Urals State Technical University UPI Mira st., 19 Mira st., 32 Ekaterinburg, , RUSSIAN FEDERATION Ekaterinburg, , RUSSIAN FEDERATION

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1 Proceedings of the 2008 Winter Siulation Conference S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. INDUSTRIAL ENTERPRISES BUSINESS PROCESSES SIMULATION WITH BPSIM.MAS Konstantin A. Aksyonov Eugene A. Bykov Elena F. Soliy Alexey A. Khrenov Dept. of Autoated Control Systes NPP Business Support Systes Urals State Technical University UPI Mira st., 19 Mira st., 32 Ekaterinburg, , RUSSIAN FEDERATION Ekaterinburg, , RUSSIAN FEDERATION ABSTRACT Necessity to ind a huge aount of factors while developing odern enterprises odels dictates new requireents for odeling software, which needs to process all data to achieve precise results, ake use of all possible eans for that, such as distributed calculations and introduce original approaches where possible to avoid extra tie waste for ultiple siulation experients. The article focuses on the software apparatus used in distributed ulti-agent resource conversion processes based tool BPsi.MAS, pointing out its advantages and describing used technologies. Second part describes BPsi.MAS deployent in Urals Industrial Group, CJSC, which allowed incoe and arket share growth. Various pricing strategies are discussed and active/passive copetitors behavior is considered. 1 INTRODUCTION One of the prospective directions of decision support systes (based on siulation and odeling (SM) syste and expert syste (ES)) tool developent is the probleorientation, which allows reducing the requireents to end-user knowledge level, especially in the prograing area. The paper presents results of developent and deployent of the ulti agent resources conversion processes (MRCP, ulti-agent RCP) theory and SMES tool BPsi.MAS, designed on the basis of that theory. Table 1 shows the coparison of popular siulation odeling tools, such as ARIS ToolSet (T), G2 (G), Any- Logic (A) and BPsi (B), a basis for BPsi.MAS. As we can see, all current systes lack support of soe features that ight be useful in effective siulation. For exaple, agent-based approach ipleentation is liited. Another disadvantage of two ost powerful systes, ARIS ToolSet and G2, is a very high retail price, which ight stop a potential custoer. Regarding the disadvantages a new tool was designed. BPsi.MAS is based on the proved efficient apparatus of resource conversion processes, fully ipleenting ultiagent approach, utilizing analytical odeling for certain subject areas and distributed calculations for coplicated processes odeling as well as introducing other convenient features. BPsi.MAS is deployed on enterprises in the Urals Region, the achieved results are presented later in this paper. Table 1: Modeling tools coparison Coparison criteria T G A B Subject area conceptual odel design RCP description language Systes goals definition: Graphical Balanced ScoreCard based Hierarchical process odel Coands description language Use of natural language for odel definition Multi-agent odeling availability Agent eleent Agents behavior odels Agent s knowledge base support Message exchange language Siulation odeling Expert odeling Situational odeling 2 MATHEMATICAL MODEL OF THE MRCP In this research, we will define the resource conversion process (RCP) as the process of an input conversion (resources necessary for process execution) into output (products outcoes of process execution). The goals of RCP subject area are: design of new RCP and existing RCP perfection, resources and conversion device state forecast, process tie and cost characteristics es /08/$ IEEE 1669

2 tiation, resources costs and echaniss usage tie estiation. A coparison of visual proble-oriented decision support syste (DSS) based on SM and ES (SMES) is already presented in this paper (see Table 1). The ain disadvantages of visual proble-oriented ulti-agent SMES tools such as AnyLogic, ARIS, G2/ReThink, in the area of RCP, are the coplexity of the RCP definition and experients ipleentation, weak resources and echaniss conflicts odeling tools, absence of intelligent agent library, no Russian language support. In this paper we will present atheatical odel of the RCP and SMES syste BPsi.MAS, which is substantially free fro the above entioned disadvantages. Creation of the RCP atheatical apparatus is based on the widespread atheatical scheas of dynaic processes description, such as Petri nets (Avrachuk, Vavilov and Eelianov 1988), queuing systes (Avrachuk, Vavilov and Eelianov 1988; Pritsker 1984) and syste dynaics odels (Avrachuk, Vavilov and Eelianov 1988; Pritsker 1984; Forrester 1961). However, it is