholonswithlowautonomyfactorswillaccepttheproposals. Theholonsremainintransientstateduringthereestablishment time,

t

,whichistypicallyashortperiodoftime.Oncethistimehas elapsed,theyverifyifthepheromoneodourhasdissipatedoris still active. If the pheromone is still active,the holons stay in transientstatefor anadditionalreestablishmenttime,until the pheromonehasdissipated.

Once the pheromone has dissipated, each individual holon reducesitsautonomyfactoragainand returnstoahierarchical controlstructure,goingbacktoastationarystate,sincetheyaccept the schedules once more from their SH. At this moment, the supervisor holons collect the updated individual schedules, achieved during the transient state, and proceed with the synchronization and posterior optimization of the existing schedule.There-scheduleissenttotheoperationalholons,which accepttheadvisedschedulesincetheyhavealowautonomyfactor again.

Thispowerfulmechanismallowsthesystemtorespondquickly toperturbations andbalancebacktoa stationarystateafterits dissipation,fulfillingthechallengeofdevelopingahybridcontrol system, i.e. theha(t) curve in Fig.1. Despite thepotential and innovation introduced by embedding this self-organization mechanism, the control system only balances between two predefinedstates andisnotabletoevolvetootherpre-defined or new control structures, supporting a truly evolvable and reconfigurablesystem.Thischallengeisinlinewiththecurrent emergent, strong requirements, demanding an additional step taking the knowledge of biology, the chaos theory and the evolutionarytheory into consideration to achieve the dynamic evolutionandadaptationofmanufacturingsystems.

3. Self-organizationperspectiveinADACOR2

TheADACOR2_{holonicarchitectureaimstoenhancetheexisting}

ADACORholonicarchitecturethroughself-organizationandchaos principlestoachieveatrulyevolvableandreconfigurablesystem. Theproposedarchitectureallowsthesystemtofindamultitude ofdynamicconfigurations,insteadofthetwofixedonesdefinedin ADACOR, basedon theinspiration of two distinct evolutionary theories.Probablythe mostrenownedevolutionary theorywas developed in the observations and studies by Charles Darwin whichwerepublishedinthebookentitled‘‘TheOriginofSpecies’’

[32].Inthisbook,Darwinstatesthatovertimespeciestendtomake small internal changes in order toadapt totheir environment (Fig.3b).Onthecontrary,thepunctuatedequilibriumtheorystates thatspeciestendtobeinastablestateforlongperiodsoftimeand suddenlymakeadrasticchange[33],illustratedinFig.3,curvea. Similarconceptscanbefoundinthemanufacturingworldwhere thekaizenphilosophystatesthatthesystemcanimprovethrough smallcontinuouschangeswhilethekaikakuphilosophyconsiders thatdrasticchangesimprovesystemperformance[34].

Thesetwodramaticallydifferentapproaches,foundeitherin evolutionarytheoriesormanufacturingstrategies,offerinsightsto respondagilelytounexpectedevents.Withthisinmind,thiswork proposes a two-dimensional self-organization model taking mechanisms at micro and macro level into consideration, as illustrated in Fig. 4, allowing the combination of smooth and drasticsystemevolution(illustratedinFig.3,curvec).

Behaviouralself-organizationisobservedatmicrolevel,where eachindividualholoncanchangeitsbehaviouraccordingtothe externalconditionsresultingina smoothevolution.Thesecond component,namedstructuralself-organization,isobservedata higherlevelandmakesthesystemevolvedrasticallybychanging the relationships between the holons (and even the cluster formation).Inthenextsection,eachofthesecomponents,aswell theinter-dependenciesbetween thetwolevelswillbedetailed further.

3.1. Actingatmicrolevel:behaviouralself-organization

Behaviouralself-organizationcontributesatmicroleveltothe globalself-organizationfoundintheADACOR2_{architecture.This}

self-organizationcomponent,seenasawayofhelpingthesmooth evolutionofthesystemtofaceunexpectedchangesinconditions, isachievedbyprovidingtheholonswithacatalogueofbehaviours andabehaviourselectionengine.

Forthispurpose,eachholoncontinuouslymonitorsitsstateand itsenvironment,seekinganopportunitytoevolve,aswellasbeing awareofanyexternalevolutiontrigger.Dependingonthetrigger type,theholonself-organizesaccordinglyby selectingtheright behaviour from theset of known behaviours. This selection is accomplishedwhentheadequatebehaviourisusedattheexact momentwiththelowestimpactontheoverallsystem.Thistypeof self-organizationwillpushthesystemtoanewoperatingpoint,i.e. anewconfiguration,producedbysmallinternalchangestocope withnewconstraints.

