RFID Uses for Prosis Ambient Control

Patrick Pujo, Yves Dubromelle and Fouzia Ounnar

LSIS, Av Esc. Normandie Niémen, 13397 Marseille cedex 20, Marseille, France

Abstract. Product traceability is now an obligation in terms of observability

and flexibility of manufacturing systems and logistics chains. RFID

technologies are due to be more and more integrated into existing

communication networks. New infotronics technologies will enlarge the

capabilities to interact, to react and to customise control systems with

innovative possibilities. New control approaches should be proposed based on

the use of emerging technologies that would allow their operability in

manufacturing sites. A model for ambient control production systems is

proposed, based on a set of holonic entities interacting through set of entities

that offer ambient service. After describing the PROSIS model, the ambient

services that can be provided are presented.

1 Introduction

Ambient intelligence applied to manufacturing systems will deeply transform

approaches to production organisation and control. Infotronics technologies [4] will

enlarge the capabilities to interact, to react and to customise control systems with

innovative possibilities that should already be envisaged and thoroughly studied.

Indeed, future production needs are already expressed through emerging paradigms

such as mass customisation requiring individualised and flexible product tracking,

lean approach leading to drastic stock reduction and enhanced flow control, Six sigma

approaches bringing more rigour and requirements in the results to be achieved, or

product traceability obligation, in particular for safety and maintenance needs.

In order to address these objectives, new control approaches should be proposed,

based on the use of emerging technologies that would allow their operability in

manufacturing sites. Indeed, in a highly competitive international environment, the

control of production system with efficiency is a key point for a company. Traditional

management and control methods show their limits against the increase of production

constraints, and it becomes essential to study new control approaches. We propose,

for the control of production systems, architecture without any hierarchical decision-

making dimension. The proposed approach uses holonic paradigm and multicriteria

model in the decisional process.

After presenting the actual research works in infotronics technologies applied to

control systems, we will propose a model for ambient control systems in production,

based on a set of holonic entities interacting through set of entities that offer ambient

service. Finally, we will describe the various parts of this model, whether nomadic or

Pujo P., Dubromelle Y. and Ounnar F.

RFID Uses for Prosis Ambient Control.

DOI: 10.5220/0002911000810088

In Proceedings of the 4th International Workshop on RFID Technology - Concepts, Applications, Challenges (ICEIS 2010), page

ISBN: 978-989-8425-11-9

not, in general interaction or in specialised interaction. We will then present some of

the ambient services that can be provided.

2 Manufacturing Ambient Control

The ambient intelligence concept comes from the federation of topics that are of

apparent different nature, like nano and micro systems, wireless technologies,

distributed computation or sensor technologies. One objective of the convergence

between these technologies is to provide new users services, such as home

automation, Smart Objects… This ambient intelligence concept, also called ubiquity,

gives users the possibility to interact from any place with many interconnected

infotronics devices, sensors and actuators, embedded around them and operating

through ad hoc networks with distributed architecture. Intelligence is referred to as

ambient because of the omnipresence of wireless communicating non apparent

computing agents. The common point between future production imperatives as those

mentioned above is the increasing need of ad-equation between the current

manufacturing system and its associated information system [19], combined with

deeper granularity in which the detail level is at part unit. Infotronics technologies

offer a large scope of solutions to answer these imperatives and find already many

applications in the manufacturing system area, changing usual operating modes.

According to [10], the impact of introducing such technologies in manufacturing

systems is considerable. It concerns for instance fast improvement of product tracking

allowing stock level reduction and improved exploitation of product availability, due

to real time reliability of product data. On a longer term, more advantages can be

developed for mass customisation management [6], product secureness [3] or

collaborative production-distribution organisation [17].