difficult enough to present all the features of the RCP with the help of specified odels. The ain objects of discrete Multi-agent RCP are presented on Figure 1: operations (Op), resources (Res), control coands (U), conversion devices (), processes (PR), sources (Sender) and resource receivers (Receiver), junctions (Junction), paraeters (P), agents (Agent). Process paraeters are set by the object characteristics function. Relations between resources and conversion device are set by link object (Relation). The agents existence resues availability of the situations (Situation) and decisions (action plan) (Decision). The RCP eleents take part in essage exchange and perfor their conversion functions on the base of their behavioral odels (state graphs) following the incoing essages. The fraes are selected as an agent s knowledge representation language. A k-operation (Op k ) can be represented by the following structure: Op k =<f, in, out, u, h Op, c a, ech, Status Op, prior Op > (1) In (1) f is a function ipleented by an operation. It is possible to forecast an output (out) on an input (in) with the help of f ; in = {in 1,, in n } is a set of inputs, characterized by the type (aterial, financial, inforation, power, labor) and quantity; out = {out1,,out} is a set of outputs, out = f (in), characterized by the type and quantity; u = {u 1, u y } is a set of control coands; h Op = {h Op 1,,h Op k } are the characteristics of the operation; C a is a start condition of the operation; ech = {ech 1,,ech q } are the conversion devices, that are characterized by the type and quantity; Status Op = {wait, active, lock} is the operation state, defined as a finite set of states: wait is waiting, active is execution, lock is interruption; prior Op is the operation priority. The start condition (C a ) is defined in (2): C a (t) = C a in (t) C a out (t) C a u (t) C a ech (t) C a status (t) C a tie (t) (2) In (2) C a in is a condition of necessary input resources; C a out is a condition of the output registration liitations; C a u - a condition of deterinant control coands availability; C a ech is a condition of necessary conversion devices availability; C a status is a condition of fulfillent availability; C a tie is a start on tie condition. The transition of the operation in the "execution" state is attended by execution of input resources capture A in RES and conversion devices A in MECH. Being in the "execution" state, the operation can jup into the "interruption" state. The operation can be interrupted to provide the execution of another operation. Passing into the "interruption" state the operation stores the oent of stopping and releases entrapped conversion devices A Lock MECH. The presence of free conversion devices C a ech (t) is checked during the "interruption" state. The operation will be in the "interruption" Sender1 Res1, Res2 U1, U2, U3, U4 PR1 Res6, Res7, Res8 P1, P2 Receiver1 А1 (АГЕНТ) Res1 Res2 Ca2 Op2 U1 Res3 Ca_J1 U2 Junction1 R5 Messa ge1 Ca3 Ca4 U3 Op3 4 U4 Res6 Res7 А Messa ge2 Op4 Res8 5 Figure 1: Hierarchical Multi Agent RCP: PR1 process works out in constituents Op2, Op3, Op4, Junction1 and corresponding resources and tools; agents A1 and A2 control different RCP levels 1670

3 state until necessary conversion devices are released. In case of C ech a (t)=true the operation jups into the "execution" state the conversion devices A MECH UnLock are captured and the execution is prolonged. Agents control the RCP objects. There is a odel of the decision-aking person for every agent. An agent (software or hardware entity) is defined as an autonoous artificial object, deonstrating active otivated behavior and capable of interaction with other objects in dynaic virtual environent. In every point of syste tie a odeled agent perfors the following operations: environent (current syste state) analysis; state diagnosis; knowledge base access; decision-aking. Thus the functions of analysis, situations structuring and abstraction, as well as resource conversion process control coands generation are perfored by agents (Figure 2). Siulation engine algorith of agent-containing odel consists of the following ain stages: current point of syste tie calculation; agent activity processing (state diagnosis, control coands generation); conversion rules queue foration; conversion rules execution and work eory state, i.e. resources and echaniss values, odification. Siulation engine accesses the expert syste unit, i.e. agent knowledge base, in order to diagnose current state and generate control coands. Each agent possesses its i. knowledge base, ii. set of goals that are needed for behavior configuration setting, iii. priority that defines agent