Inthissense,thebehaviouralself-organizationofADACOR2is definedas‘‘thechangeintheinternalstateoftheholonusingaset ofinternalrulesandmechanisms,triggeredinresponsetoaplan deviation or a new evolution opportunity,with theaim of re-establishingnormalfunctioningorimprovingperformance.’’

From a behavioural self-organization perspective, a plan deviation is detected or evolution opportunity discovered by

Fig.3.Darwinandpunctuatedevolutiontheories.

Fig.4.ADACOR2

evolutionaryperspectivebased onthetwo-dimensional self-organizationmodel.

matchingasetofrules,presentinadecisionruleengine,withthe informationavailable.

Thedecisionmakingprocessisaccomplishedusingglobaldata from ADACOR2 _{high level holons (if available and/or needed),}

namelytheSH,andlocaldatafromholons’localknowledge.Inthis way,thedetectionof previous situations,using themonitoring module, is sustained by means of access to global and local information.Accesstotheglobalinformation,relatedtothemost optimizedfunctioning,guaranteesthatbehaviouraldecisionstake into account broader, long-term solutions, thus decreasing the needforanewbehaviouraladaptationintheshortterm.Onthe otherhand,local informationsupportsthesystemreactivityby allowingtheentity toaccess localdata that at themomentof decisionismoreaccurate,i.e.moreup-to-date.Despitetheshorter refreshrateofthisdata,itsuffersfrommyopiainthesensethatit representsalocal,partialviewforasmallerareaofaction[35],and soglobaldataisusedwheneverpossible.

Thedelicatepartoffindingtherightbalancebetweenglobal andlocaldataconcernsthesharegiventoeachpart,takingdata reliabilityintoconsideration.Concerningthis,atthestart,whenno reliabledatainformationisavailable,theholonsdecideonequal shares, which will berefined (e.g.using reinforcement mecha-nisms)duringitslifecycle.

Apracticalexampleofbehaviouralself-organizationinactionis depicted in Fig. 5. During the normal operational process, a scheduledordercannotbeprocessedbytheassignedresource.In thiscase,theTHresponsibleformanagingtheorderwill,usingits setof known behaviours, adapt by changing its behaviour, re-scheduling the order (which possibly affects the rest of the schedule).

In this example, the hierarchy formed within SH#2 was dissolvedand the TH reassigned theupcoming workorders to theavailableOHs,somebeingpartoftheworkordersassignedto theSH#1hierarchy.

3.2. Actingatmacrolevel:structuralself-organization

Structural self-organization presents a means of drastic evolution, re-arranging the relationships between the holons and/ortheirorganization.Formally,inthiswork,structural self-organization can be defined as ‘‘the change in the relationships between holons, and consequently the change in the holarchy

structure, which is triggered in response to a deep impact plan deviation,thuspromotingadrasticresponsethataimstore-establish normal system functioning or improve its performance’’. This property can be divided into different levels, also linked to behavioural self-organization. On this subject, a three-level classificationhasbeendevelopedasfollows:

Level0(emergence):relationsbetweenholonsarechangedasa consequenceofbehaviouralself-organization.Thishappensasa changeinholonbehaviourcanimplyachangeinitsrelations, such as a change in the resource that will process thenext operation.Thislevelofstructuralchangeisclassifiedasweak sincethestructuralchangeisnotdirectlydrivenbytheneed. Level 1 (logical structural self-organization): each holon is

constantlytryingtooptimizeitsplaceintheholarchystructure. This constant optimization may drive the holon to change holarchy,belongtoseveralholarchiesatthesametimeoractasa freelancertoworkcompletelyautonomously.

Level2(physicalstructuralre-organization):similartolevel1with theadditionthatholons,e.g.OHs,canphysicallychangetheir place, changing not only their relations and positions in the holarchybutalsotheirphysicalposition.

Particularly,thelasttwolevels,level1and2,arerelatedtothe conceptsenvisionedbytheRMSparadigmofrapidchangeat shop-floor level. In fact, in level 1, one can assistto the logical re-organization on the control layer, which is mapped into the softwarechangeoftheRMSparadigmand,inlevel2,ahardware changeisachievedbythephysicalre-arrangementoftheresources presentattheshop-floor.

The stimulus that can trigger a structural self-organization dependsontheimpactonthesystemandcanbeanyeventthat disruptsordeviatesthepredictedexecutionofthesystem,suchas arushorder,anordercancellation,aproductionqualityissue,a supplyshortage,oraresourcemalfunction.Inordertoillustrate this, three examples are provided in which a structural self-organizationcouldbeinitiated.Thefirstistheintroductionofabig batchorder; thesecondis theconstant productionequilibrium betweentwoproducts,eachonehavingparticularrequirements, andathirdwithresourcesharingduetoamalfunctiononasimilar resource.Assumingthatthesystemisfunctioninginastablestate inagivenconfiguration,andthataverylargeorderarrivesinthe

system at a given point in time, the system detects this perturbation,througheithertheSHholonsduetoahighdemand invariationrequestsortheindividualOHsduetoahighnumberof variation proposals, as shown in Fig. 6. Realizing these new constraints,thesystemselectsthemostadequatestructure.Inthe presentexample,theSHdetectstheperturbationusingitsknown algorithms, e.g. a Particle Swarm Optimization or Genetic Algorithmforresourceallocation.

