An innovative framework for the simulation of manufacturing systems: an application to the footwear industry

spongereasonInternet και Εφαρμογές Web

12 Νοε 2013 (πριν από 3 χρόνια και 4 μήνες)

66 εμφανίσεις


An innovative

framework for the simulatio
n of manufacturing
systems: an application
to the
footwear

i
ndustry

Alexandra F. Marques
1
*
,
Miguel Mujica
3
,
Jorge Pinho de Sousa
1
,2
, Paulo Sá Marques
1
, Rui
Rebelo
1

and
António C. Alves
1




*

Corresponding author: Tel.: (+351)
222 094 399
; Fax: (+351)
222 094 350
; E
-
mail:
alexandra.s.marques@inescporto.pt

1
Inesc Tec

Porto, Portugal

2
Faculty of Engineering

University of Porto

Porto, Portugal

3

Autonomous University of
Barcelona, Spain

A
BSTRACT

Simulation in industrial environments has been recognized as a valuable approach for capturing the different
characteristics and
complexity of

the dynamics
in

industrial
processes
.
However, there is a clear need for
spreading the use of simulation tools in manufacturing companies and for simplifying the simulation modelling
process. In fact this process is still
highly demanding in terms of the s
pecific skills of the modellers and
in terms of
the time n
eeded to develop
models that
are

effectively
useful

in

actual manufacturing systems. The slow modelling
process often precludes the use of simulation for facing the operational problems that rise in

the day
-
to
-
day

operations. This paper presents a brief overview of the use of simulation tools in manufacturing, and focus on the
development of
an innovative

simulation

framework
based on

libraries of components and modules
. This
framework will
contribut
e for reducing the learning curve
in

developing simulation models

for

manufacturing and
logistics systems. The requirements and advantages of this novel modular modelling approach are presented and
discussed in the context of a case study
that uses the SIM
IO software for simulating the production and logistics
systems of a

generic footwear

manufacturing
system

in Portugal.

1.

I
NTRODUCTION

Simulation
is

becom
ing more and more important for engineering complex production and logistics systems.
Although simula
tion models started to be developed in the 60’s for virtually representing and emulating the dynamics
of real
-
size industrial environments [25
], efforts are still needed for spreading its utilization by manufacturing
companies.

Over the years, simulation
models evolved to cope with the increasing complexity of manufacturing systems, that
may include many components (operations, facilities, equipment, workers), complex relations among the components,
and may further address multi
-
products or several product
ion lines. Most of the simulation models tackle uncertainty
and risk in product demand and in the dura
tion of the operations (e.g. [28
]). Recent developments in simulation tools
take advantage of optimization routines to assure the best use of production r
esources,
or to reduce costs or delays [25
].

Simulation models have been used for many purposes including facilities planning, supporting product design,
diagnosing the performance of a production process and identifying
bottlenecks
, performing lab tests
on potential
improvement ideas (e.g. expected results of the application of
lean

techniques), conducting stress tests to the system for
scaling the production capacity and logistics, ergonomics and training (e
.g. [6], [10], [14
]). Consequently, simulation
tools may help to improve the design and analysis of manufacturing systems and may also support
scheduling
,

production planning

and
decision
-
making [15
]. The contribution

of these tools towards the increase of efficiency and
competitiveness of manufacturin
g companies is well established in some key policies and strategic documents such as
the
Future of Manufacturing

in Europe 2015
-
2020

of the EU [7
] and the
Visionary Manufacturing Challenges for 2020
of the Board on Manufacturing and E
ngineering Design in t
he USA [16
].


In practice, different simulation approaches can be taken,
including
system dynamics (e.g. [24], [27
])
,

agent
-
based
modelling
[1
7]

and

discrete event systems

(DES)
[2
]
, although the latter seams to be the mostly used for
industrial
environm
ents
.

