Identification of ADACOR Holons for Manufacturing Control

IDE'iTIFlCATIO'i OF ADACOR HOLO'iS FOR :\IA'iUFACTURI'iG CO'iTROL

Paulo Leitao l , Francisco Restivo 2

Poh"lechnic IlIslilule o(Brogall~·a. Quillla S" Apolimia. Apal'lac!o 134, P-530 1-85 7 Brog({/I~'a, POl'lugal, pleilao@)pbpl : FocI/Ill' ojElIgi/7l!erillg. Ullil'ersil\' ojPol'lo. Rua DI'. Robl:'I'lo FI'ias. P-4]OO-465 Porlo. Porlugal./ir(l..lli'. uppl I

Abstract: The identification of thc holons is a critical task in thc dcvelopment of holonie manufacturing control system applications. This paper discusses a methodology to dcvelop distributed manufacturing control systems for the shop floor level. based on the ADACOR holonie architecture. The methodology comprises a set of steps, the more relevant being the identification of the manufacturing holons and the development of holons based on rc-use libraries supportcd by the ADACOR approach. The paper focus on the first step and describes a procedure to identify the holons present in a manufacturing system. Cop\'righl ' 20031FAC Keyword: Intelligent Manufacturing Systems, I-lolonic Manufacturing Systems, Agents, Agile Manufacturing, Shop Floor Control.

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Il\TRODUCTlO'i

The development of agent-based holonic manufacturing control applications encompasses subjects such as agent platforms. agent models, agent communication languages, content languages, interaction protoeols and ontologies.

Enterprises always look for more productivity, more quality, more flexibility, more agility and better adaptation to unexpected disturbances. This is specially true in the emerging global economy, where the demand for mass customized products with very short life cycles put new requirements in terms of the ability of enterprises to respond to change

A key issue in the development of holonic manufacturing control applications is the definition of which manufacturing components will be represented by holons, and how will each holon be developed.

Steady state analysis is becoming less important. while dynamic response is becoming a key issue in the design of new manufacturing systems, with large impact at all levels, both technical and socioeconomical. New working relationships are now as important as new manufacturing devices and new manufacturing control architechlres.

This paper introduces a methodology to design, develop and implement holonic manufacturing control systems using the ADACOR architecture and components, addressing one of the major issues in the methodology, that is a procedure to identify the manufacturing holons present in the manufacturing system.

Several architecturcs using emergent paradigms and technologies have been proposed, such as agentbased and holonic manufacturing systcms.

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ADACOR is an agent-based holonic manufacturing control architechlre, focused at the shop floor level. \\hich aims to improve the agility, flexibility and reaction to disturbances through the definition of holon classes which are targeted to the spccific needs of manu fachlring systems.

ADACOR: A'i HOLO'iIC APPROACH TO MA'iUFACTURI'iG CO'iTROL

The ADACOR architecture addresses the distributed l1lanufachlring control systems at shop floor level. and has in mind thc dynamic and agile adaptation to disturbances (Leitao and Rcstivo, 2002).

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2.1

operational holons responsible to cooperate, since they have a local and partial view of the system.

Factory Entities

The architecture is based on the holonic manufacturing systems paradigm and supported by a set of autonomous and cooperative holons. each holon being a representation of manufacturing entity, such as a numerical control machine, a robot or an order. The implementation of the holons entitics is done using the agent technology, taking advantagc of its modularity. decentralization. and ability to support dynamic and complex design fcatures.

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The ADACOR architecture considers four classes of manufacturing holons: the product. task, operational and supervisor holon (Leitiio and Restivo, 2002).

Fig. Balancing Decentralisation

Each product is represented by a product holon that contains all knowledge related to the product and process. and is responsible for the process planning. It is a kind of static holon. but it must reflect the continuous improvement of the manufacturing process, for instancc.

The supervisor holon is also responsible to aggregatc thc skills and capacity of the opcrational holons that belong to the group. offering the combined services to external entities in the manufacturing system. These groups may be built according to geographic or layout conditions or to the need to combine synergies in order to offer an aggregate set of skills.

Manufacturing orders to be executed in the factOly plant, bascd on customer andlor forecast demands, are represented by task holons. Each task holon is responsible for the control and supervision of the execution of a manufacturing order and contains the dynamic information about the manufacturing order.

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An operational holon can be a set of several operational or supervisor holons, as in Fig. 2. As an example. a manufacturing cell can be represented by an operational holon that comprises several other operational holons, each one representing a manufacturing resource, and one supervisor holon representing the manufacturing cell controller. In this case the supervisor holon acts as the logic component of the holon, and the several operational holons act as the physical part of the holon. Additionally, each of the operational holons that represent a manufacturing resource can comprise several other operational holons, such as the numerical control machine itself and the several tools stored in its tool magazine.