Thisexampleculminateswiththecreationoftwogroups,each supervisedbyaSHandmanagingabatchoforders,i.e.thecurrent andthenewone.

ApertinentquestionishowdotheADACOR2_holons

structur-allyself-reorganize?Drawinginspirationfromthesocial behav-iourofschoolsoffishesandflocksofbirds,itisconcludedthatthey workverywellasa group,maintainingthesystemequilibrium and,e.g.,avoidingpredators[24],whichinthiscaseareexternal systemperturbations. The inexistence of any central authority regulatingthisglobalbehaviourmakesthismechanismevenmore robustandpowerful.

ADACOR2_{holonsfollowthesamebasicprinciplesasswarms,}

constantlytryingtobuildcohesivegroups,maintainingdistances andimposingcrowdmanagement.However,acentralauthority, which is lacking in nature, was intentionally introduced in ADACOR2_{asitcanincreaseoptimizationlevels.Infact,thiswas}

alreadysuggestedintheworkbyKoestler,whenhestatedthatthe creation of stable intermediary states in the holonic holarchy introduceshighlevelsofoptimization.Thesehigherlevelholons superviseasetofOHswhichtheytrytooptimize,e.g.optimizing schedules by coordination and synchronization of the groups’ holons,andwhereitispossibletoendoweachgroupwithasetof servicesasdiverseaspossible,creatingthepossibilitytoattendto awidersetofrequestsortoaggregateholonswithsimilarservices, creatingspecializedgroups.

3.3. Downwardandupwardcausation

The role of the two components in the self-organization mechanismneedsthecorrelationoftheeffectsbetweenthesetwo levelstobeconsidered,i.e.theeffectoftheinterrelationbetween behaviouraland structural self-organization: thechangein the

behaviourofoneholonwilldrivethesystemtoevolve,thenanew emergentglobalbehaviouremerges,asitcanbeseeninFig.7.Ina similar way, an evolution in holon behaviour occurs after a structuralself-organization.Agraphicalexplanationispresented inFig.7usingColeman’sboatanalogy[36],whichexplainsthese interrelationsindetail.

In brief,a changeinthestructuralrelations betweenholons impliesadownwardcausationwhichwillchangethebehaviourof individualholons,whilea changein thebehaviourofholons at micro-level may imply an upward causation that affects the structureoftherelationsbetweenholons.

4. Detailsoftheself-organizationmodel

Theself-organizationmodelistranslatedtothearchitectureof individual ADACOR2 holons by considering a self-organization moduleinsidethedecisioncomponentoftheholon,asillustrated in Fig.8.This moduleaddressestwo types of self-organization componentsand comprisesprimarilythemonitoring,discovery, reasoning,learning, nervousnessstabilizer and dispatcher com-ponents.

The discovery and monitoring modules are supported by a databaseandtheiroutputfeedsthereasoningandlearningengines thatprocessthedatausinga rule-basedengine.Thisprocessis responsiblefordiscoveringnewopportunitiestoevolve,aswellas deciding the best way to evolve. The monitoring module is responsible for gathering information about the state of the system, e.g.analysingtheexchangeof messagesor sensingthe

Fig.6.HugebatchorderdetectedbySHs.

Fig.7.Coleman’sboatviewinADACOR2

holons’ physical signals. In contrast, the discovery module is responsibleforqueryingtheholonicplatform tofindupcoming events,suchasthepresenceofnewholons,whichcanimplythe appearanceofanewholarchy.Theoutputofboth moduleswill feedthereasoningwithfactsthatarelaterexecutedwithasetof rulesthatcomposesthereasoner.

The reasoning module comprises two sub-modules, each containing thecurrent rules for each type of self-organization, i.e.oneforbehaviouralandtheotherforstructural.Inbrief,when themonitoringanddiscoverymodulesissuenewinformation,itis evaluatedinthetwo reasoners’modules.Eachofthereasoners provides outputs that are later evaluated by the respective nervousness stabilizer. The nervousness stabilizer prevents the holonfromfallingintochaoticbehaviour.

4.1. Reasoningandlearning

The learning capabilities, complementary to the reasoning module, play a crucial role in the self-organization model, supportingthegeneration, removaloradaptationofknowledge. Inparticular,learningisimportantintwodistinctphasesof self-organization:

(i) identificationofopportunitiestoevolveand (ii)defininghowtoevolve.

