World Applied Sciences Journal 28 (1): 45-55, 2013ISSN 1818-4952© IDOSI Publications, 2013DOI: 10.5829/idosi.wasj.2013.28.01.1876

Corresponding Author: Ostroukh Andrey Vladimirovich, Department of Automated control systems , 125319, Moscow,Leningradsky Prospect, 64, Moscow Automobile & Road construction State Technical University,Hannover, Germany.

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Interactive Game Modeling Concept for PersonnelTraining at the Industrial Enterprises

Ostroukh Andrey Vladimirovich, Barinov Kirill Aleksandrovich,1 1

Nikolaev Andrey Borisovich and Stroganov Victor Yurievich2 3

Department of Automated Control Systems , 125319, Moscow, Leningradsky Prospect,1

64, Moscow Automobile & Road Construction State Technical UniversityTech. Sciences, Dean of the Faculty of "Control Management Systems", Moscow Automobile2

& Road Construction State Technical University, 125319, Moscow, Leningradsky Prospect, 64Department of Automated Control Systems , 105005, Moscow, Str. Baumanskaya 2-ya,3

5, Bauman Moscow State Technical University,

Abstract: The paper proposes a formalized representation of technological processes and queuing networkmodelling (QNM) to assess the temporal characteristics that allow implementing the mechanisms of modelingand parameterization of the local environments of the individual processes; in addition, we have developed aPetri net for the compatibility of logical conditions to the technological processes implementation using anevent approach; The theoretical and methodological basis of the study is the work of Russian and foreignscientists in the field of theory and practice of improving the managing complex, problems in the synthesis oforganizational management structures, the application of analytical and empirical methods and models basedon queuing and graph theory, as well as methods of simulation and statistical modelling based on the use ofmodern information technology and computer science. The practical value of the work lies is the developmentof methods for modeling the technological processes included in the simulation system of organizationalmanagement structures and others. The developed methods and algorithms have been tested and implementedfor practical use in a number of companies and are currently used in the educational process at the MoscowAutomobile & Road construction State Technical University (MADI).

Key words: Collaborative learning Business game Corporate learning system Distance learning Formalmodels and method Petri nets Queuing networks

INTRODUCTION multi-layered problem [1-25]. Principles and methods of

Organizational and technological level of the modern dependent on many factors. The most significant of theseindustrial enterprises is largely determined by the creation are the specifics of particular production, sets ofand application of effective mechanisms for the formation technological processes used, production volumes,and implementation of strategic development plans and capacity utilization, tactical, technical and qualitythe effectiveness of the operational management of all parameters of products, standardization and certificationproduction, logistics, organizational and economical issues, qualification level of technical, administrative andprocesses that aim to achieve high production management personnel, management system applied,profitability, development and improvement of organizational and legal form, regulatory and legalproduction. In this regard, building the organizational framework of the enterprise, organization of internal andstructure of the enterprise management is a complex external documents circulation.

the organizational structure development are directly

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Building the organizational structure in an industrial Direct subordination means an employee or aenvironment is a paramount task in relation to other tasks department is directly subordinated to another subject ofof the industrial process control. Formulation and solution industrial relations of a higher level. In this case, the headof this task at a high scientific and technical level is a of the unit gives orders to the subordinated subject inprerequisite for the effective organization of production, order to execute orders on administrative and functionalhigh competitiveness of products, growth of financial and issues. Direct subordination is the basis of creating aeconomic indicators, continuous dynamic development hierarchy of positions and departments.and improvement of production. Functional subordination is the subordination of one

Relevance of the topic is determined by the need entity (an employee or a department) to another entityto optimize the organizational structure of within the implementation of certain functions. In thisenterprise management as a "top-level" task to be case, the head of the unit only gives orders to theprimarily settled as a basic component of an effective subordinated subject within the framework of subordinateand successful functioning of any industrial enterprise, entity functions.regardless of the products purpose and production To simulate the behavior of staff in decision-makingvolumes. when managing production and technological processes,

Organizational Models Focusing on the Formation of The analysis of interactive gaming simulationDifferent Approaches and Principles of Production revealed the main options for organizing the businessProcesses Management: Control of all the production game (Fig. 1) and characterological features of theprocesses is implemented through the creation of a business game (BG), presented in the form of a functionalstructural diagram of units with specific production diagram (Fig. 2) [1-25].factors and other factors discussed above. In current Mathematical models that describe the technological,practice, there are the following approaches to the organizational and other processes in the gamingformation of units: simulation are subjected to numerical investigation with

