=Paper=
{{Paper
|id=None
|storemode=property
|title=Virtual Factory Data Model to support Performance Evaluation of Production Systems.
|pdfUrl=https://ceur-ws.org/Vol-886/paper_7.pdf
|volume=Vol-886
}}
==Virtual Factory Data Model to support Performance Evaluation of Production Systems.==
https://ceur-ws.org/Vol-886/paper_7.pdf
Virtual Factory Data Model to support
Performance Evaluation of Production
Systems
Walter TERKAJa, Marcello URGOb
a
Istituto Tecnologie Industriali e Automazione (ITIA), Consiglio Nazionale delle
Ricerche (CNR)
b
Dipartimento di Ingegneria Meccanica, Politecnico di Milano
Abstract. The performance evaluation of manufacturing systems is a critical and
difficult task to be addressed throughout the factory life-cycle phases, including
the early design, detailed design, ramp-up, reconfiguration, and monitoring. An
efficient and effective performance evaluation may have a relevant impact on the
profitability of an industrial company. This paper addresses the application of a
data model for virtual factories to the performance evaluation problem, aiming at
exploiting the interoperability with other software tools to continuously update the
virtual representation of a manufacturing system, so that accurate estimations can
be obtained. A test case is described and then used to check the viability of the
proposed approach in the case of Discrete Event Simulation (DES) based on a
commercial software tool like Arena.
Keywords: Performance Evaluation, Discrete Event Simulation, Data Model,
Ontology, Manufacturing Systems, Virtual Factory
1. Introduction
The design of manufacturing systems is a complex task strictly related to
manufacturing strategy decisions having an impact on a long time horizon (usually
more than two years) and involving a major commitment of financial resources [1]. For
instance, strategic decisions may regard the number of plants or facilities to be built,
their size and their location, the variety of products to be manufactured, the
manufacturing technology to be used and, within a plant, the number and type of
production resources, the characteristics of the transportation and handling systems, the
degree of automation. The complexity of these decisions and their importance from the
point of view of the profitability of capital investments emphasizes the need to have
formal and structured approaches to evaluate the performance of a manufacturing
system.
Usual performance indicators in a manufacturing context can be the production
volumes, the quality of the output, the incurred cost, etc. In addition, more detailed
performance indicators may be calculated, e.g. the utilization of production resources,
the average flow time of products, the average level of the work in progress. Different
models can be used to address specific types of analysis and levels of detail while
modeling a manufacturing system to evaluate its performance. In the field of discrete
part manufacturing, two main approaches are in common use:
Analytical models using mathematical or symbolic relationships to provide a
formal description of the system [2] [3]. The model is then used to derive an
explicit expression of a performance measure or, in most of the cases, to
define an algorithm or a computation procedure able to calculate the
performance indicators.
Simulation models represent the events occurring in a manufacturing system in
its operation by a sequence of steps that are executed in a computer program
[4] [5]. This sequence of steps is generated with respect to a set of rules
modeling the behavior of the system. Therefore the characteristics and
relationships between the elements in a manufacturing systems can be
described in detail. However, the higher is the detail level and the higher is
required computational effort. If a simulation model is run for a sufficiently
long time, then proper statistics can be collected and performance indicators
can be estimated.
Simulation models enable the representation of an higher level of details, thus
providing more accurate estimates of the manufacturing system behavior compared to
analytical models. However, to reach this level of details, also a more detailed
formalization of the manufacturing system is needed. Simulation modeling of
manufacturing systems usually relies on commercial software tools (e.g. Arena, Simio,
Plant Simulation, Visual Components, etc.) providing an integrated environment to
describe the system and its behavior in terms of relationships and rules and, in addition,
to deal with the generation of random values and the collection of the statistics.
Performance evaluation tools can be more effective if they are based on a virtual
representation of the manufacturing system that is continuously updated during both
the design and operational/execution phase, thus guaranteeing an overall coherence of
the obtained results. Moreover, the generation of a simulation models and/or analytical
model can be time-consuming and it would be beneficial if this activity could be as
much automated as possible. The resulting need of interoperability between
performance evaluation tools and tools supporting the design and management of real
industrial systems can be met by an extended framework enabling:
the cooperation among different actors with different competences and
expertise in the design and management of a factory based on common
definitions and a shared virtual representation of its components linking
different manufacturing domains while guaranteeing their coherence;
the management and update of a huge amount of manufacturing data made
available through standard and interoperable interfaces.