In
general terms,
DES

models
represent the dynamics of the production and logistics systems over time
An innovative framework for the simulation of manufacturing systems: an application to the footwear in
dustry



as a sequence of events. A set of variables is used to describe the “state” of the system at a given instant.
The state of the
system changes
only w
hen an “event” occurs in a discrete instant of
time. The event may possibly have a continuous
evolution once
it

start
s
, but this is not what one is interested in: the primary focus is on the beginning and the end of
the

event, since
the
end

of an event

can

cause
the

beginning

of another

[2], [22], [23
].

DES models may be built with programming languages for simulation (such as SIMULA) or with the help of
specific software tools called Visual Interactive Modelling Systems (VIMS). There are an increasing num
ber of
c
ommercial

off
-
the
-
shelf VIMS solutions available for specific as well

as for generic applications [25
].
VIMS are more
easily used than programming languages as they provide many features for simulation modelling and
restrict
programming to the even
t cod
ing

that configure
s

the behaviour of objects in response to given events
.

Regardless of
the approach in use, the process
es

of building
and maintaining
simulation models
are
often unique
and tailored to a specific industry

or company

and to a
particula
r
simulation purpose. The modelling process is still
highly demanding in terms of the specific skills of the modellers and
in terms of
the time needed to develop simulation
models that
are

effectively
useful

in

actual manufacturing systems.

A
DES model

res
ulting from such a process
represents in detail the complex

structure and interactions of a given
manufacturing system

and can

hardly
be
reused
even for
handling

other

systems

of the same
industrial sector
.
Furthermore, the models need to be continuously u
pdated
to keep up with the changes of the manufacturing systems (e.g. new production processes, new products, changes in the
industry layout).

The limitations in the modelling process, among others descr
ibed i
n [14
]
, have

prevented a

wide use of simulatio
n
tools

in

manufacturing
companies

and particularly preclude
their

use for
handling

the operational problems that rise in
the day
-
to
-
day operations.


Recent developments in
mode
ling and programming paradigms
,

with
an
emphasis on reusability and object
orie
nted programming
, aim

at

significantly
reduc
ing

the efforts for developing simulation
models (e.g. [
8]). The

new
generatio
n of VIMS
built according to these paradigms
,

already
provide

the modeller
with
reusable standard objects
and
many

features that aim
a
t

facilitat
ing

the construction of the simulation model
, including the possibility of using
existing models as “black
-
box” modules in complex simulation models or even reusing them as templates for new
simulation models. Further a
dvances in
VIMS address th
e integration with the company information systems, as a way
to provide information required for automatically maintaining the simulation models.


However
no such generic library of components

or modules was found specifically for manufacturing systems. Th
e
literature is scarce both on conceptual frameworks and on applications of VIMS that accomplish reusability of objects
and enhance the simulation modelling process. The framework for Extended Digital Manufacturing Systems [
1
8]
provides an integrated envir
onment for products, production systems and business processes that may be used for the
purpose of simulation. However, the detailed simulation objects and the simulation modelling process are not described
in that framework. Recent work on ontolog
y models

for equipment (e.g. [13], [26
]) as well as existing standards
initiatives s
uch as the ISO 15531
-
MANDATE [5
] for m
anufacturing data and ISA 95 [21
] for
the integration of
enterprise and control systems
, may further contribute for establishing common concep
ts and definitions in this
domain. The
Core Manufacturing Simulation Data (CMSD) Standard

developed in 2010 (
[3
],
[
11
]
)

and already applied
in the car manufacturing industry
(
[12
], [
1
9]
)

may be quite useful for integrating simulation systems with other
man
ufacturing applications.

This paper aims at contribut
ing to
the development of
simpler and faster simulation modeling processes
t
h
ro
u
gh an
innovative framework for simulating manufacturing systems
,

enhancing

modularity and reusability
. Such framework
will
hopefully contribute

to a significant

reduction of the learning curve for developing new simulation models.

The
paper describes the framework concepts and its application in the creation of new simulation models with SIMIO. The
paper further presents some
preliminary results on the application of this approach for simulating a production and
logistics system in
footwear

manufacturing.

2.