Functional model: one unit corresponds to one The use of computer technology is not a pre-requisite,function; however, it contributes to the successful implementationProcess model: one unit corresponds to one process; of simulation process, providing several advantagesContracting party-oriented model: one unit [1, 2, 4, 6-8].corresponds to one counteragent. The time factor that is present and accounted for in

Contracting party-oriented model is useful in a limited outcome of the game. Changing the time scale makes itnumber of customers and is used by the following possible to cut down processing time, measured in daysscheme: one unit corresponds to one customer or a group and years, to minutes and hours, respectively. Due toof customers. repeated playback of different situations, the presence of

The most common are the functional and process feedback in the simulation system enables the participantsmodels as well as their various modifications. The paper of the game to learn and subsequently take more effectiveshows that the matrix structure combines the principles of decisions on the management of production processesfunctional and process systems construction. through analyzing the results.

the thesis proposes to use an interactive simulation.

subsequent quantitative decisions taken based on them.

BG imposes certain conditions on the process and

Fig. 1: Types of business games

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Fig. 2: Business game (BG) functional diagram

The use of BG in the process of personnel training logical conditions - interlocks in the network: O-interlockinvolves the use of didactic methods that are widely used not present; 1-interlock associated with the presence of ain teaching. Didactic principles of visibility, activity, limited size buffer; 2-interlock associated with theaccessibility, theory and practice connection, science, simultaneous process of an application by severalinterest and other principles are successfully used. network resources; 3-interlock associated with theEach BG implements these principles in appropriate presence of a limited number of applications in a certaindidactics in one way or another. network segment;...-other interlock types.

Analysis of Methods for Simulating Production and approximate, simulation or hybrid methods. The choice ofTechnological Processes in Terms of Temporal analysis method depends on the set of random processesCharacteristics: In terms of queuing theory, in the used for the description and analysis of the system, itsgeneral case, the system analysis model can be structure and type, the assumption of independence orrepresented as a closed or open QNM with arbitrary dependence of random variables, the distributionreceipt and service of applications in the network nodes, function, etc.the presence of logical conditions (interlocks) and It is proposed to use Petri nets in the work to simulatedifferent disciplines of service [6-8]. This analysis model logical conditions of the control mechanisms ofis given by the following sequence: M=(Q, El, P, L, , J, technological and production processes. In recent years,

), with Q being a set of nodes; El ={1, }-applications the classic networks were substantially expanded tosources capacity; P={P }-set of stochastic transition colored, time-related and other hierarchical structures.l

matrices for applications in the network; P ={p }- These extensions contribute to the modeling of complexl lij

probability matrix of l type applications transition from I processes in which data and time are important factors.node to the j node in the network; l L; L-multiple types of There are several reasons for using Petri nets inapplications; : Q ; -set of allowable types of QNMs technological processes modeling.in the network nodes; J: Q×L B, B = {b (l), (l)}-a set Formal semantics: the process described in terms ofi i

2

defining characteristics of service in the network nodes a Petri net has a clear and precise definition becausefor l type applications, with b (l) and (l) being the mean semantics of the classical Petri nets is formally defined.i i

2

and variance of l type application service time in i node; Graphical representation: Petri net is a graphical= {O, 1, 2, 3,...}-detects the presence or absence of language that simplifies communication with end users.

Such model can be analyzed using accurate,

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Fig. 3: Technology graph of a business process

Expressiveness: Petri nets support all the entities Methods of the Formalized Representation ofrequired for technological process modeling. All kinds of Technological Processes in the Form of QNM: Therouting that exist in modern process control systems can organizational structure is largely determined by thebe simulated. structure of technological processes flow [6-8, 10, 15, 21].