Presently, research concerns essentially the migration from simple use of

infotronics technologies (like RFID: Radio Frequency IDentification) to the concept

of intelligent product and its induced applications in heterarchical control. [14]

propose the concept of self scheduling driven by the product or by the interaction

product-process. This interaction allows local and contextual generation of tasks

oriented trade used for real time control of the resource. A product carries information

that it is able to communicate to the decision centres associated to resources. In that, a

product can be qualified as active. The entity managing immaterial aspects

(information, communication and decision) is called I-product. This definition is

closed to that given by [9], which defines an intelligent object by only its

communication ability, completed by its associates’ service delivery, communication

transparency and environment adaptability to intelligence. In parallel, [10] defines the

intelligent object concept as a dual object (physical and virtual) with information

processing capability (memory, communication, computing, action …). A

complementary concept is the one of extended product [18] which associates a

provided service to the product. This service, more and more intelligent, should be

compliant with customers’ needs. In order to offer high value added services related

to a physical product, this one should be associated to an immaterial component

carrying information and knowledge and made of services, engineering, software…

According to Hribernik [5], this immaterial component, called Avatar, allows

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implementing the global concept of distributed production and decentralised

information management specific to each product. In this context, the use of new

technologies, such as RFID, wireless networks and ubiquitous processing allows

linking the product to a network of applications related to production, maintenance…

In France, Product Driven Systems are subject to new investigations dealing on one

hand with the control of interactions between fabrication process and product and on

the other hand, with the integration of new technologies such as Wireless Sensor

Networks and Radio Frequency Identification in the cybernetics loop. These

technologies should provide the product with memory, computation and

communication capabilities: it thus becomes ‘active’ within the manufacturing system

that processes it. This ‘active’ product can be given means for capturing environment

variations, making decisions and thus fully interacting with its environment (process

resources, other products, human operators, etc.) [12]. Finally, whatever name given,

I-product, virtual product, extended product, avatar or other, future operation of

manufacturing systems will rely on this type of entity. However, objects of different

nature will have to inter-operate: the holonic paradigm [7] is not restricted in the

Holonic Manufacturing Systems [8] to an oriented product vision only; there exist

other types of holonic entities with a role as much important. Among proposals from

the HMS community [2], Product, Resource and Order Holons are three types of basic

Holons that are most recurrent [15]. We are referring to the most known holonic

architecture: PROSA [20]. Starting from there, the control of manufacturing systems

by products only is not enough. Indeed, the product as such does not carry all the

operational constraints and all related information that would allow making optimal,

or at least satisfactory, control decisions. Each of the two other Holon types (Resource

and Order) brings its own set of data and constraints, making a different viewpoint.

To take into account these data and constraints, we propose control that integrates

different viewpoints coming from different interacting entity types.

3 Prosis Model

3.1 PROSIS Model Presentation

PROSIS (Product, Resource, Order, Simulation Isoarchical System) [13] proposes a

holonic and isoarchical approach that facilitate the implementation of ambient control

solutions for manufacturing systems. We wish to study and develop decision

mechanisms with architecture and information system being as close as possible of

the material system, even to the image of the organisation of this system, and directly

interconnected to it via infotronics technologies. This approach objective is to gain in

terms of structural and decisional flexibility, and thus in terms of reactivity and

adaptability.

Initially thought for modelling complex social systems, holonic systems are made

of entities (the Holons) in mutual interactive dynamic relationships. A Holon should

be seen as a whole or a part of a whole: this is the Janus effect expressing among

others recursion notions. This approach marks a break with previous hierarchical

models in which components are of the type ‘master - slave’ following a tree like and

not varying topology of decision centres. This is reinforced with the respect of orders

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by the slave decision centre. Indeed, a Holon has a decisional intelligence giving the

possibility to act on its own behaviour and also to act on the behaviour of the systems

it belongs to [15]. Hierarchical decomposition is replaced by Holon recursion and

implementation of the Janus effect. This opens a wide level of freedom for the

implementation of a control system according to an heterarchical architecture, that is

able to mix centralised and non centralised parts.

Different holonic architectures are proposed in the literature for control HMS [2].