The development of a framework for the interoperability between software tools
supporting factory processes is currently carried out by the European project “Virtual
Factory Framework” [6]. The Virtual Factory Framework (VFF) can be defined as “An
integrated collaborative virtual environment aimed at facilitating the sharing of
resources, manufacturing information and knowledge, while supporting the design and
management of all the factory entities, from a single product to networks of companies,
along all the phases of the their lifecycles” [7]. The VFF architecture is based on three
main pillars:
Virtual Factory Data Model (VFDM), i.e. a coherent, standard, extensible, and
common data model for the representation of factory objects related to
production systems, resources, processes and products [8].
Virtual Factory Manager (VFM), i.e. the manager of a shared data repository
containing factory data that can be accessed and modified by all the software
tools integrated in the framework [9] [10].
decoupled Virtual Factory modules, i.e. the software tools that are able to
communicate with the VFM to retrieve and send shared data formalized
according to the VFDM (e.g. [11]).
This paper focuses on development and enhancement of the VFDM for modeling a
generic manufacturing system and then evaluating its performance. Section 2 gives an
overview of the current state of the art on data models for manufacturing systems and
presents the VFDM solution. Section 3 describes a test case representing a production
line. Section 4 delves into the problem of evaluating the performance of a
manufacturing system formalized according to the VFDM; in particular Discrete Event
Simulation by means of the commercial software tool Arena is addressed. Finally,
conclusions are drawn in Section 5.
2. Modeling Manufacturing Systems
2.1 State of the Art
Several scientific contributions and proposed technical standards have faced the
problem of developing a holistic and complete data model for representing
manufacturing systems, both considering tangible (e.g. machine tools, part types to be
produced, etc.) and intangible (e.g. process plans, production logics, etc.) aspects.
Among the available technical standard, ANSI/ISA-95 [12] is an international
standard for developing an automated interface between enterprise and control systems.
This standard has been developed for applications in all industries and in all sorts of
processes, like batch processes, continuous and repetitive processes. ISA-95 aims at
providing both consistent terminology and information models as well consistent
operations models. B2MML (Business To Manufacturing Markup Language) [13] is an
XML implementation of the ANSI/ISA-95 and consists of a set of XML schemas [14]
that implement the data models in the ISA-95 standard.
According to ANSI/ISA-95 standard, a manufacturing process can be modeled
using the ProcessSegment class. The ProcessSegment class can represent a single step
in a manufacturing process or a whole process through composition. The
ProcessSegment class is linked to further classes to characterize the process, e.g. the
needed equipment (EquipmentSegmentSpecification class), the personnel
(PersonnelSegmentSpecification class) and the material (MaterialSegmentSpecification
class). Furthermore, precedence relations between different process steps can be
defined thanks to the ProcessSegmentDependency class. The
EquipmentSegmentSpecification class allows the user to specify the pieces of
equipment needed for the execution of a process step and how the equipment.
ANSI/ISA-95 standard enables the user to freely define customized properties that can
be attached to most of the classes representing processes and production resources.
However, such flexibility can be a major drawback from the interoperability point of
view. Indeed, if two users adopt ANSI/ISA-95, they still have to agree on the definition
of the object properties before being able to exchange data characterized by a proper
semantic. Furthermore, ANSI/ISA-95 does not provide a complete support for
modeling physical data such as the placement and shape representation of objects in the
manufacturing system (e.g. a machine tool).
A different approach in the modeling of manufacturing process is offered by the
Process Specification Language (PSL) standard [15]. PSL is an ontology providing a
way to formally describe a process and its characteristics. The ontology has been
developed at the National Institute of Standards and Technology (NIST) and has been
approved as an international standard in the document (ISO 18629). The PSL ontology
grounds on a set of axioms of first order logic written in CLIF (Common Logic
Interchange Format) and organized in a core set together with extensions. The core
provides the definition of an activity and its occurrence related to a time variable. The
extensions enable the modeling of the execution through states, the definition of logical
expression constraining the execution of the activities, and the capability of modeling
resource and their usage by the execution of the activities. Grounding on an ontology,
the PSL standard provides a robust and reliable framework to formalize the knowledge
related to a process and guarantee an adequate level of interoperability. However, this
standard is still scarcely adopted in the industrial domain, probably because of the
perceived complexity at the enterprise level.