The operational holons represent the physical manufacturing resources, such as operators, robots and numerical control machines, managing their behaviour according the resource goals, constraints and skills, and optimising their schedule agendas.

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Supervisor Role

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The ADACOR architecture is neither completely decentralised nor hierarchical, but balances between one and the other, according to the dynamic requirements of the environment and of the operation. Basically, 111 normal operation, a supervisor holon supervises the activity of the holons under its domain, while when a disturbance occurs holons may have to find their way without the help of the supervisor holon.

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The product. task and operational holons are quite similar to the product, order and resource holons. presented at the PROSA reference architecture (Van Brussel et al.. 1998)

It is possible to have different supervisor holons coordinating several manufacturing cells with an organisational structure between them or to have only one supervisor holon that represents the factory shop floor controller. This fractal feature allows high flexibility for the control structure organisation. creating foundations to support the combination of global optimisation with reaction to disturbances.

The supervisor holon. unlike PROSA's staff holon, integrates the scheduling and control mechanisms, which allow to coordinate the holons under its domain. The supervisor holon is present in hierarchical organisational control structures. where it represents a shop floor or a cell controller, for instance. In the heterarchical control approach. the supervisor holon is not present in the system. being the task and

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Autonomy and Dissemination Concepts

In the ADACOR architecture. operational holons are autonomous entities that have capability to self-

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previous solutions (knowledge, know-how or components) should be re-used. if possible using the re-use libraries provided by ADACOR.

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The autonomy factor, a, associated to each operational holon is a parameter that evolves dynamically in order to adapt the holon behaviour according to its goals and constraints and to the environment where it is placed. It can be a continuous or discrete parameter,

The proposed methodology for the development of holonic manufacturing control applications supported by the ADACOR architecture, is based in GERA (Generic Entcrprise Reference Architecture), which is part of GERAM reference architecture (Vemadat, 1996), and consists of the following phases, as represented by Fig. 3: requirements, identification of manufacturing holons. design and implementation of the holons, configuration and operation.

Currently. a two value discrete parametcr is being used. In the stationary state. the holons arc organised in a hierarchical stmcturc, each operational holon having a {Low: autonomy factor, which means that the operational holon follows the schedule proposals issued by the supervisor holons.

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The occulTence of a disturbance requiring an agile and fast reaction may trigger the change of the autonomy factor to {H igh} and thc self-organisation into a heterarchical control stmcture (Leitiio and Restivo, 2002).

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The need for re-organisation is disseminated to usmg pheromone-Iike supervisor holons, dissemination mechanisms, through the propagation and deposit of the pheromone to the neighbourhood supervisor holons. The holons associated to each supervisor holon receive the need for re-organisation by sensing the pheromone, incrcase the autonomy factor according with the type of disturbance, and the system re-organises in a heterarchical stmcture.

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The system requirements phase focuses on the definition of requirements and objectives viewed from a holistic point of view, and the control strategies that should be used.

HOLONIC MANUFACTURING CONTROL METHODOLOGY

The identification of manufacturing holons aims to identify the holons from the manufacturing system components, their role, behaviour and interaction with other holons. The functional behaviour and interaction aspects are done in this phase, because the methodology aims to use a bottom-up approach in order to support the re-configuration and selforganisation capabilities.

The application of sophisticated tools and technologies does not guarantee by itself the success of the development of control and integration applications. It is necessary to fit those tools and technologies to the context of the control application and to take in consideration its requirements and particularities, and to use models that allow the understanding of the system under the functional and informational point of views.

The design and implementation phase is related to the development of manufacturing holons. This phase is divided in two steps: development using the re-use libraries for the implementation of the generic functions and behaviours and development of specific components depending the application requirements. As examples of specific components, it should be necessary to develop specific wrappers (independent from the holonic control approach) for each physical resource, introduce decision-making and learning mechanisms, etc.

The ADACOR architecture provides a set of models, components and re-use libraries that address the manufacturing control at shop floor level, based on the concepts described in the previous section. However it is important to define a methodology to design, develop and implement holonic manufacturing control systems using the ADACOR architecture and components. The methodology intends to define how to apply best the proposed architecture.

The configuration phase deals with the configuration of manufacturing holons, which must be mapped to the characteristics of the entities the holons represent, e.g. characteristics of a resource for the operational holons or product data model for the product holons. and the tuning of some parameters, for example

The implementation of new control systems is seldom done from scratch. requiring a huge amount of time and effort to develop new applications. in order to simplify the solution de\·clopmenl. past or

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The new control approaches should consider both the changes imposed by environment in which the manufacturing system is acting and the changes in the manufacturing system, such as addition or removal of manufacturing components, during the operation phase. In this way, the methodology should consider the reconfiguration of the system redefinition and consequent adaptation of the holonic manufacturing control application, using the re-use components available in ADACOR architecture.