These architectures present however the inconvenient of giving an important place to

the hierarchy concept in decision making. For example, when basic Holons cannot

find in PROSA a scheduling solution, a solution is derived by a Staff Holon which

uses a centralised processing algorithm. In order to simplify the implementation of an

ambient control system, we suggest that all interacting entities be at the same decision

level. Thus, there is no possibility of decisional hierarchy leading to manage

complicated decision making rights. This means an isoarchical architecture. The

isoarchy concept (word made from Greek iso (equal) and archy (power)) refers to the

same decision power and thus to a complete absence of hierarchy. In a decision

system made of several decision centres, a decisional architecture can be qualified as

isoarchical when each decision centre has the same decision capability. This property

can easily be obtained when decision mechanisms are duplicated in each decision

centre and appropriately parameterised. Isoarchy appears as a particular specification

of the concept of heterarchy and as the opposite of the concept of hierarchy [11].

However, within this category it expresses an even concept that can be applied only to

truly and totally equalitarian architectures. This particular situation between Holons

has been foreseen in holonic systems through the concept of ‘flat holonic form’ [1].

However, this architecture in which relationships between Holons makes a complete

graph was not really deeply studied.

PROSIS aims to explore this approach which is specially suited to ambient control

systems: indeed, a single hierarchy level permits Holons to directly and simply access

entities offering ambient services. The absence of a central decision system forbids

any predefined or forecast organisation of manufacturing system operations. These

should thus be progressively organised by the Holons themselves with the support of

ambient service entities. This self-organisation assumes real-time characteristics

considering all information characterising each Holon contributing to define the

operations. We then talk about self-organised control functions. These functions are

integrated into the intelligence associated to each Holon. For that, we define a Holon

as a conceptual entity based on the association of a Material Structure (the M_holon),

an Information System and a Processing System (the I_holon) that provides a

decisional intelligence allowing interaction with other Holons. This structure allows

recursive decomposition of manufacturing systems, in compliance with the holonic

paradigm, by clearly showing the duality and parallelism between the real world

(material) and the informational world (immaterial, in which data and decision

making stands).

For a nomadic Holon, the main problem is synchronisation between material and

immaterial parts of this Holon. This is solved with infotronics technologies: The

M_holon has an ID tag containing at the minimum a unique identification number

associated to the Holon whose value is stored in the information system of the

I_holon.

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A network of I_holons can be associated to a set of Holons making a

manufacturing system to create an I_holon hyper graph following recurrent

composition rules. We shall describe the different base Holons: the Product Holon

(PH), the Resource Holon (RH) and the Order Holon (OH) which feature evolutions

with respect to PROSA. Management of production related knowledge results from

interactions between these three types of Holons.

PH-RH interactions provide process knowledge: resource operating methods,

capacity, reachable quantities and possible results. PH-OH interactions indicate

production knowledge: batches description (quantities to deliver, product reference,

delivery schedule …). RH-OH gives execution process knowledge: follow up of

process execution by resources, monitoring of progress, of process interruptions …).

However, a major difference with PROSA is the disappearance of the Staff Holon

which is not needed in an isoarchic context. We replace it by a Simulation Holon

having a totally different objective: starting from the manufacturing system status

obtained by analysing interactions between the other Holons, the aim is to simulate

the manufacturing system evolution, to provide evolution indications to the workshop

manager and to anticipate eventual failures via diagnosis actions.

This Holon does not contribute to self organisation, but it facilitates the role of the

workshop manager: it provides control with proactive properties. The Simulation

Holon is not addressed in this paper.

3.2 Product, Resource & Order Holons

A Product Holon is made with a M_product (the material object) and an I_product

containing the fabrication process (knowledge needed to perform product

manufacturing and to obtain appropriate quality) and also its state model and all

traceability information; in other words containing respectively a product future,

present and past. Therefore, there exist in PROSIS as many PH instances as

manufactured products or/and Work In Progress products. This is a major difference

with respect to PROSA in which the Product Holon acts as an information server to

the other Holons of the HMS, delivering technical information for a given type of

product but not containing products state information. By definition in manufacturing,

a product is a nomadic entity. It is thus necessary to identify it and, for that, to tag it.

Unit identification goes through the deployment of ad hoc technologies linking each

M_product to its I_product. A good example of these technologies is RFID with an ID

tag attached to the M_product carrying at least an identification number, eventually

completed with key information of the I_product. Other information stored in the

central database and accessible through a network can be associated to this nomadic

information.

