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ScienceDirect
Procedia Manufacturing 00 (2017) 000–000
www.elsevier.com/locate/procedia
* Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741
E-mail address: psafonso@dps.uminho.pt
2351-9789 © 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017.
Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June
2017, Vigo (Pontevedra), Spain
Costing models for capacity optimization in Industry 4.0: Trade-off
between used capacity and operational efficiency
A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb
a University of Minho, 4800-058 Guimarães, Portugal
bUnochapecó, 89809-000 Chapecó, SC, Brazil
Abstract
Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected,
information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization
goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value.
Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of
maximization. The study of capacity optimization and costing models is an important research topic that deserves
contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical
model for capacity management based on different costing models (ABC and TDABC). A generic model has been
developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s
value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity
optimization might hide operational inefficiency.
© 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference
2017.
Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency
1. Introduction
The cost of idle capacity is a fundamental information for companies and their management of extreme importance
in modern production systems. In general, it is defined as unused capacity or production potential and can be measured
in several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity
Procedia Manufacturing 28 (2019) 189–194
2351-9789 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable
and Virtual Production.
10.1016/j.promfg.2018.12.031
10.1016/j.promfg.2018.12.031 2351-9789
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientic committee of the International Conference on Changeable, Agile, Recongurable
and Virtual Production.
Available online at www.sciencedirect.com
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
International Conference on Changeable, Agile, Reconfigurable and Virtual Production
Knowledge-based Conversion of Finite State Machines in
Manufacturing Automation
Georg Ferdinand Schneidera,∗
, Georg Ambrosius Peßlera, Walter Terkajb
aFraunhofer IBP, F¨urther Straße 250, 90429 N¨urnberg, Germany
bITIA-CNR, Via Alfonso Corti 12, 20133 Milano, Italy
Abstract
More and more information and communication technologies originating from the web are introduced in industrial automation
systems. The vision for future automation systems includes intelligent self-descriptive components, which exchange information
and potentially reason by themselves through knowledge-assisted methods. Formal domain descriptions are required to enable this
vision, including knowledge related to mechanical, electrical and control domains. This work focuses on formalizing knowledge of
the automation and control domain and investigates how knowledge-based methods can support the automated conversion between
different formalisms for modelling discrete behaviour in manufacturing automation: finite state machines. We detail our approach
by deploying the presented method in a use case related to the automation of a pick and place unit available from the literature.
©2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable
and Virtual Production.
Keywords: Automation and Control; Finite State Machines; Ontology; Knowledge-based Method
¡
1. Introduction
The introduction of Information and Communication Technologies (ICT) originating from the internet in the au-
tomation domain is envisioned to pave the way for novel services and tools related to the engineering and operation
of Industrial Automation Systems (IAS) [12]. During the engineering and operation of IAS a wide range and large
quantity of data is generated. It is envisioned that based on the novel technologies a digital twin, e.g. [23], exists next
to the real world system, which gives access to all relevant information and knowledge extracted from this data. A
number of intelligent engineering applications [2] can take advantage of a continuously updated digital twin [16], for
example, to execute simulations [17], reconfigure production modules [14] or self-description [8].
∗Corresponding author. Tel.: +49-911-568549145.
E-mail address: georg.schneider@ibp.fraunhofer.de
2351-9789 ©2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual
Production.
Available online at www.sciencedirect.com
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
International Conference on Changeable, Agile, Reconfigurable and Virtual Production
Knowledge-based Conversion of Finite State Machines in
Manufacturing Automation
Georg Ferdinand Schneidera,∗
, Georg Ambrosius Peßlera, Walter Terkajb
aFraunhofer IBP, F¨urther Straße 250, 90429 N¨urnberg, Germany
bITIA-CNR, Via Alfonso Corti 12, 20133 Milano, Italy
Abstract
More and more information and communication technologies originating from the web are introduced in industrial automation
systems. The vision for future automation systems includes intelligent self-descriptive components, which exchange information
and potentially reason by themselves through knowledge-assisted methods. Formal domain descriptions are required to enable this
vision, including knowledge related to mechanical, electrical and control domains. This work focuses on formalizing knowledge of
the automation and control domain and investigates how knowledge-based methods can support the automated conversion between
different formalisms for modelling discrete behaviour in manufacturing automation: finite state machines. We detail our approach
by deploying the presented method in a use case related to the automation of a pick and place unit available from the literature.
©2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable
and Virtual Production.