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In the following section it will be described the procedure proposed to identify the holons presented in the manufacturing systems. 4.

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Once the manufacturing control application is developed and implemented, the system enters in the operation phase. In this phase one aims to achieve the continuous improvement in the control system, with the re-tuning the control parameters and learning from the execution. Additionally, little attention has to be devoted to the changes of the manufacturing system itself along its life-cycle.

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Additionally. during the identification procedure it is important to consider another object-oriented concept: aggregation. The aggregation concept means that it is possible to aggregate holons as a set of related holons and form in their turn a bigger holon with its own identity, knowledge and skills. Examples of this concept, is found in the aggregation of sensors, actuators and tools into a manufacturing machine holon, or in the aggregation of several manufacturing machines and transport machines into a flexible manufacturing system holon.

IDENTIFICATION OF MANUFACTURING HOLO:\,S

A cmcial problem associated to the development of holonie manufacturing control applications is how to holonify an already existing manufacturing factory plant, i.e. how to identify and model the manufacturing holons from the manufacturing components presented at the factory plant. Questions like what manufacturing components should be considered as holons, or should the cutting tools or parts be considered as holons, are frequent from those who need to develop holonic applications.

The manufacturing components presented at the shop floor level comprise a set of shop floor resources R = {rh r2,' .. , rr}, a set of products P = {PI, P2, P3,' ., pp}. The shop floor resources can be processing machines PM = {pm" I w El}, operators H = {h" I WE I}, transporter resources T = {t" I w El}, mover resources M = {m" I w El}, auxiliary resources A = {a" I WE I}, tools To = {to" I WE I}, and batches of raw material RM = {rm" I WE I}, with R = (PM u HO uT u M u A u To u RM)

Recent research has been eanied out to create a methodology that supports the identification of software agents (Wooldridge and Ciancarini, 2000). According to (Bussmann el al.. 2000) none of those methodologies is applicable to agent-based or holonic manufacturing control systems, because those methodologies provide models that are inappropriate for production control systems. The need of an appropriated methodology to meet the production control requirements has led to numerous research work in this area, such as described 111 (Ritter el al., 2002; Van Bmssel el aI., 1999).

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The procedure to holonify manufacturing systems for the development of manufacturing control applications using the ADACOR architecture. requires the identification of the product. task, operational and supervisor holons. The ADACOR holons arc structured using object-oriented concepts, like aggregation and generalisation.

Identification of Operational H%ns

The first step of the procedure is to identify all manufacturing components (machines, tools, parts, etc.) present at the manufacturing system, finding the set of resources R, and building the sets PM, H, T. M, A. To and RM, identifying for each object its role. objectives. and behaviour.

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The task holons are created dynamically according to the needs to produce the products. so it is not necessary to define them at the design phase. When a product holon is required to produce the product it represents, it should launch a task holon, passing the due date and a process plan that defines the required sequence of operations to execute the product and the required machine types for each operation,

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The identilication of the supervisor holons results from the level of control coordination of the system. The SH set, which represents the list of supervisor holons, is built by assigning a supcrvisor holon to each identified coordinator object, such as shop floor controllers or manufacturing cell controllers, and defining the set of operational holons that belong to its coordination domain.

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Starting from the clements of R identified in the prcvious step, thc ncxt stcp is conccrncd to thc identification of the interactions between them, in order to extract the dependencies between the objects.

The last step is to identity the logic dependencies between holons presented at different classes of holons.

Aggregating the objects according with the identified dependencies (for example, tools in a machine, AGVs in a transport system, etc.), it is possible to build W, which is a sub-set of R, that contains the list or resources aggregated by its dependencies. according to some aggrcgation level.

The total numher of manufacturing holons identified in the design phase will be the sum of the cardinality of OH, PH and SH.

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where -< represents the dependency between two components. From the set R' it is possible to extract a list of operational holons, called OH, by mapping each R' object into an operational holon. indicating its attributcs according to an appropriatc ontology, and modelling its behaviour using Petri Nets.

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APPLICATION OF THE METHODOLOGY

The described methodology has been used to identify the manufacturing holons is the FMS platform of ClM Centre of Porto, Fig. 6. The FMS platform comprises a manufacturing cell, a material storage and transportation cell. a calibration cell and an assembly cell.

Identific(ltioll ofProduct H%m'

The next step is to identify all products available in the system and the relation between them (included in the product data model). Each product Pi comprises a set of operations 0 i = (Oil, 0,2, 0,) .... O;n). partially ordered based by precedences n i= { I Oij < O'k' Oij must precede Oik}' Each operation Oik, has a set of requirements and constraints that a class of machines should satisfy to execute the operation.