A

F
RAMEWORK

FOR

THE

SIMULATION

OF

MANUFACTURING

SYSTEMS

The framework encompasses the definition of concepts, relevant i
nformation and libraries of simulation objects that
can be used within a VIMS, for quickly creating simulation models for any manufacturing system. So far, the
framework also includes a library of modules that may be used for simulating the production and
logistics processes of
footwear

manufacturing companies.

Such framework promotes the use of new standard and simplified simulation
modeling processes, especially for companies with a library of modules available. The framework may further
contribute for ad
opting new system architectures that enhance automatic data collection from the company information
An innovative framework for the simulation of manufacturing systems: an application to the footwear in
dustry



systems, guaranteeing a continuous update of the simulation model, thus making a better use of the
enhanced features of
commercial VIMS.

The framework was
developed under the scope of the

PRODUTECH
-
PTI Project representing join efforts of the
national i
ndustry of
m
anufacturing
t
echnolo
gies towards the increase of its productivity and competitiveness.

The
work
-
plan for the development the framework encompasse
d a specification phase and an implementation phase for
producing prototypes for several types of industrial sectors. The specification

of the framework was built upon a
comprehensive literature review on the simulation of manufacturing processes and on a
survey of manufacturing
equipment used and produced by the industrial sectors that take part in the PRODUTECH initiative. A group of experts
in simulation from Portuguese research centers and consultancy companies met regularly during the specification pha
se
for drawing an initial concept map for simulating manufacturing systems independently from the VIMS platform.
Although SIMIO (
www.simio.org
) was selected for the prototype implementations.

Several other researchers a
nd users of VIMS from the manufacturing companies in PRODUTECH also met in full
day workshops for discussing concepts and procedures, setting the type of problems to be addressed by the library, and
identifying and characterizing the main attributes of its

components. During these workshops, the users have assured
that all the manufacturing equipment identified in the initial survey could be mapped into objects of the general library.
The group of experts agreed upon some premises about the processes for qu
ickly developing and updating simulation
models, which were then refined and improved during the implementation phase.

The group of experts

further recognized that relevant modules for the manufacturing systems should be
case
-
specific. Therefore, despite
some general definitions and a common working procedure, the group split into
smaller working groups for separately approaching several industrial sectors. The group that focused on the
footwear

manufacturing companies in Portugal conducted several interna
l workshops during the modules specification. Regular
development meetings during the implementation phase assured the compliance with common developing procedures.

The
domain of the framework is the
concepts and information for the simu
lation of manufact
uring systems
,
encompassing three main groups of concepts: structure of a simulation model, library of components for the simulation
of manufacturing systems, and required information (see figure 1).


Figure 1: Concept Map
of the framework for simulating
manufacturing systems






The group of concepts related with the
simulation model

may be common to all industrial sectors. In general terms,
any DES model within a VIMS represents a system composed by
entities

that are progressively handled b
y
components

representing equipment and/or workers linked by physical or logical connectors. The state of a model changes only
when a
discrete set of events occur [22, 23
]. The
event code,

the decisions and business logic that guide the entity flows
across

the system are built using a simulation language or alternatively using graphical flowcharts that sequence a set of
An innovative framework for the simulation of manufacturing systems: an application to the footwear in
dustry



elementary routines. In SIMIO these routines are called Processes and Steps

[9
]
. Such programming structures use as
input the information f
or simulation, which is imported to specific tables of the VIMS. Each execution of the simulation
model is a
simulation run
. During the simulation, the VIMS compute a set of relevant indicators that depend on the
simulation goal. The evolution of the indic
ators during the simulation is displayed in dashboards or results tables.

Simulation models focus on the whole or parts of the production and logistics processes. Consequently, the generic
library for simulating manufacturing systems is built around the c
oncept of component or resource. The
sub
-
components that may constitute the equipment are not considered in this work.

The
component

or
resource

represents

one equipment, machinery and/or worker that performs nuclear operations
of the manufacturing system
. The component can also be composed by a set of complementary equipment and workers
that can only be used as a group. An example is a functional group of shoe sewing machines that execute the same task
but any single shoe is only handled by one of the mac
hines. The total capacity of this pool or resources is the sum of the
capacity of each machine.