Analysis: Petri nets are characterized by a For this reason, construction of a model structure forvariety of analysis techniques. These techniques technological connections management is of interest.may be used to verify the properties of Petri nets Technology graph over a set of N vertices is a directed(safety, invariance, deadlocks, etc.). From this point of graph without loops T=<N, E >, arcs of which (u, v) Eview, it is possible to evaluate the effectiveness of are mapped to the r- dimensional vectors l (u, v) with non-alternative processes using standard analysis based on negative components: l :E R . The graph vertices arePetri nets. the basic operations of the company technological

Independence from the manufacturer: Petri nets processes or end executors. Relation (u, v) E in theprovide a mathematical tool for process modeling and technology field signifies that there is a r-component flowanalysis independent of specific tools. of raw materials, energy, information and other resources

Furthermore, the paper will provide an analysis of from u-element to the v-element; l (u, v) vectorsoftware technologies for creating simulation tools for components define the intensity of each component ofmanufacturing processes, organizational structures and the flow. An example of a process graph with two-business games, which revealed feasibility of the component flows is shown in Fig. 3. The numerical valuesfollowing technologies: COM (Component Object Model) of these components for each arc of the graph processfor implementation of the endpoints of bilateral BG client describe the amount of information needed for decision-and server parts cooperation; ActiveX-for the creation of making.separately developed and compiled, dynamically linked The graph vertices simultaneously representobjects that implement the user interface and the logic of operations and workplaces of the technological process.multiple-part game scenarios when combined in the The implementation of technological chain stages isperson of one player; TCP/IP Sockets-for two-way data performed by certain performers among whom there istransfer between endpoints for client and server parts of subordination and distinction. Each employee exercises athe BG; frame technologies (TFrame, reusable embedded specific set of technical operations. In this case, the loadpieces of user interface and logic)-for the implementation on each employee is different. As a result, the productionof scripts for the specific parts of BG or their automatic cycle generates an appropriate organizationalmodels; multithreading-for efficient query processing management structure with the necessary number ofof BG client parts by the server side; FirebirdSQL personnel.(free compact database with transaction support)-for BG In this paper, it is assumed that each production anddata storage, gaming composition, advancing log and the manufacturing process may be repeated any number offinal results of the BG; XML-XML-as a file format for the times. Moreover, individual implementations may beintermediate storage, import and export of technological parallel in time, using the same R resources. It is assumedprocesses structure model and the organizational that each implementation is launched by a certain initiator.structure description in the form of QNM and simulation In particular, the process can be represented by a trackprocess parameters. with repeating functions (Fig. 4).

T T

T

T T +r

T

T

H11{ I } { I }H22 H33 H44 H55 H66 H77 H99H88 10

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Fig. 4: An example of an abstract business process track

Fig. 5: An example of similar process descriptions

Fig. 6: Unified description of the Z and Z processes1 2

This paper considers the case of assigning processesthat are very similar in description, for example Z (track A)1

and Z (track B) (Fig. 5). Both tracks use equivalent2

operators h and h , but they interact with different input1 2

and output parameters. In this paper, we propose amethod for combining the description of such processes.To solve the problem, the initiator detection is enhancedwith a possibility of including the parameters. An orderedset of parameters represents the local environment of theprocess [4, 6-8, 22 - 25].

In this case, we can propose the following diagram offolding the descriptions of two processes (Fig. 5) into onegeneral description (Fig. 6).

As can be seen from the figures, I -initiator is1

associated with the local environment (a, e), whereasI -initiator-with the local environment (f, g). The operator2

h is modified into the operator h' , which is linked to the1 1

parameter b and the first parameter of the initiator’s localenvironment. The operator h' is connected to the2

parameters b, c, d and the second parameter of theinitiator’s local environment. The operators h' and h'1 2

shall mean the union. The initiators I and I are1 2

simultaneously present in this diagram.These arguments can be extended to the case of n

parallel running processes. Processes generated by atrack or a structure using the combined elementaryoperators and the local environment will be similar.

Thus, for the model Execution of customerorders the process is launched by an input streamP (applications received by the organization) andn

1

R (supply of goods required for the execution of orders)1

and ends with an output stream P (applications rejected),n2

P (request to the warehouse for goods reservation, withm5

m being the number of request), P (application to thej7

manufacturer for the supply of products, with j being thenumber of application submitted to the manufacturer), Pn

10

(orders delivered to customers) and P (orders collectedn11

by the customer).The implementation of the Execution of customer

orders (Fig. 7) includes 5 operations (O , O , O , O and1 2 3 4

O ), which have input and output objects, converted as a5

result of their implementation.This way, the input objects for receiving and

processing requests operation (O ) are requests received1

by the company (P ) and the rejected requests (P ) andn n1 2

requests accepted for execution (P ) are output objects.n3

The logic of converting the input object flow into theoutput is shown in Table 1.