Differentconstraintsimposedifferenttypesoflearning techni-quesandforthisADACOR2_{usessociallearningforthepropagationof}

new behaviours among the holons and the propagation of accumulatedknowledgefromPHstoTHsusingapheromone-like mechanism, as defined in the ADACOR architecture [19]. This mechanismpreservesthesameprincipalsencounteredinnature,as the closer a holon is tothe epicentre of propagation, the more affecteditwillbe,theimpactbeingattenuatedfortheouterholons. Thispropagationhasagradientdecayfromitsepicentreandalso propagates tooutside its holarchy, wheresome actions can be undertakentocheckthestatusoftheevent.

Thepropagationofeventstooutsidethestartingholarchyraisesa complementaryissuethatisrelatedtothereliabilityofthewarning propagation.Tocopewiththisandchecktowhatextentaholoncan trustthe informationreceived,individualADACOR2_holonsusea

trust-basedmechanismtoevaluatetheinputsfromotherholonsand assertiftheinformation/behaviourisofvalueornot.

Asecondexampleofalearningtechniquebasedonrewards,i.e. reinforcement-basedmechanisms,isusedasameanstoevaluate pastevolutiondecisions.Inthisway,badpastdecisionswillhavea negative impact on the future choice for the same decision contrarytoagoodresultthatwillhaveapositiveimpact.

It is important tonote that the learning mechanisms tobe embedded in individual holons should be kept as simple as possible,asitisinbiology.Nevertheless,morecomplexlearning

mechanismscanbedeveloped,whichcanpotentiatethelearning moduleevenmore.

4.2. Nervousnessstabilizers:awayofcontrollingchaos

Oneofthemainproblemsthatcouldappearinself-organized distributed autonomous systems is chaotic system behaviour whereholonsarecontinuallychangingtheirbehaviourorentera continuouscycleofconstantevolution/adaptation. Tocopewith this issue, each individual ADACOR2 _{holon has a built-in}

stabilizationmechanism,comparable tocarshock absorbers, to avoidinstabilityfirstlyintheholonsandsecondlyinthesystem.By introducingthesestabilizers,thesystemoperatesinsuchmanner thatit ispushedtoitslimitsbyenhancingtheself-organization andchaoticprinciples,butalwaysremainsundercontrol.A two-layerapproachisused,asillustratedinFig.9,wherethefirstone regulates each self-organization module individually and the secondonemergesthetwo,decidingwhichemergentbehaviour willproducethemostvaluableevolution.

Some ofthetypicalapproachestocalmtheholons’desireto changecanbecontrolled by restrictingthe numberof changes withinaspecifictimeframe,onlyallowingtheentitiestochangeat pre-definedintervals or by setting the exploration/exploitation threshold[37].Inthiswork,aninnovativetechnique,inspiredfrom theclassicalcontroltheory,namelytheProportional,Integrative and Derivative (PID) controller, was used as the stabilization mechanism. The following three parameters are those of the controller:theproportionalpart(Kp)thatadjuststhereactiontime

totheperturbationtrigger,theintegrativepart(Ki)thatregulates

theacceptederrorofthedesiredgoal,andthederivativepart(Kd)

thatsetstheresponsivenesstoreachthegoal(asinEq.(1)): fðtÞ¼KpeðtÞþKi

Z t 0

ð

t

Þd

t

þKdd dteðtÞ

Similarly,inADACOR2_{,thesethreeparametersregulatethe}

self-controlofholons,whereKpdefinesthetimefromwhichtheholons

starttoreacttotriggers,allowingthemtoovercomemomentary

perturbationsortogathermoreinformationabouttheperturbation, e.g.inOH,tostarttheself-organizationmechanismonlyafterthe wholesetofnewallocationrequestshasbeencollected.InthePID controlleranalogy,ahighvalueofKpwilldrivethesystemtorespond

fastertoperturbations,whichcouldleadthesystemtobehaveinan unstablemanner,whilealowvaluewillnotenablethesystemto reachthedesiredgoal.Thesamecautionsmustalsobetakeninto accountwhenselectingthetimeafterwhichtheholonswillreactto plandeviations.Ahighvaluemaydrivetheholonstobeconstantly inanadaptationmodewhilealowvaluemightdrivethemtonever react.Secondly,Kidescribestheminimumacceptableimprovement

ofthesolutionfound(byeachself-organizationmechanism)that canbeconsideredasenoughtopermittheuseofthechanges.Inthe PID analogy, the Ki, or the integrative part, is responsible for

theeliminationoftheerrorinsteadystate,i.e.goingascloseas possibletothedesiredgoal.SincethegoaloftheADACOR2_holonsis