For the purpose of simulation, each component has a generic behavior that is modeled with an elementary process
(EP) (as discussed in section 2.2). EPs are hie
rarchically classified in classes and can be included in more complex
models, c
alled modules. Like in [13], [5
] a
module

represents a logic association of components and/or other
elementary modules that for the purpose of simulating complex systems may be
considered an unique object with inputs
and outputs. Any module is defined by a sequence and number of components and/or modules and the logic that links
those objects within the module. An example is a shoe sewing line with many distinct sewing machines.

Each type of
shoe is processed by several of these machines in an unique sequence.

Both components and modules
first
ly

developed
may be

encapsulated and added to a library for re
-
utilization.
The
utilization of a module may require adaptations of the inhe
rent logic routines (i.e. processes) for each application.

A component is characterized by a set of properties, states, methods and rules. Properties are intrinsic
characteristics such as its maximum working velocity, power autonomy, and maximum capacity.

A state is a
characteristic of the object whose value changes during the simulation, such as its instance velocity or used capacity. A
method is an action or function executed by the component in response to events. Generic methods that may be
considered
for a manufacturing equipment can be: “putting the product in a queue when it arrives to the equipment”,
“stop processing if a required worker is not available”, “stop processing during setup and changeover or at the
beginning or end of the day”. A rule is

a logical, physical or business
-
related condition that must be verified during the
simulation. Examples include axioms, such as “the maximum velocity cannot be exceeded” but also other rules such as
“the need to stop processing when a malfunction occurs”.

A module may be characterized by its own set of properties,
states and methods, whose values may be computed directly from the values of the attributes of its components.

Finally

the information required for
the simulation of
manufacturing systems

is dep
icted in the conceptual map of
figure 1, built upon the existing ISO 15531
-
Mandate and CMSD standards.

Accordingly,
in general the simulation
considers a production plan for a given period that can be a day or a week. The plan includes the list and amount
of
products to be produced, leading to a required material demand plan: purchase orders to the suppliers, and production
orders for the components to be produced internally by the company. Following the plan, the production scheduling
establishes the seque
nce in which the products will be produced and in which equipment, thus creating the production
orders that are the main input for the simulation. Each product is manufactured according to a receipt or product routing
that is also an input for the simulati
on, including the list and sequence of the core production operations, and the
processing times for each unit of product in primary or alternative resources.


The
generic library
of Components for the simulation of manufacturing systems
,

developed
in the c
ontext of
the framework
,
gro
up

elementary processes into 3
classes
: Manufacturing, Storing and Movement

(see f
igure 2)
.


The
Manufacturing

class includes the resources that perform operations that modify the nature of the product. The
resources of this cla
ss are mainly represented by the types and amounts of products that get in (b) and out (c) of an
elementary process (see figure 3).

There may be a unique product flow coming into the resource through an entering
node (a) or the different types of products
may have distinct flows with origin in other different resources, but just
having a single entering node. Similarly, there may be one or several exiting nodes (d). The resource representation
further includes entrance and exit buffers for queuing the produ
cts before and after transformation.

In general terms, th
e component
may behave as a bas
ic workstation
, whenever the units of a single product lot are
simultaneously received and transformed into a single product lot that leaves the resource as a whole, fo
r further
handling. In this case
b

=c

=1

and usually
a

=d

=1
. In this approach, the focus is the “changes” introduced in the
product flow, and therefore the consumables and the possible by
-
products are usually not considered.