In order to properly estimate the time and cost ofperforming an operation, refinement will be launch at thefourth stage. It will identify the sequence of actionsperformed, during which the objects (i.e. customer orders)that have a certain set of characteristics will be convertedinto concrete operations and transferred from one activityto another. So, for the operation receiving andprocessing requests , an object will be characterized byorder identification number (O ), name of the contracting1.1

authority (O ), bank account of the customer (O ) and1.2 1.3

other. When switching to another operation, objectsinherit a set of characteristics from the previous operation,gain new ones and lose the ones that are not necessaryfor the current operation.

It is assumed that the technological processoperation flow can be represented in terms of the classicalPetri net, which possesses certain properties. Let’s

P1

P2

P3

t1

t2 t3

t4

P4t5 P9

t7

t8

P10

P11

P5

R1 P6

P7

t6 P8 t9

World Appl. Sci. J., 28 (1): 45-55, 2013

50

Fig. 7: Execution of customer orders process diagram

Fig. 8: Petri net for the Execution of customer orders

Table 1: Converting the objects flow

Operation The flow conversion logical formula

O P P or P P1 1 2 1 3

O P R P P R or P R P P R2 3 1 4 5 1 3 1 6 7 1

O P R P or P R P3 4 1 9 8 1 9

O P R P R4 6 1 8 1

O P P or P P5 9 10 9 11

consider the model of a system , whose task is to carry0

the incoming applications. The model implements anevent approach, under which the functioning of thesystem is represented as a sequence of events that takeplace under appropriate conditions. An incident changesthe conditions, allowing new developments occur and soon. In this case, the system can be described as a Petri0

net

= { , P , T , F , M (0)}, (1)0 0 0 0 0

with being the discrete time, = 0, 1, 2,...; P = {p , …,0 1

p } - a set of nodes, called positions; T = {t , …, t } - an 0 1 m

set of nodes, called transitions; F = F F - incidence0 0 0P T

function; F = |f | -n×m matrix, F = |f | -m×n matrix, f0 ij 0 ji ijP p T t p

= 0-arc multiplicity from p to t , f = 0-arc multiplicity fromi j jit

t to p , i = 1, …, n, j = 1, …, m.j i

Petri nets that simulate the system class0

considered in this paper, have the following features.

These networks are safe; resource in each of theirm ( ) position can be 0 or 1. In this case, m ( ) = 1i i

indicates that there is a condition to perform aspecific system function. Accordingly, at m ( ) = 0i

this condition is not fulfilled.Multiplicity of all arcs of the considered network f p

ij

and f , i = 1, …, n, j = 1, …, m can only take thetji

values 0 or 1, indicating either no relationshipbetween the nodes, or transfer (or consumption) of asingle resource from node to node. Due to suchrelations, the network security is provided.

Petri nets for modeling the "Implementation ofcustomer orders" has the configuration shown in Fig. 8.

In this case, the network transitions-t , l = 1..9-matchl

the set of operations on the process implementation.Positions-P , i = 1..11-match the transition conditions.i

The arrows indicate the arcs connecting the vertices ofdifferent types. The objects-requests from various cliententerprises (P , i = 1..11, j = 1..n -j-request, entering the i-j

i i

position), received by the organization and resources(R1), corresponding to the goods in stock, act as markers(chips).

???? ??????????

???? ????????

Ki1

Kini

...Qi

Si

Kj1

Kjnj

...Qj

Sj

......

...

G

Kl1

Klnl

...Ql

Sl

Km1

Kmnm

...Qm

Sm

C3

...

... .... ..

...

...

...

...

...

...

…

.........

...

. ..

...

......

...

...

...

... ...

... ...

T1

Tk

...C1

... ... C2

( ) ( )

( ) ( ) ( )

1.. 1..

,t s

g t si i

i M i M

X P P P= =

= ∪

( )

( ) ( )

1..

,s

sys si

i M

Y H H=

=

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51

Fig. 9: The general structure of QNM supported by simulation modeling tool environment

Customer requests (P , P , …, P ) received by the1 2 n1 1 1

organization match the network input position (P ).1

Output position (P and P ) are complete orders10 11

delivered (P ) or received by the customer on the spotn10

(P ).n11

Thus, the system describes the topology of processmodels and rules of control transfer between the stages.At the physical level, these limitations corresponds to thestudy of the system without considering the resourceflow in the system.

The Presentation of Technological Processes as QNMModels with Descriptive Elements in the Form of PetriNets: A graphical editor that allows to structureparallel-sequential processes (Fig. 9) using visual meanshas been developed; serving nodes represent themultichannel queuing systems (servers) and the arcs aremarked with transition probabilities between them [8].