The Industry Foundation classes (IFC) standard by buildingSMART [16], partially
based on STEP standard [17], represents an open specification for Building Information
Modeling (BIM) data that is exchanged and shared among the various participants in a
building construction or facility management project. The IFC standard is available as
an EXPRESS schema specification [18] and is structured as a set of schemas that are
grouped into four layers: Resource layer (i.e. general purpose or low level
concepts/objects), Core Layer (where the most abstract concepts of the model are
defined), Interoperability Layer defining concepts or objects common to two or more
domains, and the Domains/Application Layer. The standard was mainly conceived for
Architectural Engineering Construction (AEC) industry domains (e.g. Building
Controls, Structural elements, Structural Analysis, etc.) and therefore provides most of
the definitions needed to represent tangible elements of a manufacturing systems.
Furthermore, generic definitions of intangible characteristics (e.g. processes, work
plans, etc.) are provided, so that its data structures can be specialized for other
industrial domains, such as the manufacturing domain.
2.2 Virtual Factory Data Model
The Virtual Factory Data Model (VFDM) of the VFF project is based on already
existing technical standards and extends their definitions to represent the characteristics
of a manufacturing system in terms of the products to be manufactured, the
manufacturing process they must undergo and the resources entitled to operate the
different manufacturing operations [8]. The VFDM is mainly based on the IFC
standard release IFC2x4 RC2 [19] that was translated into a set of ontologies by
adopting the Semantic Web approach [20]. Indeed, the XSD/XML technology [14] was
considered at first, but it is not suitable for knowledge representation, explicit
characterization of data with their relations on a semantic level, and management of
distributed data, thus endangering referential consistency. On the other hand, the
Semantic Web approach offers the possibility to represent formal semantics, merge
ontologies dealing with different domains, efficiently model and manage distributed
data, and ease the interoperability between different applications.
The Entities in the IFC standard are mapped to OWL Classes in the VFDM. Most
of the classes derived from IFC are specializations of two fundamental classes named
IfcTypeObject and IfcObject, both being subclasses of IfcObjectDefinition. The former
class is the generalization of any thing or process seen as a type, the latter seen as an
occurrence. OWL individuals of class IfcObject may be linked with a corresponding
individual of class IfcTypeObject.
IfcTypeObject has the following subclasses: IfcTypeProduct, IfcTypeProcess,
IfcTypeResource. IfcTypeProduct represents a generic object type that can be related to
a geometric or spatial context (e.g. manufactured products, machine tools, transport
systems, etc.). IfcTypeProcess defines a generic process type that can be used to
transform an input into output (e.g. assembly operation, machining operation, etc.).
IfcTypeResource represents the information related to resource types needed to execute
a process. A resource represents the “use of thing”.
IfcObject has the three main subclasses (i.e. IfcProduct, IfcProcess, IfcResource)
that represent an occurrence of the corresponding type modeled by the subclasses of
IfcTypeObject.
The previously described generic classes can be exploited to model a wide range of
manufacturing systems while taking into consideration both physical and logical
aspects. The subclasses of IfcTypeObject can be used to specify the designed
characteristics of a manufacturing system, e.g. the part types to be produced (as
individuals of IfcTypeProduct), the process plans (as individuals of IfcTypeProcess),
the required type of production resources (as individuals of IfcTypeResource). On the
other hand, the subclasses of IfcObject can be used to represent the execution phase of
a manufacturing system by defining the workpieces in process (as individuals of
IfcProduct), the actually executed operations (as individuals of IfcProcess), and the
usage of production resources (as individuals of IfcResource).
The relations between the processes and resources can be formalized as shown in
Figure 1 where the boxes represent classes and the arcs represent property restrictions
linking classes according to the Manchester OWL Syntax [21]. Moreover, Figure 1
shows how system design data (upper part of the figure) can be linked with system
execution data (lower part of the figure).
operatesOn only hasRelatedObjects only isResourceOf only hasRelatedObjects only
IfcRelAssignsTo IfcRelAssignsTo
IfcTypeProcess IfcTypeResource IfcObjectDefinition
Process Resource
hasRelatingProcess only hasAssignments only hasRelatingResource only hasAssignments only
hasRelatingType only hasRelatingType only
IfcRelDefines IfcRelDefines
ByType ByType
isTypedBy only isTypedBy only
operatesOn only hasRelatedObjects only isResourceOf only only hasRelatedObjects only
IfcRelAssignsTo IfcRelAssignsTo
IfcProcess IfcResource IfcObjectDefinition
Process Resource
hasRelatingProcess only hasAssignments only hasRelatingResource only hasAssignments only
Figure 1. Relations between process and resource classes in the VFDM.