An innovative framework for the simulation of manufacturing systems: an application to the footwear in
dustry




Figure
2
:
Generic library o
f components for
the simulation of

manufacturing systems


Figure
3
:
General representation of the elementary processes of the Manufacturing class


The component behaves

as a
separator

by splitting a single product lot into distinct types of product lots t
hat, from
that point on, will have separate handling. In this case
b

=1, c

>1

and usually
a

=1, d

>1
. The
combiner

has the inverse
behavior by merging distinct types of products, usually coming from distinct entry nodes, into a single product type
that, fr
om that point on, will constitute a unique flow (
b>1, c=1

and usually
a>1, d=1
). The resources that perform
packing (and unpacking) operations are considered a special case of combiner (separator) since they combine several
units of one or several types of

products into a container (that may be considered a different type of product) and, from
that moment on, the full container may be viewed as a new product.
The
component

behaves as a
batch station

whenever received products are clustered into buffers acco
rding to their size or other specific processing requirements
,

and are then processed as a lot when a buffer reaches a mini
mum dimension. Afterwards, the processing lot is split and
the different product types follow distinct product flows
(
b =1, c =1
and

a >1, d >1
).

Exceptional situations may occur in the classification of these elementary processes. Examples include the basic
workstation with more than one exit node
(a = 1, d > 1)

for splitting the resulting product among the principal and
alternative r
esources, according to some pre
-
defined rule. The separator may have a unique exit node
(a

=1, b

=1),

whenever the distinct types of products follow a unique flow but, by some reason, the types of products need to be kept
differentiated. These exceptional
situations may be discussed case
-
by
-
case taking into account the use of the resources
in a given industrial sector.

The
storing class

includes the
component
s that are mainly devoted to the temporary retention of products. The
equipment in this class accep
ts a limited amount of products and product types, store them for a period of time (that may
or may not be previously established) and then releases them whenever necessary, often taking into account the
products entering sequence, and their entrance and d
ue dates. This class applies to both raw materials, work
-
in
-
progress
and finished products, here considered as variants of the product entity. The typical elementary processes of the
storing
class

are the
basic warehouse

for discrete products, and the
stan
dard tank

for continuous products. Other elementary
processes include the
breaker

that, besides storing, may also break the incoming product lot into smaller lots to be
separately released. The
supermarket

is often used in
lean

engineering is a particular
case of the basic warehouse with
normalized stocks and products being restocked in the same amount they are released to the production process.

An innovative framework for the simulation of manufacturing systems: an application to the footwear in
dustry



The
components

included in the
movement class

are devoted
exclusively
to the
physical handling of products. An
equipment that simultaneously moves and transforms should be included in the previous class of
transformation
.
Within the
movement class
, the equipment behaves as a
mover
whenever the whole or parts of the equipment move with
the product. In the
transporte
r
products are moved along the equipment. Vehicles move products across trajectories
that may be complex but predicable


vehicle with fixed route

-

or with random components


vehicle with unfixed
route.

It should be noted that in the generic library des
igned in this work, resources are fully characterized in terms of
properties, states, methods and rules that are relevant for simulating their behavior for the set of goals previously
established.

The framework further includes

libraries of models for an
industrial sector
.

Such libraries
should approach

the
main stages of the
generic
production and logistics
systems.
The

modules are instantiated and parameterized by the
modeler during the process of creating a simulation model for the specific case under s
tudy.
Modules are characterized
according to the number and type of components, number and type of modules, rules for sharing workers and
consumables among the resources of the modules, performance indicators for the module, and rules for setting the
produ
ct flow within the model. The latter may include the rules for selecting the pieces of equipment to use in case of
alternative resources, as well as tables for establishing the production routing.

Up to now, modules are identified
through an in
-
depth anal
ysis of existing manufacturing systems and their
simulation models,
this process involving

both the modelers and the business experts.
A general module identification
process may emerge in a bottom
-
up approach driven from the experience of modules identifi
cation in each sector. Such
process may further benefit from the results of r
ecent work in prototyping virtual factories developed under the scope of
EU pro
jects such as VFF (see e.g., [26
]) and COPERNICO [20]
.

The framework will lead to a
new
simulation m
odeling process

that actually
reduce
s

the work of the modeler

through the use of reusable components and models. This process should in
volve the
“customer”

and possibly the
supplier
s

of production and logistics equipment
,

in the simplified
approach

of coll
ecting information for the simulation
.
It should also guarantee

that part of the work may be conducted independently from the
selected
VIMS.
All these
features will hopefully contribute for shortening and simplifying the simulation modeling process.