order in control gaining queue. Generally in case of any corresponding to agent s activity situation an agent tries to find a decision (action scenario) in the knowledge base or work it out itself; akes a decision; controls goals achieveent; delegates the goals to its own or another agent's RCP objects; exchanges essages with others. The operation is in the "execution" state while t < t k End, where t k End is the terination of k-operation oent. The operation jups into the "idle" state when the condition t = t k End is et. This jup is attended by the creation of output resources A RES out operations and the release of seized conversion devices A MECH out. The syste graphs apparatus was applied for the representation of an RCP hierarchical organization and integral characteristics calculation (Avrachuk, Vavilov and Eelianov 1988): (3) PR L= i { PR pi L= j =< { Sender ; p i = 1,..., n Op p L= j j Re ceiver Junction k } = 2,..., ;{Re lation } = >. i AB L i Agent } ; L= i In (3) the graph of i-level integration will be foratted as a result of step-by-step integration of the graphs, PR 2,..., PRi 1 PR with j-level integration processes (subprocesses) set foration {PR p L=j; p=1,, n p L=j} on each j- stage, L - level of integration. Units of the RCP set {Sender Op Receiver Junction Agent } L=I {Sender Op Receiver Junction Agent } L=i-1 { Sender Op Receiver Junction Agent } and resource ratio set {Relation k AB } L=i {Relation k AB } L=i-1 {Relation k AB } of the syste graph PR L=i are RCP units and units resource ratio, also Sender Op Receiver Junction Agent units and Relation k AB resource ratio of the first level of integrations syste graph PR which doesn t include step-by-step integration in one process PR p L=j (Figure 3). Each top of the syste graph RCP is characterized by attributes (etrics) set h 1,, h z. All sets of syste graph tops attributes specify attributive set. The calculation of integral etrics of processes (syste tops) h 1,, h z on an arbitrary i-level (i > 0) is set above the tops of the (i-1)- level of integration. Figure 2: Use-case diagra deterines relations between agents and RCP eleents 1671

4 The possibility of the use of typical process description odels for the creation of the atheatical odel of the RCP is exained; the typical process description odels are: the augented Petri networks; the queuing systes. It s shown that given odels don t allow aking an adequate representation of the RCP. There are soe disadvantages revealed for the Petri nets: absence of tiing; absence of the concurrent activities conflicts; the lack of the division of ark types (resources types); odels of real processes described in the ters of Petri nets are bulky and badly readable. Owing to the fact that the change of the N E scheas has only two positions, there is no ability of process interrupt odeling. The conceptual apparatus of the Q-scheas are not corresponding with the proble area of the RCP, Q-scheas are oriented on the odeling instruents activity, and in the RCP there are odeling of the consecution and paraeters of the conversion processes. In (5) V_ips is the choice process fro Bps and fro Rps subset of the active rules Bps_v and active data Rps_v, which will be used in the next interpreter cycle; S_ips - the rules and data coparison process; K_ips - conflicts resolving process (or planning process), which is defining, what coparisons will be fulfilled; W_ips - selected copared rule execution process. The results are the data odification in Rps. The structure of a transforation rule which corresponds to the structure of the operation Op k is defined: MECH RULE k = < C a(t), A IN (tca), A Lock (t Lock ), (6) MECH RULE AUnLock (tunlock), AOUT(tEnd),Status, tierule, prior, kind_prior, break_off MECH ALock (tlock) = ALock (tlock), ALock (tlock) (7) RES MECH AUnLock (tunlock) = AUnLock (tunlock),aunlock (tunlock) (8) In equations (6), (7), (8) A IN (t Ca ) = A RES in (t Ca ), A in- MECH (t Ca ); A OUT (t End ) = A RES out (t End ), A MECH out (t End ); Status- RULE ={wait, active, lock, done} are the states of a rule, where done - the rule is fulfilled; tie RULE is the duration of conversion process; kind_prior is the type of priority (relative, absolute); break_off = {true, false} is the tag of the interruption prohibit, if there is "true" the rule cannot be interrupted; A RES Lock is the resources expenditure, including the tie resource, necessary for stopping the operation Op i ; A RES UnLock is the resources expenditure, including the tie resource, necessary for resuing the operation Op i. The rule status transition graph is presented on (Figure 4). >, RES Launch condition check state Next point of tie Operation