During the manufacturing system design/planning phase, the resource types needed by
a process type can be specified by means of the objectified relationship class
IfcRelAssignsToProcess, whereas the resource providers (as individuals of class
IfcObjectDefinition) can be linked to a resource type thanks to the class
IfcRelAssignsToResource.
During the manufacturing system execution phase (both real and simulated),
occurrences of processes and resources can be created while referring to specific types
defined during the design phase thanks to the class IfcRelDefinesByType.
As described by Terkaj et al. [8], the VFDM specializes some classes of the IFC
standard for the manufacturing domain, paying attention in particular to the type classes
IfcTypeProduct, IfcTypeProcess, IfcTypeResource and the corresponding occurrence
classes IfcProduct, IfcProcess, IfcResource.
VffProcessType and VffProcess are defined as subclass of IfcTypeProcess and
IfcProcess, respectively, to model generic transformation processes that, provided a
given input, obtains a certain output according to certain rules and using a specified set
of resources, i.e. a recipe. A process can be described as a whole or can be decomposed
into subprocesses thanks to the class IfcRelNests. VffProcessType and VffProcess are
further specialized to represent manufacturing, assembly, maintenance and handling
processes. Moreover, precedence constraints between the processes can be defined by
means of the objectified relationship class IfcRelSequence, whereas input and output
entities of a process can be linked by using the classes IfcRelAssignsToProcess and
IfcRelAssignsToProduct, respectively.
VffProductionResourceType and VffProductionResource are subclasses of
IfcTypeResource and IfcResource, respectively, modeling a generic resource used in a
factory (and its production systems). These classes are further specialized to represent
equipment resources, material resources, and human resources, respectively.
In the VFDM the classes VffMachineryElementType and VffMachineryElement
have been defined as subclasses of IfcTypeProduct and IfcProduct, respectively, to
represent generic pieces of machinery equipment.
Finally, specific property classes (e.g. VffProcessProperties,
VffMachineryElementProperties) have been created to properly characterize processes,
resources and machinery elements.
3. Test Case on Production Line
This section presents a test case representing a production line to show how the VFDM
can be employed to create factory projects and use them with different digital tools.
The test case consists of four ontologies that instantiate the VFDM classes, thus
exploiting the data distribution empowered by the Semantic Web approach: three
factory libraries (i.e. VffLibrary01, VffLibrary02, VffLibrary17) and one main factory
project (i.e. VfProductionLine04). All these ontologies import the set of VFDM
ontologies.
VffLibrary01 ontology defines a production site and a building.
VffLibrary02 ontology defines five machine types (as individuals of class
VffMachineryElementType (i.e. MtA, MtB, MtC, MtD, MtE). Each machine type is
associated with two possible shape representations in VRML and 3DS format.
VffLibrary17 ontology defines a part type as individual of class VffWorkpieceType
(i.e. a subclass of IfcTypeProduct) and a possible process plan to obtain a final product
from a raw piece. The process plan named processPlan01 is defined as an individual of
class VffManufacturingProcessType (i.e. a subclass of VffProcessType) and
decomposed into five process segments (as individuals of class
VffManufacturingProcessType) characterized by a processing time and a predefined
sequence. Moreover, each process segment requires a specific type of production
resource and the processing time is modeled as an exponential distribution (see
Table 1).
Table 1. Process planning.
Individual of Description Required resource type as Stochastic
VffManufacturing individual of Processing time
ProcessType VffProductionResourceType distribution
processPlan01 Process plan N/A N/A
DR01 Drilling operation drillingRes01 Exponential(0.033)
ML01 Milling operation millingRes01 Exponential(0.02)
ML02 Milling operation millingRes02 Exponential(0.02)
QC01 Quality control qualityControlRes01 Exponential(0.033)
GR01 Grinding operation grindingRes01 Exponential(0.025)
VfProductionLine04 ontology contains the factory project that imports and enriches the
data provided by the three libraries. The factory project defines the units of
measurement, the representation context and world coordinate system where the
production site and the building imported from VffLibrary01 are placed. One
production line is designed and placed in the building of the factory. The production
line consists of seven machines (as individuals of VffMachineryElement) that are typed
by the machine types defined in VffLibrary02 (see Table 2) and characterized by a
shape representation and a placement. The production line is designed to process the
part type defined in VffLibrary17 and is thus organized into five production stages.