3.

C
A
SE
S
TUDY
:

P
RODUCTION
AND LOGISTICS SYSTEM
S OF THE
FOOTWEAR

INDUSTRY IN PORTUGAL

The footwear industry
has an important i
mpact in the Portuguese economy, representing 8% of national exports.
Employs

approximately 33 thousand
employ
ments for producing
around

62 million pairs
of shoes
, 95%
of which are
exported for more than 130 countries
[1
]
.

The traditional
footwear

manufacturing
system
encompasses
cutting,
stitching
,

assembly
,

finishing

and packing
. These
production stages

are
often
physically
separated and

create
considerable intermediate
s
t
ocks

leading to long
production lead
-
times.

Over the years, t
h
e

traditional industry
units
have been
specializ
ing

in producing high value
-
added leather footwear.
T
he
footwear
manufacturing systems
tend to
become more and

more flexible and integrated, enabling the production of
small quantities
of highly diverse

models

tailored to customer specifications
.


To assess the advantages and limitations of the proposed approach to the simulation of footwear manufacturing
systems,

the project focused on
three generic
footwear

manufacturing
systems provided by the CTCP [4
] for producing
200 pairs, 500 pairs and 1000 pairs per day
. According to the Framework, an exhaustive list of the equipment used in
each of manufacturing system wa
s firstly compiled and each equipment
mapped into

the
proposed standard
ele
mentary
processes
. Then, the manufacturing process was scrutinized for identifying modules that may simplify the simulation
modeling process. The One Step
transporter
prototype deve
loped by Inesc Porto was one of the first modules
identified. This module is particularly relevant as it contributes for the current specialization t
r
end of the sector by
integrating
traditional
ly

independent

stages of the process such as the
assembly

and
finishing.

4.

P
RELIMINAR
Y

RESULTS

Preliminary results suggest that the 62 production and logistics equipment listed for the cases were successfully
mapped into the elementary processes of the library of components

(
t
able 1)
. The proposed framework proved t
o be
adequate to model the
behavior of all the
equipment

for the purpose of simulating the systems behaviour
. Results
showed that the Sewing stage
has the highest number of types of equipment, most of them
behaving as basic
workstations
.

An innovative framework for the simulation of manufacturing systems: an application to the footwear in
dustry



The elementary pr
ocesses of the library were successfully built from objects of the standard library of the SIMIO.
As an example, the basic workstation was implemented using a Workstation. Other elementary processes such as the
basic warehouse are implemented as combinatio
ns of standard objects and require significant modeling efforts.

Table 1: Number of types of equipment used
in the
footwear

manufacturing systems in Portugal.

Stages of the
footwear
manufacturing system

Elementary processes

Basic workstation

Separator

C
ombiner

Basic warehouse

Transporter

Total

Cutting

1

3


3

1

8

Pre
-
stitch
ing

6

1




7

S
titch
ing

19


1

2

1

23

Assembling

11

1

5


1

18

Finishing and packing

2


1


3

6

Total

39

5

7

5

6

62


The ongoing work in identifying the modules for the
footwear

ind
ustry suggests that there will be at least one
module for each stage of the manufacturing process and for each of the target shoe production level.
The preliminary
results with the 1 step transporter show that it may be implemented using a set of transport
er elementary processes.
Significant modeling efforts are used to configure the logic of the product flow in the system (see figure 4). Further
conceptualization work is needed to create a general 1 step transporter module that can be easily parameterized
by the
user
to fit to a specific manufacturing process.


Figure 4: Simulation model for the 1 step

transporter prototype


At this stage, the i
nformation
used for simulation was randomly generated and imported from text files built for
resea
rch purposes. F
uture work will foresee the direct integration with a footwear manufacturing company. Preliminary
results further show that the simulation modeling process is significantly simplified although the modelers experience
in using the commercial solution and hi
s background in manufacturing systems are still of great relevance.


5.

