Implementation of a Holonic Control System in a

IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 38, NO. 5, SEPTEMBER 2008

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Implementation of a Holonic Control System in a Flexible Manufacturing System Paulo Leitão, Senior Member, IEEE, and Francisco J. Restivo, Member, IEEE

Abstract—Answering to the need to have innovative manufacturing control systems tailored to the current economical, technological, and customer trends, where dynamic and volatile environments prevail, adaptive holonic control architecture (ADACOR) aims to increase the agility and reconfigurability of the production system, contributing for the improvement of the enterprise competitiveness when it works in dynamic and volatile environments. The paper describes the implementation and experimental validation of an ADACOR-based holonic manufacturing control system in a real flexible manufacturing system, using multiagent systems technology. The results extracted from a set of experimental tests allowed to verify the correctness, applicability, and merits of the ADACOR concepts and also contribute to prove the applicability of multiagent systems in industrial environments. Index Terms—Holonic manufacturing, intelligent manufacturing control systems, multiagent systems, reconfigurable automation.

I. INTRODUCTION N THE current times, manufacturing companies have to face dynamic environments where economical, technological, and customer trends change rapidly. In fact, they are placed in a worldwide market demanding for products with high quality at lower costs, highly customized, and with short life cycles. Traditional manufacturing control systems are typically large monolithic and centralized applications, developed and adapted on a case-by-case basis, requiring an expensive and huge timeconsuming effort to implement, maintain, or reconfigure the control application, and do not cope efficiently with the current requirements imposed to manufacturing systems, namely in terms of flexibility, agility, and reconfigurability (see, among others, [1] and [2]). Since Charles Darwin’s theory of the evolution of species we know that species change over a long period of time, evolving to suit their environment, and that the species that survive to evolution and changes in the environment are not the strongest or the most intelligent, but those that are more responsive to change. Translating into the manufacturing domain, the companies better prepared to survive are those that better respond to emergent and volatile environments. A recent study elaborated by the European Commission [3] reinforces this idea, pointing out the

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Manuscript received October 9, 2007. This paper was recommended by Associate Editor R. Brennan. P. Leitão is with the Department of Electrical Engineering, Polytechnic Institute of Bragança, 5301-857 Bragança, Portugal (e-mail: [email protected]). F. J. Restivo is with the Faculty of Engineering, University of Porto, 4200465 Porto, Portugal, and also with the Institute for Development and Innovation in Technology, 4520-102 Santa Maria da Feira, Portugal (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TSMCC.2008.923881

need to have adaptive, digital, networked, and knowledge-based manufacturing processes. In these circumstances, the challenge faced by the manufacturing companies, to remain competitive, is to implement manufacturing control systems that exhibit innovative features, like agile response to the occurrence of disturbances and dynamic reconfiguration without stopping, reprogramming, or restarting the process, based on distributed and intelligent control approaches. Multiagent systems and holonic manufacturing systems (HMS) appear as suitable emergent paradigms to address this challenge. Multiagent systems suggest the definition of distributed control based on autonomous agents that account for the realization of efficient, flexible, and robust overall plant control, and consequently the disturbance handling component. In a similar way, HMS (http://hms.ifw.uni-hannover.de) translates into the manufacturing world the concepts developed by Arthur Koestler for living organisms and social organizations. Holonic manufacturing is characterized by holarchies of holons (i.e., autonomous and cooperative entities), which represent the entire range of manufacturing entities. A holon, as Koestler devised the term, is a part of a (manufacturing) system that may be made up of subordinate parts, and in turn, can be part of a larger whole. Several manufacturing control architectures using agentbased and HMS have been proposed in the literature [4]–[10]. One of these architectures is ADACOR (adaptive holonic control architecture for distributed manufacturing systems) [11] that addresses the agile response to change, increasing the agility and flexibility of manufacturing control systems, especially those located in volatile environments characterized by the frequent occurrence of disturbances. At the time, and in spite of the promising perspective of agent-based and HMS, significant proofs of the applicability and advantages of these emergent paradigms in real automation environments are still missing. In fact, only very few industrial implementations were reported, such as [1], that developed an agent-based production control system for a DaimlerChrysler factory plant, and [9] that developed an agent-based control system for the chilled water systems of the U.S. Navy ships. Some reasons can be pointed out, such as the required investment, distributed thinking, scalability, interoperability, methodologies, and technologies for systems development, and integration of physical automation devices [12], [13]. However, a crucial point is that an industry wants to use proven technology, and does not want to be the first to try it in their production processes [14]. This requires the maturity of the multiagent systems technology and proofs of its real applicability and merits.