Each needed production resource type can be provided by one or more machinery
element as shown in Table 2. An example of relations between the individuals defined
in the test case is shown in Figure 2 where the boxes represent individuals (identified
by their local URI and class) and the arcs represent object properties linking the
individuals. In particular, it is shown that the process segment ML02 requires the
resource type MillingRes02 that can be provided by the machine type MtC (i.e. MS02
or MS03) or by the specific machine MS04.
Table 2. Machinery elements.
individual of Related individual of Description Provided resource type as
VffMachineryElement VffMachineryElementType individual of
VffProductionResourceType
DS01 MtA Drilling machine drillingRes01
MS01 MtB Milling machine millingRes01
MS02 MtC Milling machine millingRes02
MS03 MtC Milling machine millingRes02
MS04 MtB Milling machine millingRes02
CS01 MtD Quality control qualityControlRes01
machine
GS01 MtE Grinding machine grindingRes01
hasRelatedObjects
isResourceOf id2 M
MS04
operatesOn hasRelatedOb
bjects (IfcRelAssig
gnsTo (VffMachinery
Resourcce) Eleement)
id1
ML
L02 MillingRes02 hasRelatedObjects
(IfccRelAssignsTo
(IfcTypeProcess) (IfcTypeResource)
Process)
id3 M
MtC
(IfcRelAssig
gnsTo (VffMachinery
isResourceeOf
Resourcce) ElemeentType)
hasReelatingType
id4
(IfcRelDefines
ByType)
isTypedBy isTypedBy
MS02
2 M
MS03
(VffMachinery (VffMachinery
Elemennt) Eleement)
Figure 2. Relaations between proocess type, resou
urce type and mac
chinery element.
Figure 3. 3D vissualization of thee manufacturing system
s represente
ed in the test case..
The preseented test case has been serialized in n RDF/XML files [22] annd can be
uploaded//downloaded to/from a shaared data rep pository that is i made availilable by a
VFM instaallation, so th
hat the containned factory prooject and libraaries can be ac
accessed by
any VF module
m that iss integrated inn the VFF fraamework thus. For instancee, Figure 3
shows a visual
v represeentation of thhe factory prooject made by a VF modu dule named
GIOVE Virtual
V Factory y [11].
4. Discreete Event Sim
mulation
The appliicability of th
he VFDM to model a man nufacturing sy ystem and itss behavior,
aiming at evaluating itss performancee, has been vaalidated focussing the attenttion on the
case of Discrete
D Evennt Simulationn (DES) and taking as a reference thee test case
presented in the previou
us section.
The capability off generating simulation models m in an
n automatic (or semi-
automatic) way has beeen often consiidered one of the great challlenges in the simulation
of manufaacturing system ms [23] [24]. In such kind of approaches, a simulatioon model is
generated from a dataa source usinng algorithms for creating the model aand proper
interfaces to interact wiith a specific simulation en
nvironment. The automatic generation
of a simullation model answers
a to thhe need of speeeding up the overall time rrequired to
build a sim
mulation mod del and, in adddition, should also reduce th
he time needeed to verify
a model by decreasing thet time requiired to debug thet code.
Withiin the area off the simulati on of manufaacturing systeems, similar isssues have
been also addressed in n the literaturre, e.g. by Loorenz and Schhulze [25], RRandell and
Bolmsj [26], and Mu ueller et al. [27]. Most of the presented approoached are
characterized by one orr more of the ffollowing draw wbacks:
thhe work is strictly focuused on a specific
s manufacturing seector (e.g.
ssemiconductorrs [27]);
laack of universsal validity;
liimited level of
o automatism that can be acctually reached.
The design of the VFDM was drivenn by the need of providing a way to unam mbiguously
describe a generic manuufacturing sysstem regardlesss the specificc application aand, hence,
to addresss some of the lacks of the aapproaches alrready proposed in the literatture.
4.1 Disccrete Event Sim
mulation usingg Arena
Among the t great num
mber of avaailable generaal-purpose co ommercial offf-the-shelf
(COTS) simulation pacckages, Arenaa by Rockwelll Automation [28] is one oof the most
used bothh in the academic and induustrial world forf applicationns in the mannufacturing
field [29] [30].