executed, not finished Done state Operation executed Launch condition et Execution state Necessary echaniss available Figure 3: Syste graph of ulti-agent RCP The apparatus production rules are used for building a kernel of siulation syste (Jackson 1998, Newell 1973). The structure of the production rules of the RCP syste is defined as: PS = < Rps, Bps, Ips > (4) In (4) Rps={RES(t)} {MECH(t)} is the current state of resources and conversion devices (operative storage); Bps is a set of resources transforation rules (knowledge base); Ips is an inference engine. The interpreter can be represented by: Ips = < V_ips, S_ips, K_ips, W_ips >, (5) Interruption state Coon echaniss use conflict with higher priority operation Figure 4: The rule status transition graph The value Ca(t) is checked during «Launch condition check» status. If Ca(t) = true, then the rule in the sae instant (t=t Ca ) passes in a "activity" status Status RULE = active. Operations Active IN (t Ca ) fulfilled in this status are: capture of the necessary input resources and echaniss, calculation of costs and teporary etrics of a rule. The rule transition in the "interruption" status can be caused by necessity of resources, which are essential for 1672

5 the higher priority rule releasing. In this status the echaniss Active Lock MECH are released and the stop oent is stored. Free resources availability is checked during the "interruption" status C a ech (t). The rule is in the interruption status until the necessary echaniss will be released. In case of successful check, the rule passes in a "fulfillent" status: Status RULE = active. The transition in this status is accopanied by the echaniss capture operation Active UnLock MECH. At the tie t=t End the rule is finished (transition to the "fulfilled" status), the operations Active OUT are fulfilled: the output resources are created, the captured resources are released, the values of output paraeters are calculated and the essage of the process copletion is generated. It is accepted, that the conflicts can arise at the process execution in the following cases: on input resources; on the echaniss. The conflicts resolving between different types of the rules ("operation", "junction", "source", "receiver") are realized on the rules execution planning echanis, which are ibedded in the inference engine. Inside the "operation" rules the order of execution is regulated on the priority - the axiu priority rule will be treated earlier. For each "operation" rule the priority is set at the odel construction. Each rule is characterized by the priority class: relative or absolute, and also by the possibility of the interruptions prohibition. The scale of priorities for the rules with relative and absolute priority is coon. The priority varies fro 0 up to M (0 - best priority). In existing siulation odeling systes the echanis of the inference engine will be realized as follows (Pritsker 1984): the rules are activated by the inference engine; thus the truth of a condition (IF) is checked; if it is true, the output achine fulfils operations were in the conclusion (THEN). The operation algorith of the inference engine consists of the following ain stages: 1) definition of a current instant SysTie = in Tj, j {RULE} (standard algorith of the discrete-event siulation (DES)); 2) agent s actions processing (current situation diagnosis, executive instruction working-out); 3) queueing of transforation rules; 4) execution of transforation rules and operative storage state transition. Iitator applies to the expert syste odule for the current situation diagnosis and executive instruction working-out. The ain disadvantage of the direct and return output, that is usual for a static expert syste, is the unpredictability of the execution tie. For dynaic systes direct and return output with an exhaustive search possible to execution rules is the inadissible luxury. With the purpose of calculation iniization it is offered to use the inference engine algorith, in which the odel tie registration is organized with the use of the centralized events calendar. The ain ethods of speeding up include: event ethod of prooting on tie, lists of different types of the rules, bringing in additional attribute to structure of the working storage units. The use of these ethods in the evaporator achine-building production siulation allows reducing the tie of realization of one achine experient fro 7 hours 53 inutes to 48 inutes. The achinebuilding process consists of 420 technological operations. That confirs an Ulrich hypothesis about the registration of events allocation in tie experientally. He has achieved high saving of a achining tie; using that in digital