toimprovethemselvesalways aspossible,they’reconsideredas movingagoal,whichisamaximizationfunction.Insuchway,theKi

parameteracts asthe minimumacceptable improvementofthe actualgoal.Lastly,Kddefineshowfastthesolutionmustbefound,

actingasalimittofindsolutions,afterwhichthemostacceptable onesareconsidered.InthePIDanalogy,theKdparameterhelpsto

improvethesettlingtimeandincreasessystemstability.Mimicking thistothenervousnesscontroller,onewillfindthatthisparameter actslikethetimelimiterforagivenholontoadapt.Afterthistime, theholonswillstopadaptingandthuscalmdownitsbehaviour.The cautionstosetthisparameterregardsthefactthatahighvaluewill enablealongadaptationtimewhilealowvaluemightnotenablethe holontoreachanacceptablegoalvalue.

AnotherconcerntobetackledinADACOR2_{isrelatedtothefact}

that theself-organization processcanbe verytime-consuming, namelyintermsofthestructuralvector,whichcanbeofmajor importance in large-scale systems in which the amount of information available increases exponentially. This important constraintissolvedbytakingasystemsnapshot,i.e.thecurrent systemstate,wheneverthereisadisruptiveevent.This context-awarefeatureiscomplemented,attheendoftheself-organization

Fig.9.StabilizationmechanismembeddedinADACOR2

process, with the actions taken and the results achieved (i.e. performanceindicators),allowingthedecisionassessment after-wards. Additionally, this process can be enriched with data processing,e.g.datamining,tofacilitatefutureeventsbyallowing thesystemtofindthebestmeasurestakenforsimilareventsand startawarmself-organizationprocess,i.e.itisnotnecessaryto discoveranewconfiguration,onlytoadaptaknownone. 5. Experimentalresults

Two experimental simulation scenarios were developed to assesstheproposedarchitecture. Thebehavioural self-organiza-tionwasvalidatedusingtheAIP-PRIMECAFlexibleManufacturing System(FMS) located at the Universite´ de Valenciennes et du Hainaut-Cambre´sis,andthestructuralself-organization,duetoits particularities,wasvalidatedusingamodifiedversionofthe AIP-PRIMECAFMS,wheretherigidityoftheresources’positionwas removedand theconveyorsystemwasreplacedby Automated GuidedVehicles(AGVs).

5.1. Descriptionofthecasestudyandsystemimplementation Thecasestudyusedinthisworkispresentedindetailin[38].In brief,theFMSiscomposedofsixworkstationslinkedbyaconveyor system.Thesystemisabletoproducethreeproducts,namelythe words‘‘BELT’’, ‘‘AIP’’and ‘‘LATE’’, each beingcomposed of sub-assemblies (e.g. the product ‘‘BELT’’ is composed of the sub-assemblies‘‘B’’,‘‘E’’,‘‘L’’and‘‘T’’).Thepartsareassembledbythe workstationsaccordingtoaprocessplandesignedforeachpart,as describedinTable1.

TheworkstationsdisposedalongtheFMScanperformasetof operationsaccordingtotheirskills,asillustratedinTable2(also includingtheprocessingtime,inseconds,ofeachoperation).Asan example,the‘‘Loading’’operationcanbeexecutedbyworkstation M1whilethe‘‘Axis’’operationcanbeexecutedbyworkstationsM2

andM3,bothwithaprocessingtimeof20s.

Theproposedself-organizedholonicsystemusedmulti-agent system technology, and more particularly, the Java Agent DEvelopmentFramework(JADE)[39]todeveloptheagent-based

infra-structure,namelythebehaviourofeachindividualADACOR2

holon and the cooperation patterns designed. The holon’s intelligencewasembeddedusingaruleengineimplementedwith theJavaExpertSystemShell(JESS)platform[40].

In the real AIP-PRIMECA scenario, a particular holon type, named ConveyorSystemHolon(CSH),instantiatedfromanOH, wasdevelopedtomanagetheroutingandgateswitchinginside theconveyorsystem,providing somespecificfunctionssuchas serving as an intermediary to manage the dispatching of the transportationorderstotheavailableshuttles.TheCSHusesthe Jungtool [41]tosupportthedynamic parametercalculationin thenodesandalsotoprovidedisplayfunctionalities.TheCSHhasa Graphical User Interface (GUI), illustrated in Fig. 10, which provides real-time information regarding the location of the shuttlesandthestateoftheworkstations,accordingtoa colour-baseddiagram.