Keywords: Automation and Control; Finite State Machines; Ontology; Knowledge-based Method
¡
1. Introduction
The introduction of Information and Communication Technologies (ICT) originating from the internet in the au-
tomation domain is envisioned to pave the way for novel services and tools related to the engineering and operation
of Industrial Automation Systems (IAS) [12]. During the engineering and operation of IAS a wide range and large
quantity of data is generated. It is envisioned that based on the novel technologies a digital twin, e.g. [23], exists next
to the real world system, which gives access to all relevant information and knowledge extracted from this data. A
number of intelligent engineering applications [2] can take advantage of a continuously updated digital twin [16], for
example, to execute simulations [17], reconfigure production modules [14] or self-description [8].
∗Corresponding author. Tel.: +49-911-568549145.
E-mail address: georg.schneider@ibp.fraunhofer.de
2351-9789 ©2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual
Production.
190 Georg Ferdinand Schneider et al. / Procedia Manufacturing 28 (2019) 189–194
2G.F. Schneider et al. /Procedia Manufacturing 00 (2019) 000–000
A prerequisite for the seamless interoperability between intelligent components of an IAS and novel services are
formal descriptions of the respective domains, i.e. mechanical, electrical and control. Semantic Web Technologies [4]
provide the ability to formally specify domain knowledge and additionally support the integration of heterogeneous
tools and formats apparent in the engineering of IAS [2]. Already existing ontologies enable applications such as
simulation of production systems [17], production and maintenance planning [28] or automated rule-based verification
of control logic in the automation domain [25].
A variety of formalisms exists for the specification of the actual, discrete control behaviour of a manufacturing
plant (see Section 2). The modelling capabilities of these formalisms differ [24] and a conversion between these can
be necessary when switching engineering tools or implementation languages. The correct conversion between these
formalisms constitutes one part of the control domain knowledge that needs to be explicitly defined and shared to
enable, for example, automated conversion between formalisms for envisioned modular self-configuration of compo-
nents [12] in future automation systems.
This work presents a novel method which enables the automated conversion of incompatible features of finite
state machines (FSM). We show how expert knowledge can be formalized to support the automated conversion of
certain characteristics of FSM. This explicit, formal domain knowledge can be shared among humans and machines
and thus provides benefits over other conversion approaches, where conversions are hidden in source code of model
transformations.
The remainder of this paper is structured as follows. In Section 2a brief overview on FSM in IAS and the formal
modelling thereof is presented. In Section 3a novel knowledge-based conversion method of features of FSM is shown.
The practical relevance of the novel method is studied by applying it in a use case presented in Section 4related to
the engineering of a Pick and Place Unit (PPU) [32], where Mealy characteristics of a UML state machine [19] are
converted using the described methodology in accordance to the needs of a simulation software tool.
2. Related Work
A variety of formalisms exist for the description of discrete-event control logic in automation systems. Based
on deterministic automata, early FSM were defined by Mealy [15] and Moore [13]. Harel combined and extended
these formalisms to his state charts [6], which are the basis of widely the adopted UML state machines [19]. In
automation a variety of abstractions for FSM exits such as Sequential Function Charts (SFC) [10], GRAFCHART [30]
and GRAFCET [9].
The exchange of information and knowledge on discrete control logic is deemed relevant and formats and models
exist. The DeMO ontology [26] focuses on the interoperability between discrete-event formalisms such as petri nets,
timed automata or Markov processes and presents mappings and tools for the conversion on schema and instance
level. The approach in this work is based on a similar idea but considers the specific needs of executable control in
industrial automation systems and links to adjacent domains. The FSM ontology [3] enables to formally describe UML
state machines. For instance, it is employed in a use case from building automation [27] to describe discrete-control
behaviour. The PLCopen XML format [21], which is based on XML-technology, enables the exchange of control logic
compliant to the SFC formalism [10]. For the description of state-based control logic of batch process according to the
ANSI/ISA 88 standard an ontology is presented in [8]. An approach for the automatic generation of Petri Nets models
from an ontological representation was proposed in [1]. An ontology to support the model-based design of control
logic (e.g. by detecting dead locks) is presented in [7]. The CTRLont ontology [25] provides means to describe control
actors and relates them to explicit, formal descriptions of control logic as well as physical components of automation
systems. The conversion between different formalisms apparent for the description of complex systems is studied in
[29], but the conversion knowledge is not formally specified.
The presented references show that a variety of formalisms as well as approaches for the model-based conversion
exist. However, there is still a research gap to explicitly model the conversion knowledge and share it among stake-
holders in the context of IAS. Hence, the remainder of this paper introduces a method to overcome this limitation.
Georg Ferdinand Schneider et al. / Procedia Manufacturing 28 (2019) 189–194 191
G.F. Schneider et al. /Procedia Manufacturing 00 (2019) 000–000 3
3. Knowledge-based Conversion between Finite-State Machines
As outlined in the previous section, a number of formalisms exist for the definition of discrete-control behaviour in
IAS using FSM. A difficulty observed in the engineering and implementation of control logic in IAS is that engineering
tools support only some of the common features and thus a conversion is necessary [24].