logical networks leads to the only 1 % of units is siultaneously active (Avrachuk, Vavilov and Eelianov 1988). In the evaporator task only 3 % of the average were active. Coparison of algoriths speeds up exhaustive search (A_p) and it is optiized (A_opt) (Figure 5). Experient Tie, sec A_p A_opt Tie interval Figure 5: Coparison of algoriths 3 MULTI-AGENT SMES SYSTEM BPSIM.MAS The proble-oriented SMES syste is developed on the basis of surveyed odel. SMES package of the RCP BPsi.MAS is worked out on the basis of the following eans: Borland Delphi 7 and database control syste MS SQL Server. SMES syste BPsi.MAS provides execution of the following functions: the creation of dynaic odel RCP (Aksyonov, Klebanov and Khrenov 2003; Aksyonov et al. 2005; Aksyonov et al. 2006); siulation; analysis of the siulation experient results; obtaining reports on results; export of experiental results in MS Excel and MS Project forats. Phases of work with BPsi.MAS are presented on Figure 6. The process consists of the following phases: subject area conceptual odel design; MRCP situational odel design and experients carrying out. The syste is based on the proble-oriented siulation syste BPsi (Aksyonov, 2003). Such approach to dynaic situations odeling systes (DSMS) is discussed in (Philippovich, 2003) where a DSMS is based (extends) a different proble-oriented siulation syste. The syste utilizes a frae-based approach (Shvetsov, 2004) for the purpose of subject area conceptual odel design. This approach relies on frae-like structures and 1673

6 conceptual graphs constructions overlapping (Sowa, 1976; Sowa, 1984; Sowa, 2000). Differentiation of active and passive fraes and object behavior consideration are aong the advantages of such approach. Multi-agent RCP situational odel design process consists of the phases, presented on Figure 6. Aksyonov, Bykov, Soliy and Khrenov Figure 7: UIG, CJSC ain process, presented in BPsi.MAS If an existing odel does not ipleent agent-based approach, one can easily add agents to it. After the agent properties are set we can switch on or off its participation in siulation. Figure 8 deonstrates agent properties window. Goal setting and its achieveent control is presented on Figure 9. Figure 6: Work with BPsi.MAS, phases (left) and MRCP situational odel design stages (right) 4 BPSIM.MAS DEPLOYMENT BPsi.MAS was used for ulti-agent dynaic odel developent of Urals Industrial Group, CJSC (further referenced as UIG). The ain reason for odeling is UIG behavior algorith and pricing strategy developent, targeting share of the arket growth and transition to higher technological level, increasing enterprise copetitiveness. Fragent of the odel is presented on Figure 7. Model akes use of the following paraeters: enterprises (share of product arket; sales volue; preises price per square eter; processes tiing data); copetitive environent (nuber of copetitors on arket, share of the arket, strife intensity, copetitors prices, reaction on tie and price, estiated copetitiveness rate, elasticity of deand on price, deand seasonality, arket capacity). Model describes the following enterprise processes: production process; sales process; products installation to enduser; after-sales service. A nuber of experients targeting the search of effective pricing strategy and considering various agents- copetitors behavior sets (active/passive) were run. Figure 11 presents the output data, which are various strategies, resulting in two sall copetitors displaceent fro the arket. Figure 8: Defining agent settings Figure 9: Goal achieveent progress Agents do have equivalent decision-aking individuals odels (head of sales, production, engineering departents, etc.) or copetitive enterprises odels. Figure 10 presents a knowledge base fragent of a certain agent, Art-Line copany, which is a copetitive copany for the Urals Industrial Group. The knowledge base eets the 1674

7 BPsi.MAS forat and is filled within this product. Unfortunately it is ipossible to translate the whole knowledge base into English, so only the captions were translated. An operator (BPsi.MAS end user) is able to define agent s action in every particular situation. In order to do this he needs to fill the required data including IFs and THENs. The table on Figure 10 has several other fields. is the a sequence nuber of a particular situation. SITUATION NAME corresponds to the coon nae of the situation, e.g. Reaction to price changes. IF is defined with syste language and corresponds to the launch condition of the situation, e.g. when copetitive price is lower than 1000 units, which ight look like fres95>1000 in syste language. THEN looks uch alike with IF, is defined in syste language