Theexperimentaltestswereexecutedbyconsideringthe self-organizedholonicagent-basedsolutiondevelopedusingtheJADE framework, to implement the logical control layer, which is populatedbyseveralinstancesofTH,OH,PHandSHholons.The implementationofholonshavingphysicalrepresentation,e.g.OHs, requiresthedevelopmentofwrappersinterfacessupportingthe integrationoftheworkstationspresentedintheAIP-PRIMECAcase study.Inthiswork,thevirtualresourceconcept[42]wasusedto providetransparencyintheintra-holoninterconnection, encom-passingthedevelopmentoftheimplementationoftheservicesat theserverside(i.e.theresource)thatwillbeinvokedontheclient side(i.e.theagent).Inthisway,theclientignoresthedetailsofthe server implementation, and particularly for thecontrol system perspectiveitisindistinguishabletobeconnectedtoanemulation platformortoarealsystem.

Havingthisinmind,asetofemulatorsareusedtoimitateeach workstationpresentedintheAIP-PRIMECAcasestudy,eachone implemented as a JADE agent codified in Java language (the behaviourofeachagentcomprisesadisturbancemodeldefinedfor eachworkstationandpresentedinthenextsection).

5.2. Analysisoftheresultsofthebehaviouralself-organization experiment

Abatteryofexperimentaltests,usingasimulatedAIPsystem, was conducted to validate the behavioural self-organization component,comparingdifferentcontrolapproaches:

Hierarchical approach, wherea SHalways provides optimized schedulingfortheOHs.

Heterarchicalapproach,runningwithnoSHentity,i.e.eachTH interactsdirectlyandhorizontallywiththeOHs.

ADACOR approach, presenting the adaptive control approach balancingbetweenhierarchicalandheterarchicalapproachesin responsetotheoccurrenceoffailures.

Table 1

Processplansforthecatalogueofproducts.

Sequence B E L T A I P

#1 Loading Loading Loading Loading Loading Loading Loading

#2 Axis Axis Axis Axis Axis Axis Axis

#3 Axis Axis Axis Axis Axis Axis Axis

#4 Axis Axis Axis r_comp Axis I_comp r_comp

#5 r_comp r_comp I_comp L_comp r_comp Screw_comp L_comp #6 r_comp r_comp I_comp Inspection L_comp Inspection Inspection #7 I_comp L_comp Screw_comp Unloading I_comp Unloading Unloading #8 Screw_comp Inspection Screw_comp Screw_comp

#9 Inspection Unloading Inspection Inspection #10 Unloading Unloading Unloading

Table 2

Machineskillsandprocessingtimes(inseconds).

Operation M1 M2 M3 M4 M5 Loading 10 Unloading 10 Axis 20 20 r_comp 20 20 20 I_comp 20 L_comp 20 20 20 Screw_comp Inspection 5

ADACOR2_{approach, enabling thebehaviouralself-organization}

approach (inthis work, byselecting newbehaviours,suchas thoseusingaPotentialFields(PF)approach[43]).

Two experimental scenarios were considered: one with no perturbations and a second with perturbations. In the latter, failures were introduced in workstation M2 at a rate of 25%

(correspondingtoscenario#PS12ofthebenchmarkingplatform

[38]),meaningthaton averagetheresourcewillbeunavailable everyfourworkorders,guaranteeingdeterminisminthetests.In thissituation,theworkorderaffectedandthesubsequentones needtobere-scheduled.

Inthiswork,thecomparisonbetweenthecontrolapproaches considerstheproductionofabatchof‘‘BELT’’and‘‘AIP’’products, andaimsatreducingthemakespan(Cmax).Table3summarizesthe

makespanachievedforeachcontrolapproachtakingthescenarios describedintoconsideration.

As expected, the results showthat in the scenario withno perturbations, the hierarchical-like approach achieved better results,i.e.morereducedmakespan,thantheheterarchicalones. Insuchsituations,ADACORandADACOR2_{exhibitthesameresults}

as thehierarchical approachdue to thepresence of supervisor holonswhichensureoptimizedschedulesfortheirsubordinates, runninginthismannerinahierarchicalstructure.

In thesecond scenario,withthepossibility ofperturbations occurring,allthecontrolapproachesanalysednaturallysuffered performancedegradation,withthehierarchicalapproachsuffering themost.Onthecontrary,ADACOR2sufferedtheleastdegradation in performance, even less than the previous ADACOR and the heterarchical-likeapproach,sinceADACOR2_{enablesthedynamic}

behavioural self-organization in each holon to cope with the occurrenceofaperturbation,inthiscaseselectingthePFbehaviour for the re-allocation mechanism, evaluating which behaviour offersthemostappealingoutcome.

5.3. Analysisoftheresultsofthestructuralself-organization experiment

Toextractthefullpotentialofthestructuralself-organization providedinADACOR2_{,thetrialsassessedmustallowshop-floor}

re-organization.ThisisnotpossibleintherealAIP-PRIMECAFMSdue to its physical rigidity. So, a modified systemwas considered, whichassumedthatworkstationshadmovingcapabilitiesinthe sensethattheycanbeunpluggedfromoneworkingpositionand movedtoanother.Aworkingpositionisdefinedasaplaceonthe

Fig.10.GUIoftheConveyorSystemHolon.