An Arena
A model is built by dragging mo odules into th
he model wiindow and
connectingg them to defi
fine the flow oof entities thro
ough the modeel. An examplle is shown
in Figure 4 where partss are generateed in the Geneerate Parts block on the lefft and then
flow to thet Machine Part block representing the executio on of a certa tain set of
operationss.
gure 4. A simple simulation modeel in Rockwell Arena.
Fig
As shownn in Figure 5, 5 a set of reesources can be invoked to operate thhe defined
operationss. In this casee, each part enntering the bllock asks for the resource M
Machine 1
Arena model. A different and more flexiible way of
that has beeen already deefined in the A
defining a process orr a sequencee takes advan ntage of the capability oof defining
sequencess. A sequencee consists of aan ordered lisst of stations that an entityy will visit.
For each station in thee sequence, vaalues may be assigned to attributes
a andd variables.
Moreoverr, using sequen nces it is posssible to assign
n different rou
uting to differe
rent type of
entities, i.e. part types. Each station in the sequence is referred to as a step (oor jobstep)
and can bee characterized by specific attributes (e.g g. the processing time).
Figure 5. The processs definition windo
ow in Rockwell Arena.
A
4.2 Arenna and VFF
A DES siimulator based d on Arena c an exploit thee interoperabiility enablers offered by
VFF onlyy if it becom mes a VF moddule (see Secct.1), thus beeing able to aaccess and
understandd the conten nts of the shaared data rep pository wherre factory prrojects and
libraries are
a formalizeed according to the VFD DM. The info ormation storred into a
VFDM-coompliant projeect can be ussed to automaatically generaate an Arena simulation
model onlly if the Arenaa data structurres are properlly mapped to the VFDM cla lasses. This
mapping hash been impllemented by a software com mponent nameed Arena-VF Connector
that is attaached to Arenna and workss as a client exploiting
e the services offeered by the
VFM. Thhe Arena-VF Connector hhas been dev veloped in C++
C languagee and can
import/expport ontologies serialized in RDF/XML L format. Th he Arena-VF Connector
makes usee of the VF Connector
C C++ + Library thaat is based on the Redland C libraries
[31] and provides
p functtionalities to pparse, create an
nd modify thee ontologies thhanks to an
internal map
m betweenn OWL classses/restriction ns and C++ classes/methhods. The
instances of C++ classees are used as handlers of th he ontology inndividuals to su
support and
ease the binding
b between the factoryy project indiv viduals and th
he internal datta structure
of Arena. The Arena-VF Connectoor makes use of the COM M interface prrovided by
Arena to automatically
a generate Arenna models.
The development
d of
o a Arena-VF F Connector reequires a deep p analysis becaause Arena
representss a manufactu uring system according to o proprietary data structurees and the
relationshhips between thet processess and resourcees are formalized in a diff fferent way
comparedd to the VFDM M. In particuular, Arena reequires each step of a proocess to be
explicitly assigned to a station, thus ppreventing (grrounding on the traditional modeling)
the opporrtunity of deffining a grouup of entities that can be used as resoources and
postponinng the actual assignment oof the parts to o different staations at run time. The
definition of a sequencce in Arena al allows the modeling of the process stepss, but each
process stteps must be directly
d linkedd to a station or
o a group of station
s of the same type.
To cope with
w this limiitation, the asssignment of parts p to statio
ons at run tim
me must be
explicitly managed in Arena.
A
Takinng as a refereence the exam mple in Figuree 2, the manu ufacturing systtem model
can be traanslated into ana Arena moddel as shown n in Figure 6. The resourcee type (e.g.
millingRess02) required by a specificc process step (e.g. the millling operationn ML02) in
the sequennce is mapped d to a Stationn block and thhe assignment to the availabble objects
providing the needed resource
r (e.g.. the machinees MS02, MS S03, MS04) iss explicitly
managed through a treee of Branchh blocks with h as many leaves as the nnumber of
machine occurrences
o th
hat can executte the process step. On the leaves
l of the bbranch tree
the parts to be manufaactured are roouted to the sp pecific machiine using a R Route block
after recording the desstination in ann attribute of the Assign bllock. Instead of Branch
blocks, diifferent methoods can be useed to model specific
s assign
nments policiies as well.