and corresponds to situation action, e.g. lowering own price or fres99:=fres95-5 in syste language. DESCRIPTION field is used to fill additional rearks on the situation, e.g. The reaction to price changes lowers own price by 5 units. The given situation exaple is very siple, the language allows uch ore coplicated definitions which can be seen on Figure 10, language independent fields IF and THEN. A nuber of experients targeting the search of effective pricing strategy (aong those presented on Figure 11) and considering various agents- copetitors behavior sets (active/passive) were run. Figure 12 presents the output data, which are various strategies, resulting in two sall copetitors displaceent fro the arket. Figures 13 and 14 present the dependencies of arket share and price with an active copetitors behavior odel and abrupt UIG price changes, resulting in enterprise s segent of arket growth, ade possible due to its leading role in price dictation together with unavailability of larger copetitors to change prices rapidly, having longer price reaction tie. Figure 12: Copetitors displaceent fro arket (copetitors passive behavior) Figure 10: Copetitive agent s knowledge base Figure 13: Market share changes: abrupt price changes together with active copetitors behavior Figure 11: Pricing strategies resulting in copetitors displaceent fro arket Figure 14: Copetitors reaction to abrupt price changes Sooth price changes together with active copetitors behavior odel do not result in noticeable segent of arket growth. Figure 15 presents UIG incoe variation depending on various pricing strategies. Rows on Figure 15 represent 1675

8 the following strategies, according to row nuber in the legend: 1. Sooth price decrease fro 5500 RUR in 50 RUR intervals per iteration for a period of one year. After reaching 4500 RUR price increases to 5500 RUR with copetitors passive behavior. 2. Abrupt price decrease fro 5500 to 4500 RUR in the first quarter with copetitors passive behavior. 3. Soft price decrease fro 5500 to 4500 RUR in the first half-year with copetitors passive behavior. 4. Soft price decrease fro 5500 to 4500 RUR in the first half-year with copetitors active behavior. 5. Abrupt price decrease fro 5500 to 4600 RUR in the first quarter with copetitors active behavior. 6. Periodical price decrease fro 5100 to 4600 RUR (interittent pricing strategy) taking into consideration copetitors activity with copetitors active behavior. 7. Periodical price decrease fro 5470 to 4970 RUR taking into consideration copetitors activity with copetitors active behavior. 8. Abrupt price decrease fro 5500 to 4280 RUR in the first two onths with copetitors active behavior. 9. Abrupt price decrease fro 5500 to 4200 RUR in the first two onths with copetitors active behavior. 10. Soft price decrease fro 5500 to 4160 RUR in the first seven onths with copetitors active behavior. 11. Soft price decrease fro 5500 to 4180 RUR in the first five onths with copetitors active behavior. 12. Abrupt price decrease fro 5500 to 4300 RUR in the first two onths with copetitors active behavior. 13. Abrupt price decrease fro 5500 to 4300 RUR in the first two onths with copetitors passive behavior. 14. Soft price decrease fro 5500 to 4160 RUR in the first half-year with copetitors passive behavior. Rows 6 and 7 are based on abrupt price changes and active copetitors behavior odel. Price and arket share variation for row 7 were presented earlier on Figures Rows 2 and 5 show reasonable incoe rate, but these experients were carried out with passive copetitors behavior odel. Figure 16 presents UIG arket share variation depending on various pricing strategies. Rows 6 and 7 based on abrupt price changes and active copetitors behavior odel correspond to annual incoe of 1.9 and 2.6 illion dollars together with arket share growth to 22 and 20 percent respectively. Experients 2 and 5 show reasonable results in arket share but consider passive copetitors behavior which cannot be relied on. Experient 8 does not eet the incoe requireents. The difference between experients is caused by the variation of initial values, odel characteristics, etc. Graphics are achieved with the use of BPsi.MAS output data that is collected fro experients. Figure 15: Incoe depending on various pricing strategies Figure 16: Market share depending on various pricing strategies After a series of experients a pricing policy, resulting in share of the arket growth fro 6.6% to 20-22%, was deterined. Liiting to current proble the optial values of processes characteristics were calculated. The projected saving rate fro the odeling results ipleentation is estiated by $1.9 illion per year. In addition, optial values for the nuber of distribution points and ounting units depending on seasonal deand and applied to the current pricing strategy were calculated in the fraework of the current project. 5 SUMMARY The designed dynaic situations ulti-agent RCP odeling syste BPsi.MAS is based on discrete-event siulation odeling and is integrated with an expert syste. Multi-agent approach to resource conversion processes dynaic odel increases its intellectuality with expert, situ- 1676