FSM comprises nodes representing the discrete states of a controller and edges that represent transitions between
states. In UML [19] states are graphically represented as rectangles with rounded corners and transitions as arrows. A
fundamental difference in FSM is the association of actions to transitions (Mealy-like) or to states (Moore-like). This
is rooted in the early formalisms for FSM namely Mealy [15] and Moore [13] machines.
In particular, Mealy machines can be converted into almost equally behaving Moore machines [13]. This knowledge
is used to convert Mealy-like features of UML state machines, i.e. actions associated to a transition of a UML state
machine are redefined as actions in the target states. In Fig. 1the methodology for the automated knowledge-based
conversion is depicted. The specification of the finite state machine, e.g. a UML state machine, is formalized using an
ontology-based approach (StateMachineOntology [25]). The resulting individuals are hosted in a triple store.
The knowledge about the conversion is formalized using SPARQL Update [4] that can be executed manually or
automatically to perform the feasible conversions. An excerpt of the SPARQL Update to convert a Mealy machine
into a Moore machine is shown in Listing 1. For reference, Fig. 2shows an example of triples representing the Mealy-
like FSM in Fig. 1.
triple store with
query engine
formalise
Mealy-like Moore-like
SPARQL
generate
Fig. 1: The designed UML state machine is formally represented and stored in a triple store. Expert knowledge for the conversion encoded as
SPARQL update is executed and the respective converted state machines can be generated. Here, Mealy-like characteristics are converted to Moore-
like.
Listing 1: SPARQL update encoding knowledge to redefine actions from transitions to states in UML state machines.
PREFIX [...]
DELETE { ?t sm:transitionAction ?act . }
INSERT { ?tar sm:do ?act . }
WHERE {
?act rdf:type sm:Action .
?t sm:transitionAction ?act .
?t sm:transitionGuard ?gua .
?t sm:target ?tar . }
192 Georg Ferdinand Schneider et al. / Procedia Manufacturing 28 (2019) 189–194
4G.F. Schneider et al. /Procedia Manufacturing 00 (2019) 000–000
:init
sm:InitialState
:t1 :WaitingForStart :t2
sm:source sm:source sm:target
sm:Transition sm:SimpleState
owl:Class owl:ObjectProperty
rdf:type
owl:NamedIndividual
sm:transitionGuard
:t1-gua :t1-act
sm:transitionAction
sm:Guard sm:Action
Nomenclature:
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns
#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX sm: <https://w3id.org/ibp/StateMachineOntology#>
PREFIX : <http:/example.org/test#>
:Extending
sm:target
sm:Transition
Fig. 2: Excerpt of instances representing the Mealy-like FSM depicted in Fig. 1according to the StateMachineOntology [25].
4. Use Case
We study the usefulness of the described methodology by applying it in an open use case related to a PPU available
from literature [31,32]. We investigated a scenario where the discrete control logic is specified as a UML state
machine and then the control and the plant are virtually tested together. We simulated both of them using the Modelica
modelling language [5], where different options exist for the implementation [20,24].
The PPU use case [32] is designed as an open use case for manufacturing automation and it consists of several
scenarios. Its documentation [32] provides a detailed textual description for each scenario including visualizations of
involved components, SysML [18] class diagrams, and state machines specifying the control logic of the plant. The
Scenario0 [32] is studied that involves a stack separating work pieces from storage. As we do not have access to the
real machinery, we instead modelled the discrete manufacturing process based on the Petri Net formalism [13] using
PNlib [22], i.e. a Modelica library to model Petri Nets.
An initialization procedure is executed for each of the PPUs components before the automated operations [32].
Fig. 1shows a UML state machine (Mealy-like) that describes the initialization procedure Stack ACT Init, which
is slightly adapted from [32], where the actions are associated to the transitions of the state machine. We reimple-
mented the SysML specification in UML to avoid differences between the two formalisms when reusing the State-
MachineOntology [25] for the formal description. The proposed conversion methodology is applied to the result-
ing specification. In particular, we modelled the Scenario0 in Modelica [5] and simulated the models using the Dymola
simulation environment to test our initialization procedure. Within Modelica different ways exist to implement FSM.
For instance, the approach reported by Schamai et al. [24] supports Mealy-like semantics, whereas the StateGraph
library [20] enables to model state machines with Moore-like semantics.