The Resource Holon is conceptually similar to the definition proposed in PROSA:

it includes a material part, like automated equipment (NC machine tools, industrial

robot), making an M_resource, and an information processing part, the I_resource,

which drives the equipment and contributes to allocate tasks to the resources.

Resource allocation methods for the I_resource are not the same as those in PROSA

since interactions with the other types of Holons are defined in an isoarchical context.

Furthermore, RF identification is implemented only in the case of nomadic resources,

like mobile robots, shuttles, etc.

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An Order Holon represents a task in a manufacturing system: a manufacturing

order concerning in general a set of PH. It is responsible for the performance of

assigned work within specified times. It is thus closely linked to the concept of batch,

WIP and delays / lead time. This is a nomadic entity with a strong link with one (or

several) Product(s) Holon(s). The I_order checks dates satisfaction during work

performance and about the consideration of economic factors (batch size, WIP (Work

in Progress) volume, minimisation of production changes, batch partition, etc.). The

M_order will be, according to the case, the manufacturing order with an ID tag, or the

container also tagged, allowing the manipulation of one or several M_products.

4 RFID Functions in Ace

Self organisation of a set of Holons suggests that control decisions must be taken

locally. PROSIS naturally reflects the physical organisation of Holons: around each

M_resource are physically placed p M_products and k M_orders. These k M_orders

are related to the p products (k less or equal to p). Self organised decision making in

control requires the participation of all the locally implicated entities: resource, orders

and products.

For that, local and specific interactions will be established between the I_holons of

the (1+p+k) concerned Holons. These I_holons make a local ‘Flat Holonic Form’. It is

clear that these Holons do not all have the same objective: some trade-off should be

found to facilitate the emergence of a good control solution.

In order to manage this, an ACE (Ambient Control Entity) providing I_holons with

various ad hoc services is associated to each resource. The ACE also offers services to

the Holons making the WIP associated to this resource.

The first type of ambient service offered by an ACE is reception of the (1+p+k)

I_holons, made each of their information system (own data, reconstruction of the

current Holon state) and of an instantiation of their decisional system. An ACE

contains mechanisms allowing the management and the access to the information

related to these I_holons. Also, an ACE has a RFID coupler allowing managing the

information related to resource WIP according to input/output products.

Synchronisation between the I_holons and the M_holons ensures compliance between

the physical world and the information system. When a product or a products batch

arrives in the resource WIP (or leaves it), the corresponding ID tag is read and the

whole set (I_product, I_order) is updated (added to or taken out the ACE information

structure).

When an I_product or I_order is activated, its internal thread starts, triggered by

the RFID event (figure 1). This thread becomes then the decisional system of the

I_holon. It can for example decide to move to another resource if the current one

become too slow or if it is down. It is stopped and destroyed when the RFID coupler

indicates that the M_product or M_order has leaved the resource WIP.

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Fig. 1. Thread control via RFID events.

Our RFID hardware is composed of 13,56MHz couplers compatible with both ISO

14443 (all subtypes) and ISO 15693 cards, with a 4cm R/W range. We have chosen

4Kb memory ISO 14443-2 B chips with 106Kb/s transfer rate and anticollision

functions. They use Calypso technology.

Among ambient services also proposed by an ACE, we find visualisation through a

Human-Machine Interface (HMI), real time self organisation through mono- or multi-

criteria heuristics, real time indicator calculation, performance analysis, archiving or

traceability.

5 Conclusions

The future needs of the production systems lead to introduce more intelligence in the

core of these systems. The PROSIS model gives an answer by bringing decisional

intelligence to the products, resources and orders, via a holonic approach. To support

the holons, ACE are used. They propose ambient services to the holons, like hosting,

instantiation, or synchronization between I_holons and M_holons, using RFID

technology.

This allows the improvement of the real time control of the production system,

using multicriteria decision algorithms of AHP type.

References

1. Bongaerts, L., Monostori, L., McFarlane, D. and Kadar, B., 2000. Hierarchy in distributed

shop floor control. Computers In Industry, 43, pp.123-137.