9 ational and siulation odeling integration, as well as allows new proble classes to be solved. Such probles include anageent processes odeling and resolving of resource liitations based conflicts eerging between decision-aking people in ulti-agent environent. ACKNOWLEDGEMENTS The research is carried out with Start progra support under state contract #5058P/7296 between sall scientific and technical sphere enterprises extension work fund and NPP Business Support Systes Ltd as well as within the bounds of the President of Russian Federation grant #MK REFERENCES Aksyonov, K., B. Klebanov, and A.Khrenov Coputer-aided design syste of siulation business process odel, Proceedings of the 4th IMACS Syposiu on Matheatical Modeling, ARGESIM Report no. 24. Austria, Vieena University of Technology. Aksyonov, K.A., BPsi siulation syste user anual. Ekaterinburg, 58 p. Aksyonov, K.A., E.F. Soliy, N.V. Goncharova, A.A. Khrenov, and A.A. Baronikhina Developent of Resource Conversion Processes Model and Siulation Syste // Proceedings of the EUROCON Serbia & Montenegro, Belgrad. Aksyonov, K., E.Soliy, N.Goncharova, A.Khrenov, and A.Baronikhina Developent of Multi Agent Resource Conversion Processes Model and Siulation Syste, Coputational Science, ICCS 2006: 6th International Conference, Proceedings, Part III. Lecture Notes in Coputer Science, Volue UK, Reading. Avrachuk, E.F., A.A.Vavilov, and S.V.Eelianov Technology of syste siulation, M.: achine construction industry; Berlin: Techniques, 520 p. Forrester, J Industrial Dynaics, Cabridge, MA: MIT Press. Jackson, P Introduction to Expert Systes, West Group, Rochester, NY, Addison-Wesley. Newell, A Production systes: odels of control structures // Visual inforation processing, New York: Acadeic Press. Philippovich, A. Yu., Situational, siulation and expert odeling integration in printing industry. Moscow, p Pritsker, A. A. B., Introduction to siulation and SLAM II. Syste Publishing Corporation, West Lafayette. Shvetsov A.N., Corporate intellectual decision support systes design odels and ethods. DPhil research paper. St.Petersburg, Russia, p.461. Sowa, J. F., Conceptual graphs for a database interface. IBM Journal of Research and Developent, 20:4. P Sowa, J. F., Conceptual Structures : Inforation Processing in Mind and Machine. Reading, MA : Addison - Wesley. 481 р. Sowa, J. F., Knowledge Representation : Logical, Philosophical, and Coputational Foundations. Pacific Grove, CA : Brooks / Cole Publishing Co. 594 p. AUTHOR BIOGRAPHIES KONSTANTIN A. AKSYONOV (1977) received the coputer science engineer degree fro the Ural State Technical University (USTU UPI) in He has received his Ph.D. fro the USTU UPI in Aksyonov is the assistant professor of the Coputer-based syste departent of the USTU UPI. He is a deputy dean in the sphere of the scientific research. The orientations of the research study: siulation odeling of the processes and systes, inforation technologies, business-processes autoation, inforation systes designing, strategic control. A scholar of the President of Russian Federation in The winner of the contest The future of the city Ekaterinburg in the view of the young scientists, The winner of the contest for the best research study work USTU UPI, Has got a Urals Region Governor Prize for the young scientists in 2005 and a grant of the President of Russian Federation for the young scientists in Konstantin A. Aksyonov is a IEEE Meber. Contact: <wiper99@ail.ru>. ELENA F. SMOLIY received the coputer science engineer degree fro the USTU UPI in The orientations of the research study: siulation odeling of the processes and systes, inforation systes designing. A prize-winner of the region contest for the best research study work in ALEXEY A. KHRENOV received the coputer science engineer degree fro the USTU UPI in The orientations of the research study: siulation odeling of the processes and systes, inforation technologies, inforation protection. EUGENE A. BYKOV received the coputer science engineer degree fro the USTU UPI in Currently he is a graduate student of USTU-UPI. Main subject of research is coputer siulation of ulti-agent RCP. He is a IEEE eber, Syste, Man and Cybernetics society eber. Contact: <oedius@gail.co>. 1677