C
ONCLUDING REMARKS

The
key elements of the framework for the simulation of manufacturing systems are described. Preliminary results
in the footwear industry are presented. Future work

will complete these results
by implementing in SIMIO all the
elementary processes. Specific resource components for the footwear industry will be developed in SIMIO. Additional
research will lead to new modules for the footwear industry. Other lines of wo
rk will foresee the application of the
proposed framework for other industry sectors, including the textile manufacturing and metal works.

An innovative framework for the simulation of manufacturing systems: an application to the footwear in
dustry



A
CKNOWLEDGEMENTS

This research was conducted under the scope of the
p
r
oject Produtech PTI (n.º 13851)
-

New Processe
s and
Innovative Technologies for
Production Technologies

(
www.productech.org
),

partly funded by the Incentive System
for Technology Research and Development in Companies (SI I&DT), under the Competitive Factors Th
ematic
Operational Programme, of the Portuguese National Strategic Reference Framework, and EU's European Regional
Development Fund.


R
EFERENCES

[1
]

APICCAPS. “Statistical study on Footwear, Components and Leather goods
-
2011”. 237 pp. 1993.

[2
]

Banks, J.;
Carson, J.; Nelson, B. and Nicol, D.: Discrete
-
event system simulation
-

fourth edition. Pearson.2005.

[3
]

Core Manufacturing Simulation Data Product Development Group:

SISO
-
STD
-
008
-
2010 Standard for: Core Manufacturing Simulation
Data

UML Model

. 2010.

http://www.sisostds.org/

[4
]

CTCP. “Manual Prático de Novas Tecnologias de Produção

e Organização do Sector do Calçado”.
237 pp. 1993.

[5
]

Cutting
-
Decelle, A.F. and Michel, J.J.: “ISO 15531 MANDATE: a standardised d
ata model for manufacturing management”. Int. J. of
Computer Applications in Technology, 2003 Vol.18, No.1/2/3/4, pp.43
-
61.2003.

[
6
]

Fowler, J.W. and Rose, O.: “Grand Challenges in Modeling and Simulation of Complex Manufacturing Systems”, SIMULATION, Vol.

80,
No. 9, pp. 469
-
476, 2004.

[7
]

Geyer, A., Scapolo, F., Boden, M., Dory, T. and Ducatel, K.: “The Future of Manufacturing in Europe 2015
-
2020: The Challenge for
Sustainability”.
IPTS
-
EU. 2003.
http://foresight.jrc.ec.europa.eu/documents/eur20705en.pdf

[
8]

Herrmann, J.W, Lin, E., Ram, B. and Sarin, S: “Adaptable Simulation Models for Manufacturing”, in Proc. of the 10th Int. Conf
. on
Flexible Automation and Intelligent Manufacturin
g, Vol. 2, College Park, USA, pp. 989
-
995. 2000.

[9
]

Kelton, W.; Jeffrey Smith, J.; David Sturrock, D.:

Simio and Simulation: Modeling, Analysis, Applications


second Edition

, Simio LLC,
400pp. 2011.

[
10
]

Kuehn, W.: “Digital factory: integration of simu
lation enhancing the product and production process towards operative control and
optimization”, International Journal of Simulation, Vol.7, No.7, pp. 27

29, 2006.

[
11
]

Leong, S. K.; Lee, Y. T.; Riddick, F. H.: “A Core Manufacturing Simulation Data Informa
tion Model for Manufacturing Applications”,
Proceedings of the Systems Interoperability Standards Organization 2006 Fall Simulation Interoperability Workshop, 2006.

[
12
]

Leong, S. K.; Johansson, M.; Johansson, B.; Lee T.; Riddick, F. H.: “A Real World Pilo
t implementation of the Core Manufacturing
Simulation Information Model”, Proceedings of the Simulation Interoperability Standards Organization (SISO) Spring 2008 SIW
Workshop, 11pp, 2008.

