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DISKRETNA SIMULACIJAwith other modelons. A modeloncan consist of other modelons andit can be a part of a modelon – likeobjects in object oriented programming.A modelon structure is used todescribe part-system, entity-wholerelations, interaction and cooperationof building blocks and self-reflexiveresponsibilities. Structuring systemsinto modelons and relations amongthem provides the means to model,understand and design complexhierarchies of decision-making processesfor control.One example of implementation ofa bionic distributed control systemconsists of machines and AutomatedGuided Vehicles (AGVs). The exampleis proposed and annotated on atheoretical level and it is evaluatedby discrete event type simulation inthis contribution. Modelons of partsin production, and modelons ofmachines and AGVs correlate theiractivities for the success of the whole,which is optimized production.2 The product as a controlactor in FMSLet us remember that the problem ofmanufacturing control is in the classicalapproach typically approachedby planning (level of production system)and by scheduling (productionfloor level). The problem is posedin terms of production flow and ofresource allocation. The outcome ofthese steps is classically a Gantt chartthat freezes the operations allocatedto each resource, as a function oftime. It can be said for this approachthat it is mostly based on workwith manufacturing operations. It ischaracterized by giving answers tothree questions that are, in order:Which resource does the operation?– When does the operation take place?– What product is concerned bythe operation? [6].The product approach (PA) [7], aimsat higher flexibility and reactivity. Inthis context a product may representan elementary component, an assemblyof several elementary componentsor a set of similar components. PAisbased on a society model. Membersof this model are both, products andVentil 14 /2008/ 3resources. This approach leads torearranging the order of the questionsto: What product? Which resource?and When?The product becomes an active elementof the production system andtakes part in the decision makingprocess which defines its’ furtherproduction. Each product communicatesand negotiates with all theresources to make appointments foreach operation. The products arelike customers and the resources areservice providers. As a result, the productionsystem is made up of a set ofautonomous members, i.e., productsand resources, and they cooperate toachieve their goals. Agent structurescan be a method to implement sucha system.A fundamental question of the applicabilityof this reasoning is thefollowing one: if each member of thesystem optimizes his behavior for hisbenefits, how does that correlate tothe benefit of the whole?The question might seem trivial – atleast it is not that much addressedin the technical literature. Based onthe fact, one might conclude, thatthe skepticism is not justified. Onthe other hand, our approach tomanufacturing imitates principlesfrom animal and human world.Does such organization (group oflocal optimizations equals to globaloptimization) work for animals andhumans? Regarding former, it looksit works for highly organized species,as bees are. It is just we do not knowmuch, what kind of reasoning do theycarry in their genes and pass on fromgeneration to generation.Regarding humans, history and dailylife give us pro and contra exampleson individual vs. collective optimization.Principles of democracy seemto correlate benefits of individualsand benefits of society the most. Butprinciples of democracy involve lotsof consideration and empathy, whichdon’t promise much as mechanismsof optimization in manufacturing.Besides, many activities of humanindividuals are optimized for theirindividual well being (wild privatization,taikunization, shattering financialmarkets by intention), but theyare catastrophic for the society. Theseexamples convince us in relevanceof questioning one to one mappingbetween individual and collectivewell being. However, a general positivecorrelation between the two cannot be overseen. It convinces us thatthe socio approach to manufacturingis worth of exploration. Especially,since it is the designer of a manufacturingsystem, who plays God whendesigning a control system. He hasfree will to decide, what to take fromthe fields of sociology and psychology,and what to take not. One way toevaluate correlation between individualreasoning and its’ benefit to theoverall system is simulation.2.1 Implementation of PAPrinciples of PA associate all theknowledge and all the decision capacitythat are required for productioncontrol. As a result, the productpossesses the specific knowledge tosearch for and to process informationon the production process: productiongoals, decision rules, equipmentfeatures and production environment[8]. This specific knowledgebase contains all information on theproduct, as it passes phases of theproduction process, including:– its identity, its functional and structuralfeatures, its parameters,– the process and the operational sequenceto produce the product,– the priority weights,– the equipment features and productionenvironment.Products’ knowledge base also containsinformation on the prearrangedschedule, on the up-to-the-timestate of advancement and quality ofperformed operations, on productiongoals and decision rules. This informationor knowledge is used to findout a heuristic solution to achievethe planned objectives, taking intoaccount the unexpected events. Regardingbehavior of resources, theyare autonomous, as products are.Resources have a specific knowledgebase containing required informationto perform operations.245