2. Deen, S.M., 2003. Agent-Based Manufacturing – Advances in the Holonic Approach.

Springer-Verlag Ed, ISBN 3-540-44069-0.

3. Dobre D., Bajic E., 2008. Active product modeling for chemical security management

based on smart object concept. Proceeding of MOSIM'08 International Conference, Paris –

France

4. Djurdjanovic, D., Lee, J. and Ni, J., 2003. Watchdog Agent – An Infotronics Based

Prognostics Approach for Product Performance Assessment and Prediction. International

Journal of Advanced Engineering Informatics, 17 (3-4), pp.109-125.

105

5. Hribernik, K.A., Rabe, L., Thoben, K-D. and Schumacher, J., 2006. The product avatar as a

product-instance-centric information management concept, International Journal of

Product Lifecycle Management, 1 (4), pp.367–379.

6. Huang, G.Q., Zhang, Y.F. and Jiang, P.Y., 2007. RFID-based wireless manufacturing for

walking-worker assembly islands with fixed-position layouts. Robotics and Computer-

Integrated Manufacturing, 23 (4), pp.469-477.

7. Koestler, A., 1967. The ghost in the machine. London: Editions Hutchinson.

8. Mathews, J., 1995. Organizational foundations of intelligent manufacturing systems - the

holonic viewpoint. Computer Integrated Manufacturing Systems, 8 (4) pp.237-243.

9. Mattern, F. and Sturm, P., 2003. From Distributed Systems to Ubiquitous Computing – The

State of the Art, Trends, and Prospects of Future Networked Systems. In: Klaus Irmscher

and Klaus-Peter Fähnrich, eds. Proc. KIVS 2003, pp.3-25, Springer-Verlag

10. Mc Farlane, D., Sarma, S., Chirn, J.L., Wong, C.Y. and Ashton, K., 2003. Auto ID systems

and intelligent manufacturing control. Engineering Application of Artificial Intelligence, 16

(4), pp.365-376.

11. Mesarovic, M.D., Macko, D. and Takahara, J., 1970. Theory of Hierarchical Multilevel

Systems. New York: Academic Press.

12. Morel, G., Va1ckenaers, P., Faure, J.M., Pereira C., and Dietrich, C., 2007. Manufacturing

plan control challenges and issues. Control Engineering Practice, 15 (11), pp.1321-1331.

13. Ounnar, F., Pujo, P., 2009. Pull control for Job Shop: Holonic Manufacturing System

approach using multicriteria decision-making, Journal of Intelligent Manufacturing, DOI

10.1007/s10845-009-0288-4

14. Pujo, P. and Brun-Picard, D., 2002. Pilotage sans plan prévisionnel ni ordonnancement

préalable. In: P. Pujo and J.P. Kieffer, eds. Collection IC2 - Productique : Méthodes du

pilotage des systèmes de production. Paris: Hermès Science Europe Ltd.

15. Pujo, P., Broissin, N., and Ounnar, F., 2009. PROSIS: An isoarchic structure for HMS

control, Engineering Applications of Artificial Intelligence, 22 (7), pp.1034-1045

16. Saaty, T., 1980. The Analytic Hierarchy Process, Mc Hill.

17. Soroor, J., Tarokh, M.J. and Shemshadi, A., 2008. Initiating a state of the art system for

real-time supply chain coordination. European Journal of Operational Research,

DOI:10.1016/j.ejor.2008.03.008.

18. Thoben, K. D., Jagdev, H. and Eschenbaecher, J., 2001. Extended Products: evolving

traditional product concepts. Proceeding of the 7th International Conference on Concurrent

Enterprising, Engineering the Knowledge Economy through Co-operation. Bremen,

Germany, pp.429-439.

19. Udoka, S.J., 1991. Automated data capture techniques: A prerequisite for effective

integrated manufacturing systems. Computers & Industrial Engineering, 21 (1-4) pp.217-

221.

20. Van Brussel, H, Wyns, J., Valckenaers, P., Bongaerts, L. and Peeters, P., 1998. Reference

architecture for holonic manufacturing systems: PROSA. Computers in Industry, 37,

pp.255-274.

106