Table 3

Resultsforthebehaviouralself-organizationexperiment.

Heterarchical Hierarchical ADACOR ADACOR2

Cmaxwithno perturbations(s) 492.0 455.4 455.4 455.4 Cmaxwith perturbations(s) 551.5 563.5 535.4 522.8 Performance degradation(%) 18.2 23.7 17.6 14.8

shop-floorthatisequippedwithsystemstosupportthenormal functioningoftheworkstations,suchaspower,compressedairand communications.AsecondimportantchangeistheuseofAGVs insteadoftherailconveyorsystem.Fig.11depictstheconceptual viewoftheadaptedsystem, wherethepurplepointsrepresent workingpositionsandthegreenonescanonlybeusedasrouting paths. A fundamental issue to ensure coherence between this adapted system and the real one is the calculation of the transportationtime that must beas close aspossible between thetwo.Forthispurpose,itisassumedthatmovingfromonepoint toanothertakestheAGV3swithaclearpath.Ontheotherhand, shiftingresourcesnotonlydependsonthedistancetobetravelled, butcanvaryfromworkstationtoworkstationduetosize,weight andthecomplexityofcables.

Withthisinmind,asinthepreviousexperiment,theADACOR2

holons were deployed in the production control system, with workstationsbeingmappedasOHsandeachAGVasaspecialized transporterinstantiationof theOH (seeFig.11). TheCSHis no longerusedhere.

Theexperimentaltestsconductedinthepresentstudyassumed a level 3 structural self-organization since workstations can changeplaceon theshop-floor.A bigbatchorderwasusedby individual holons as the trigger to start the structural self-organization.Additionally,nocentralauthoritywasused,making the structural self-organization complete in its magnitude, according to the concept of swarms found in nature. In brief, afterreceiving a batch oforders, everyOH will start gathering information from other OHs, where resource queues, allocated workorders,processingtimesandactuallocationareexchanged. Theprocedureusedinthisexamplefollowstheprinciplewherethe most overloaded OH are the most critical. So, these resources shouldbefixedfirst,minimizingthetransportationtimesbetween them.AfterallocatingalltheOHs,eachOHcalculatestheoutput results,i.e.thedecrease/increaseinCmax,andsharesit withthe

otherOHs,alsosending,inthecaseofabettersolution,theKPIs and the new positions for each OH. Finally, the best overall solution,foundinalltheOHs,isautomaticallyassumedandused. The system described initializes a manufacturing order to produce2x‘‘BELT’’andafter120sanewmanufacturingorderto

produce5x‘‘AIP’’appearsinthesystem.Thistriggersastructural self-organization,asillustratedinFig.12.

Twoscenarios weresimulatedandcomparedtoevaluatethe structuralself-organizationmechanism.Thestructural self-orga-nization was disabled in the first scenario and enabled in the second.Severalsimulationswerecarriedoutforeachscenarioand theaveragevaluesarepresentedinTable4.

As can be observed by the analysis of the previous table, enablingthestructuralself-organizationreducestheoverallCmax. Thisis mainlyaccomplishedby thereduction intransportation timeswhenthestructuralself-organizationisenabled(reduction from 19.4% to 14.9%). Additionally, a reduction in the time to processeachindividualorderwasobserved.

The experimental results obtained show the merits of the structural self-organization approach to cope with severe, changing conditions. However, the experiments also showed new possibilities to improve the proposed algorithm, e.g. by consideringoptimizationinthere-configurationoftheresources duringthestructuralself-organizationprocess.

6. Conclusions

Thecomplexityofsystemshasgrowntounprecedentedlevels, triggering the needfor new, moreagile, robustand responsive control architectures. MAS and HMS play an importantrole in addressing this challenge by proposing a distribution of the processing capacity with several autonomous and cooperative entities.

Inspired from biology and evolutionary theories, this paper proposes an innovative control architecture called ADACOR2, whichconsiderstheintroductionofatwo-vectorself-organization mechanism:behaviouralself-organization,foundat micro-level, which allowsthesystemtorespondsmoothlytoperturbations, andstructuralself-organization,displayedatmacro-level,which lets thesystemreactmore drastically.The introductionof self-organization properties in these autonomous entities and the overallsystem,mayleadtosomesystemnervousness,makingthe systemmoreunstableandunpredictable.Sincetheobjectiveisto push the system to its limits whilst maintaining its stability, nervousness stabilizers are also proposed to enhance the self-organizationandchaoticprinciples,whilemaintainingthesystem undercontrol.