The palletising and calibration cell, has a human operator, and is responsible for the assembly and calibration of tools and the palletising of the materials that circulate in the shop floor. The material storage and transportation cell is responsible for the transportation of materials within the shop floor and for the temporary storage of materials. The cell has an AGV, an Automatic Storage/Retrieval System (AS/RS), and scvcral transfer tablcs among thc shop floor. Additionally. there are several cutting tools to be loaded in the machines according the needs. The assembly cell has the objective to assembly the components to achieve the final product. The cell has a robot and a CCD camera coupled to the robot, for visual inspection. The manufacturina cell is responsible for the physical processing of the parts. The cell comprises two CNC machines and an anthropomorphic rohot for load/unload of the machines.

Bik = {B ikw I WE I}. These requirements define a set of resources Pik that can execute the operation Oik. which is a sub-set of R, Pik<;;; R. The procedure to identify the product holons is similar to the identification of operational holons. First, it is necessary to find the set of products and sub-products manufactured by the factory plant. building the set P. A product holon will represent directly each element of P, building the PH set.

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transport system. one operational holon could be introduced for each AGV or conveyor and optionally a supervisor holon to act as cell controller. On the other hand. if the transport system is viewed as an entity that provides services. it is only necessary to consider an operational holon that represents all transport system.

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The development of holonie manufacturing applications requires the identification of the autonomous entities present in the manufacturing system. which may be represented by holons, and the definition of their role. responsibilities, behaviour and interaction with other holons

Fig. 6 - Flexible Manufacturing System Platform

This paper introduces a procedure to holonify an existing manufacturing system. based mainly in the physical and logical aggregation of manufacturing entities, in order to support the development of manufacturing applications using the ADACOR architecture.

Using the proposed methodology. the first step is to define the set of resources available in the shop floor. From the previous analysis of the FMS platform it was found the following resource elements. fonninl! the R set: hi (human operator), t 1 (AGV devicc). t: (AS/RS system), tO j (scveral tools available in the factory), ml (assembly robot). a. (CCD camera), pm. (C C turning machine). pmz (CNC milling machine), m2 (handling robot). -

REFERE:\CES Bussmann, S.. N. Jennings and M. Wooldridge (2000). On the Identification of Agents in the Design of Production Control Systems. In: AgentOriented So(t\mre Engineering, P. Cianearini and M. Wooldridge (eds.). LNCS 1957, SpringerVerlag, pp. 141-162. Leitao, P. and F. Restivo (2002). Holonic Adaptive Production Control Systems. In: Proceedings of

The second step of the methodology is to represent the physical dependencies between the identified objects. From the platfonn description it is possible to verify that the CCD camera is dependent of the assembly robot, so it is not considered a holon. Additionally, each cutting tool belongs uniquely to one machine, being possible to aggregate each tool to the appropriated CNC machine and consider only one component. If the tools are shared by several machines, the tools could not be aggregated to the machine and it was necessary to consider a holon for the machine and one holon for each tool.

.special session on Agent-based Intelligent Automation and Holonic Control Systems of the 28'1> Annual Conference of the IEEE Industrial Electronics Society, Sevilla, 5-8 November. Ritter A .. W. Baum. M. Hopf and E. Westkauper (2002). Agentification for Production Systems. In: Proceedings of European Joint Conferences on Theo':"!' and Practice of Software, Grenoble, France, 6-14 April. Van Brussel, H., J. Wyns. P. Valckenaers, L. Bongaerts and P. Peeters ( 1998). Reference Architecture for Holonic Manufacturing Systems: PROSA. Compwers In IndustnJ. 37, pp. 255-274. Van Brussel. H., L. Bongaerts, J. Wyns, P. Valckenaers and T. Ginderachter (1999). A Conceptual Framework for Holonic Manufacturing Systems: Identification of \1anufacturing Holons. Journal of Manufacturing S,stelJls, 18 (I), pp. 35-52. Vemadat. F. (1996), Entelprise Modeling and integration. Chapman & Hall. Wooldridge, M. and P. Ciancarini (2000). AgentOriented Software Engineering: The State of the Art. In: Agent-Oriented So/ilmre Engineering, P. Ciancarini and M. Wooldridge (eds). LNCS 1957. Springcr- Vcrlag.

After the analysis of the physical dependencies, it is possible to represent each aggregate component belonging to R' by an operational holon. In this way, the operational holons identified are: hI. t h t 2, mJ, pml' pmz and m2' In spite of not being referred, buffers must bc considered. In this case it was assumed that each buffer has a physical dependency to a machine or workstation. The identification of supervisor holons can be done by analysing the description of hierarchical levels in the platform control. As the shop floor is divided into cells, it is possible to identify one supervisor holon for each cell (mcS. acS. m5tcS and pccS) and one supervisor holon for the shop floor control (5fS). Another situation not considered in this casc is the transport system. The holonification depends of the level of aggregation. When the focus is in the

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