[13
]

Lohse, N., H. Hirani, S. Ratchev, and M. Turitto.: “An ontolog
y for the definition and validation of assembly processes for evolvable
assembly systems.” (ISATP 2005). The 6th IEEE International Symposium on Assembly and Task Planning: From Nano to Macro
Assembly and Manufacturing, pp 242
-
247. 2006.

[
14
]

McLean, C. an
d Leong, S., The Expanding Role of Simulation in Future Manufacturing, Proceedings of the 2001 Winter Simulation
Conference, pp. 1478
-
1486, 2001.

[15
]

Mujica, M.A., Piera M. A., "A compact timed state space approach for the analysis of manufacturing system
s: key algorithmic
improvements", International Journal of Computer Integrated Manufacturing, vol.24(2),pp. 135
-
153, Taylor & Francis, 2011.

[16
]

National Research Council (NRC) / Board on Manufacturing and Engineering Design (BMED,) Visionary Manufacturin
g Challenges for
2020, National Academy Press, Washington, D.C., 1998.

[1
7]

North M. J. and Macal C. M.: “Managing Business Complexity Discovering Strategic Solutions with Agent
-
Based Modelling and
Simulation”, Oxford University Press, 2007.

[
1
8
]

Nylund, H
. Salminen, K. and Andersson, P. Framework for Extended Digital Manufacturing Systems.
International Journal of Computer
Integrated Manufacturing Vol. 24 No.5, pp. 446
-
456, 2011.


[
1
9
]

Johansson, M.; Johansson, B.; Skoogh, A.; Leong, S.; Riddick, F.; L
ee, Y.T.; Shao, G.; Klingstam, P.:” A test implementation of the core
manufacturing simulation data specification”, Proceedings of the 2007 Winter Simulation Conference, pp. 1673
-
1681
.

[20]

Rose
-
Anderssen, Baldwin, JS, Ridgway, K., Boettinger F., Agyapon
g
-
Kodua, K., Brencsics,I. and Nemeth, I:” Application of production
system classification in rapid design and virtual prototyping”. Proceedings of the 14th International Conference on Modern In
formation
Technology in the Innovation Processes of the Industr
ial Enterprises, Budapest, Hungary, October 24

26, 2012.

[21
]

Scholten, B. “The roadmap to integration: A guide to applying the ISA
-
95 standard in manufacturing”. ISA. 2007.

[
22
]

Schriber, T.J. and Brunner D.T., Inside discrete
-
event simulation software: h
ow it works and why it matters, Proceedings of the 2010
Winter Simulation Conference, pp. 216
-
229
, 2010.

[
23
]

Schriber, T.J. and Brunner D.T., Inside discrete
-
event simulation software: how it works and why it matters, Proceedings of the 1997
Winter Simula
tion Conference, pp. 14
-
22
, 1997.

[24
]

Sterman
, J
: “
Business Dynamics: Systems Thinking and Modeling for a Complex World”
. Irwin/McGraw
-
Hill, 2000.

[25
]

Swain, J.: “Software survey: Simulation
-
Back to the future. ORMS
-
Today 38 (5). http://informs.org/ORMS
-
Today. 2011.

[26
]

Terkaj, W, Pedrielli, G. and Sacco, M.:“Virtual Factory Data Model”.
Proceedings of OSEMA 2012 Workshop, 7th International
Conference on Formal Ontology in Information Systems, G
raz, Austria, July 24
-
27, 2012.

http://www.vff
-
project.eu/

[27
]

Wakeland, W.W., Gallaher, E.J., Macovsky, L.M. and Aktipis, C.A.:“A Comparison of System Dynamics and Agent
-
Based Simulation
Applied to the Study of Cellular Receptor Dynamics”, in Proc. Of t
he 37th Annual Hawaii, Int. Conference on System Sciences, 2004.

[28
]

Wenzel, S., Boyaci, P., Jessen, U., Simulation in production and logistics: Trends, solutions and applications, Advanced Manu
facturing
and Sustainable Logistics, Lecture Notes in Busines
s Information Processing, Vol. 46, Part 1, pp. 73
-
8
4, 2010.