Finally, thhe machine occurrences
o ((i.e. MS02, MS03,
M MS04) are mapped to Station
blocks (seee the bottom of Figure 6) thhat are followwed by a traditional sequencce of Arena
blocks, i.ee. a Seize blo ock allotting the machine, a Delay blo ock initializedd with the
processingg time and a Release
R blockk freeing the machine.
m Then n the manufacctured part
can be rouuted to the folllowing processs step in the sequence (SE EQ keyword inn the Route
block).
Arenaa also offers the
t opportunitty of modelin ng identical machines
m (e.g. MS02 and
MS03) as a single resou urce with carddinality greaterr than one. Ho owever, by addopting this
option thee dispatching of parts to the identical machines is internally m managed by
Arena acccording to pred defined policiies that cannott be directly controlled.
Figure 6. Autom
matically generatted Arena model
Further blocks can be added to ann Arena simu ulation modell for collectinng specific
statistics regarding
r the involved resoources. These statistics can be b formalizedd according
to the VF FDM by using g the definitioons imported from the IFC C standard. Foor instance,
the IfcRessourceTime claass offers a seet of attributess to store the time-related
t innformation
associatedd to a resourcee, e.g. the start
rt and finish time for the asssigned worklooad and the
percentage usage durin ng the consideered time horrizon. The tim me-related attrributes can
specify sccheduled or actual
a values,, thus showin ng how the VFDM
V can bbe used to
support faactory plannin
ng, performancce evaluation and factory monitoring
m actiivities.
The performance
p o the consideered manufaccturing system
of m has been evvaluated in
terms of utilization
u of the different resources and d flow time ofo the parts. TThe results
have beenn also validateed against a ssimulation mo odel built maanually and reepresenting
the same manufacturing
m g system (Figuure 7).
Figure 7. Maanually generated
d Arena model
Tablee 3: Simulation results.
r
Confidence interval (999%) on the
Performancce indicator Automatically Manually
mean of the differencee between
(average) generated modeel generated model
wo results
the tw
Utilization DS01
D 60.15 % 60.13 % [-0.41,0.45]
Utilization MS01
M 60.20 % 59.94 % [-0.16, 0.69]
Utilization MS02
M 33.49 % 33.05 % [-0.94, 0.87]
Utilization MS03
M 33.30 % 33.53 % [-0.31, 0.81]
Utilization MS04
M 33.44 % 33.52 % [-1.03, 0.86]
Utilization CS01
C 59.99 % 59.82 % [-0.79, 1.13]
Utilization GS01
G 80.26 % 79.63 % [-0.12, 1.38]
Flow time 0.107 [hours] 0.104 [hourrs] [0.00, 0.01]
Table 3 reports the results for 10 simulation runs of length 10 days with a warm up of
one day, for both the automatically and the manually generated simulation models. The
last column in Table 3 reports the 99% confidence intervals for the mean of the
difference between the results of the two simulation models. All the confidence
intervals contain the value 0, hence, the difference can be considered equal to 0 and,
consequently, the two simulation models provides the same results demonstrating the
validation of the automatically generated simulation model.
5. Conclusions
This paper has presented a data model for representing virtual factories, in particular
aiming at modeling the complex relationships between physical and logical entities of a
manufacturing system. It was shown how the adoption of a shared data model can
enhance the interoperability between software tools supporting the design, management
and performance evaluation of the factories.
Further developments of the data model are needed to better represent the
production logics characterizing a manufacturing system so that the generation of a
simulation model can be automated as much as possible. Moreover, the accuracy of the
generated simulation models will be improved if the common data model is used to
formalize the data coming from the shop-floor, thus closing the loop between the real
factory and its virtual representation.
In this paper the VFDM has been used mainly to support interoperability, however
further research can be carried out to exploit the enablers of the Semantic Web
approach to perform reasoning and enrich the knowledge about specific manufacturing
contexts.
Finally, the applicability of the VFF approach needs to be further tested by
integrating more software tools for performance evaluation into the framework. Such
integration will be supported by the development of programming libraries helping the
implementation of customized versions of VF Connector.
Acknowledgements
The research reported in this paper has been funded by the European Union Seventh
Framework Programme (FP7/2007-2013) under the grant agreement No: NMP2 2010-
228595, Virtual Factory Framework (VFF) and the grant agreement No: 262044,
VISION Advanced Infrastructure for Research (VISIONAIR). The authors would like
to thank COMPA S.A. (Sibiu, Romania) for kindly providing information for
representing the test case.
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