Server block may have any structure and consist ofan arbitrary number of nodes. The terminal block can alsoconsist of an arbitrary number of nodes connected inparallel. Points C , C , C in the QNM diagram are fixed1 2 3

nodes of the diagram, intended to be connected to asingle generator network, terminal block, server block anda circuit network by snapping to them with arrows of thecorresponding blocks. Each server S in the diagram editori

is represented as a single unit. Placement sequence ofblocks in the diagram and their connection is arbitrary.

The transition probabilities along the arcs betweenthe network nodes are bound by the followingrestrictions: P(C , •)=1, P(S , •)=1, the transition2 ?

probabilities for all other arcs equal to 1.The multitude X of all parameter nodes in the system

can be represented as:

(2)

where P is the multitude of generator parameters;(g)

P -the multitude of i-terminal parameters; M -the(t) (t)i

number of parameters in the QNM; P -the multitude of(s)i

i-server parameters; M -the number of servers in the(s)

QNM.The multitude Y of all system characteristics is

represented as:

(3)

where H is the multitude of system characteristics;(sys)

H -the multitude of i-server characteristics; M -the(s) (s)i

number of servers in the QNM.The following set of parameters is formed for the

construction of functional relationships

Y =f(X , X , c , t , c , t ), (4)(v) (v) (c) 1 1 cm cmstart end start start

X ={x }-the multitude of system nodes’ variable(v) (v)j

parameters,j=1..K , with K being the number of system nodes’(v) (v)

variable parameters,X X, with X being-the multitude of all parameter(v)

nodes in the system.X ={x }-the multitude of system nodes’ invariable(c) (c)

j

parameters,j=1..K , with K being the number of system nodes’(c) (c)

invariable parameters,

X =X/X , K +K =|X|(c) (v) (v) (c)

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Fig. 10: Process description

Y ={y }-the multitude of dimensional vectors N, one(v) (v)j

per each output (the one for which to the relation ofvariable parameters and system nodes is built) system ornode characteristic.

Y Y, with Y being the multitude of all system and(v)

node characteristics.

|{I}|=|x |=|y |=N(v) (v)j j

<c , t >-value of the number of processed1 1end end

applications and modeling time for stop condition of onemodel run;

<c , t >- value of the number ofcT cTstart start

applications processed and simulation time for thecondition to start collecting statistics during eachmodel run.

Thus, there has been developed a simulation modelin the form of a nine-phase QNM (Fig. 11) for theregistration process, accounting and control ofcomponents repair order execution (Fig. 10). FIFO is usedas a service discipline [6-12].

For the uniform intensity distribution ofapplications received and the implementation of theoperations ~R(1.5, 5), µ ~R(0.2, 0.5), µ ~R(0.3, 0.5),1 2

µ ~R(1, 3), µ ~R(0.3, 0.6), µ ~R(0.4, 0.5), µ ~R(1, 5),3 4 5 6

µ ~R(0.5, 1), µ ~R(0.5, 0.6) and µ ~R(0.1, 0.2),7 8 9

sample path diagram of the process and theautocorrelation function of its implementation time areshown in Fig. 12

Analysis of the process time autocorrelation functionshowed that it has overextended nature, i.e. the processis rather slow.

Fig. 11: Nine-phase QNM model for order execution

2 9002 8002 7002 6002 5002 4002 3002 2002 1002 0001 9001 8001 7001 6001 5001 4001 3001 2001 1001 0009008007006005004003002001000

313029282726252423222120191817161514131211109876543210

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a) sample path of the process

b) autocorrelation functionFig. 12: Characteristics of repair order execution

CONCLUSION and business games aimed at assessing the personnel

Thus, we have carried out a systematic analysis of making.the methods and models used to describe production and There has been developed a formalized process-technological processes in industrial enterprises and oriented description of the parallel to serial execution ofassociations, organizational structures, process control process flow diagrams’ individual stages in the common

qualifications in terms of the effectiveness of decision-

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space of resources (such as technological lines, magazine 2011. 9. Online access: http://technopersonnel, energy resources, etc.), which allows to mag.edu.ru/doc/207618.html (connection dateimplement the mechanisms of modeling and 27.09.2013).parameterization of local environments of the individual 8. Barinov, .A., 2013. B.S. Goryachkin, L.V. Ivanovaprocesses. and A.B. Nikolaev, Formal models of representation

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