The initialization procedure is implemented in Modelica as specified with Mealy-like semantics [24] (see Fig. 1,
Mealy-like) and considering the specification obtained by using the conversion method with Moore-like semantics (see
Fig. 1,Moore-like). We simulated both controllers with the plant model of Scenario0 and the results of the simulation
are depicted in Fig. 3a and Fig. 3b, respectively. The initialization procedures start at time t=0. The stack model is
parameterized to require 2 seconds to extend and 1 second to retract. The naming of the interface variables corresponds
to the definitions provided in the documentation [32]. X1 1is the output signal of the control and it is initially set to
true. After two seconds the stack is extended and at t=2 the corresponding signal X1 2evaluates to true. After one
more second the separator is retracted and the respective output from the stack X1 3(stack is retracted) evaluates to
true (t=3). A boolean value indicating the activity of a respective state is denoted in the lower plot of each subfigure,
where the naming of the states complies to the states visible in Fig. 1. The simulation results show that both of the
implemented state machines behave exactly in the same manner.
Georg Ferdinand Schneider et al. / Procedia Manufacturing 28 (2019) 189–194 193
G.F. Schneider et al. /Procedia Manufacturing 00 (2019) 000–000 5
0 1 2 3 4 5
true
fals e
true
fals e
true
fals e
sc0_St ack.X1_1
sc0_St ack.X1_2
sc0_St ack.X1_3
0 1 2 3 4 5
true
fals e
true
fals e
true
fals e
true
fals e
sc0_ACT_Init_Schamai.WaitingForStart
sc0_A CT_Init_Schamai.Extending
sc0_A CT_Init_Schamai.Retracting
sc0_ACT_Init_Schamai.WaitingForRetracted
(a) Simulation results for the initialization procedure with Mealy-like se-
mantics. Top: sc0 Stack - Modelica model name, X1 1, X1 2, X1 3 con-
trol signals as defined in [32]; Bottom: sc0 ACT Init Schamai - Model
name, Boolean signals indicating if the respective state is active.
0 1 2 3 4 5
true
fals e
true
fals e
true
fals e
sc0_St ack.X1_1
sc0_St ack.X1_2
sc0_St ack.X1_3
0 1 2 3 4 5
true
fals e
true
fals e
true
fals e
true
fals e
sc0_A CT_Init_SG.WaitingForStart.active
sc0_A CT_Init_SG.Extending.active
sc0_ACT_Init_SG.Retracting.active
sc0_A CT_Init_SG.WaitingForRetracted.activ e
(b) Simulation results for the initialization procedure with Moore-like
semantics. Top: sc0 Stack - Modelica model name, X1 1, X1 2, X1 3
control signals as defined in [32]; Bottom: sc0 ACT Init SG - Model
name, Boolean signals indicating if the respective state is active.
Fig. 3: Results for simulating the both options for five seconds; x-axis in both subfigures: Simulation time in seconds.
5. Discussion
Different problems related to the conversion between state machine formalisms exist [24]. In particular, capturing
the correct execution order of actions (e.g. entry in UML state machines) needs to be carefully investigated. In our
approach actions related to transitions are relocated to do-actions, which might lead to different behaviour in special
cases.
Within this work the presented method is evaluated in a simple yet descriptive use case for demonstration purposes.
Further testing and evaluation is required potentially using other scenarios of the PPU [32] use case.
Within the use case presented in this work the conversion from the triples in RDF graph to Modelica source code is
undertaken manually. In future we aim at automating this process by implementing routines to consume and generate
Modelica code.
In the current approach the expert knowledge is separately defined as SPARQL query. Using the SPARQL Infer-
encing Notation (SPIN) [11] it could be included for reasoning under the OWL-Description Logic (OWL-DL) [4]
profile.
6. Conclusion
This work aims at contributing to the formal description of the control domain in industrial automation systems.
We presented a novel methodology to perform the knowledge-based conversion of features of Finite State Machines
(FSM). The FSM are explicitly modelled using the StateMachineOntology [25] and the conversion knowledge
is explicitly encoded as SPARQL Update, thus, the approach can be automated. Moreover, this explicit conversion
knowledge can be shared among interested stakeholders.
The authors tested the method in a use case related to a Pick and Place Unit [32] to convert incompatible features
of a UML state machine and provide appropriate formalisms to a simulation application. The simulation results show
that the originally specified and the converted state machine behave in the same way. In future we would like to
investigate how explicit mappings can be formalized to enable consistent semantics between different finite state
machine formalisms. A starting point is the work in [24] and for discrete-event simulation models the work in [26].
194 Georg Ferdinand Schneider et al. / Procedia Manufacturing 28 (2019) 189–194
6G.F. Schneider et al. /Procedia Manufacturing 00 (2019) 000–000
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