Thisinnovativearchitecturewastested,usingsimulations,on two differentsystem topologies, namely thereal AIP-PRIMECA systemandamodifiedversionofthissystemwheretransportation wasachievedusingAGVsandtheworkstationscouldmove.Inboth scenarios,ADACOR2_{hasbeenproventodisplay abetteroverall}

performance, in this case a lower Cmax, than theother control

approaches tested. Consequently it is less affected by the

Fig.12.Structuralself-organizationinpractice.

Table 4

Resultsforthestructuralself-organizationexperiment. Without structural self-organization Withstructural self-organization Cmax(s) 1147.5 1112.0 %transpTime(%) 19.4 14.9 avg(order)(s) 725.2 646.2

occurrence of unexpected perturbations, frequent in industrial environments.

Futureworkwilldealwithmergingthetwoself-organization mechanisms,by meansof thesecond layerof thenervousness stabilizer, and considering the deployment of more powerful mechanismsforthebehaviouralandstructuralself-organization vectors.Additionallyanddespitethisbeingasimulationvalidation approach,amorepracticalimplementationisalsoenvisionedby changingaparameterinthestartoftheagents,whichcontrolthe realcellthroughModBuscommunicationcommands.

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Jose´ BarbosahasaM.Sc.inIndustrialEngineeringat IPB,andiscurrentlyaPh.D.candidateattheUniversity ofValenciennesandHainaut-Cambre´sis(France).Heis a researcher at Polytechnic Institute of Braganc¸a, Portugal,participatingintheEUFP7 ARUMproject andinthepastintheEUFP7GRACEproject.Heisalso invitedteacherattheDepartmentofElectrical Engi-neeringofthePolytechnicInstituteofBraganc¸a.Hehas morethan20paperspublishedatinternationaljournals andproceedingsofinternationalconferences.Hismain research topics focus on the development of self-organizingandevolutivemanufacturingcontrol archi-tecturesfollowingtheholonicandmulti-agentsystem paradigmsenrichedwithbiologicalinspiredmechanisms.Heisalsomemberofthe IEEETechnicalCommitteeonIndustrialAgents.

PauloLeita˜oreceivedtheM.Sc.andPh.D.degreesin ElectricalandComputerEngineering,bothfrom the University of Porto, Portugal, in 1997 and 2004, respectively. He is a professor at the Polytechnic InstituteofBraganc¸a,currentlyHeadoftheDepartment ofElectricalEngineering,andmemberoftheArtificial IntelligenceandComputerScienceLaboratory(LIACC). Hisresearch interestsare in the field ofindustrial informatics,collaborativefactoryautomation, reconfi-gurable production systems, intelligent supervisory control, agent-based and holonic control and bio-inspirationengineering.Heparticipate/hasparticipated inseveralnationalandinternationalresearchprojects andNetworksofExcellence,haspublishedmorethan130papersinhigh-ranked internationalscientificjournalsandconferenceproceedings(peer-review),is co-authorofthreepatentsandservedasgeneralco-chairofseveralinternational conferences,namelyIFACIMS’10andHoloMAS’11.HeisSeniormemberoftheIEEE Industrial Electronics Society and currently the Chair of the IEEE Industrial ElectronicsSocietyTechnicalCommitteeonIndustrialAgents.

Emmmanuel Adam is an associate professor in Computer Science since 2003. His works concern holonicmultiagentorganizations,thatistosayflexible, open and hierarchical organizations, where each element is free to choose its actions to reach its individualgoalwhilebeingconstrainedbythe necessi-tyfortheorganizationtoreachitscollectivegoal.The applicationfieldsarethetransportsimulationandthe manufacturingsystemsmanagement.Heisamember ofprogrammecommitteesofinternationalconferences (e.g.AAMAS,PAAMS)andreviewerforinternational journals(e.g.EAAI,IJPR,JIMS,COMIND).

DamienTrentesauxiscurrentlyfullprofessoratthe University of Valenciennes and Hainaut-Cambre´sis (France).Hisareasofinterestconcernthecontroland the optimization of discrete event systems (manufacturing,transport,logistics,andservices)using multi-agent,bio-inspiredandholonicmodels.Hehas supervised12Ph.D.thesisandisauthorandco-author of more than 100 peer reviewed publications in journals,books,andchaptersofbooksandconference proceedings.Healsoco-chairedseveralspecialissuesin journalseditedbyElsevier,SpringerandLavoisier.Heis amemberoftheIEEEIndustrialElectronicsSociety(IES) TechnicalCommitteeonIndustrialAgents.Heisalso memberoftheIFACTechnicalCommittee5.1onManufacturingPlantControl.He belongstothe‘‘boardofeditors’’fortheElsevierJournalEngineeringApplicationof ArtificialIntelligence.