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This paper intends to contribute to prove the applicability, correctness, and merits of ADACOR holonic control system in particular and holonic control systems in general. For this purpose, the paper describes the implementation of ADACOR concepts in a flexible manufacturing system and its experimental validation through the evaluation of ADACOR control system performance, both in terms of quantitative indicators directly related to production parameters (lead time, throughput, tardiness, resource utilization, and repeatability) and of qualitative indicators related to the dynamical behavior of the system (agility). The evaluation is based on the comparison of the ADACOR control system with other control approaches that present different degrees of heterarchy. The paper is organized as follows. Section II briefly describes the ADACOR holonic control architecture and Section III introduces the experimental case study used to evaluate the applicability and merits of ADACOR concepts. Section IV describes the implementation of ADACOR concepts using multiagent technology and Section V presents the ADACOR holonic control system working in practice. Section VI discusses the results obtained during the experimental tests, and finally, Section VII rounds up the paper with the conclusions.

Fig. 1.

ADACOR holonic control architecture.

Fig. 2.

Architecture of an ADACOR holon.

II. ADACOR HOLONIC CONTROL ARCHITECTURE The ADACOR architecture is based on the HMS paradigm, which is well suited to deal with manufacturing control problems in a distributed manner. The ADACOR is built upon a community of autonomous and cooperative entities, designated by holons, to support the distribution of skills and knowledge, and to improve the capability of adaptation to changing environments. Each holon is a representation of a manufacturing component that can be either a physical resource (numerical control machines, robots, conveyors, pallets, etc.) or a logic entity (products, orders, etc.). ADACOR defines four holon classes [15], product (PH), task (TH), operational (OH), and supervisor (SH), as illustrated in Fig. 1, according to their roles and functionalities. The product, task, and operational holons are quite similar to the product, order, and resource holons defined in ProductResource-Order-Staff Architecture (PROSA) reference architecture [4], while the supervisor holon presents characteristics not found in the PROSA staff holon, namely, the possibility to coordinate other supervisor holons in a federation architecture and the responsibility to manage the dynamic evolution of groups of holons according to the environment context. The product holons represent the products (and subproducts) available in the factory catalog, the task holon represents the production orders launched to the shop floor to execute the requested products, and the operational holons represent the physical resources available at shop floor. The supervisor holons provide coordination and optimization services to the holons under their supervision, introducing hierarchy in decentralized systems. The architecture of a generic ADACOR holon, illustrated in Fig. 2, comprises a logical control device (LCD) and a physical manufacturing resource, if it exists. The LCD device acts as an agent, being responsible for regulating all local manufacturing

activities. It is organized in three main components: communication (ComC), decision (DeC), and physical interface (PIC) components [11]. The communication component is responsible for the interholon interaction, supporting the sharing of local knowledge by distributed holons. The decision component regulates the holon’s behavior, namely performing the manufacturing control functions, such as the process planning, scheduling, and plan execution (which includes the dispatching, monitoring, and reaction to disturbances), and adapting to emergence (such as group formation or dynamic reorganization). The physical interface component is responsible for the intraholon interaction, providing mechanisms to integrate the manufacturing resources such as robots and machine tools. Having in mind improving the agility and reconfigurability of manufacturing control systems, maintaining the same levels of productivity, ADACOR architecture introduces an innovative adaptive production control approach [11] that intends to be as centralized as possible and as decentralized as necessary, i.e., balancing between two alternative states.

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˜ AND RESTIVO: IMPLEMENTATION OF A HOLONIC CONTROL SYSTEM LEITAO

1) A stationary state where the system control relies on supervisors and coordination levels to achieve global optimization of the production process. 2) A transient state triggered by the occurrence of disturbances and presenting a behavior quite similar to the heterarchical architectures in terms of agility and adaptability. The self-organization capability embedded in ADACOR holons is the key concept that allows balancing the control between these two states, reaching an adaptive control approach that combines the agile reaction to unpredictable disturbances with the global production optimization [15]. The self-organization mechanisms require local and global driving forces to support the adaptation. In ADACOR architecture, the local driving forces are the autonomy factor, which is a dynamic parameter that reflects the degree of autonomy of each holon, and the learning capability, which allows the dynamic evolution of the holon behavior [11]. The global self-organization of the system is achieved only if the distributed entities have stimulus that drive their local self-organization capabilities. In ADACOR, the global selforganization of the system is achieved through the interaction between local individual holons, propagating the emergence, and the need for reorganization, using propagation mechanisms inspired by ant behavior. The learning capabilities embedded in ADACOR holons support the dynamic evolution and reconfiguration of the organizational control structure, allowing that different control structures can be reached during the adaptive production control approach life cycle. ADACOR uses Petri nets to formally specify the ADACOR control system, simplifying the understanding and getting a comprehensive view of the system functionality, taking advantage of its powerful mathematical foundation. Namely, the specifications and functionalities of each ADACOR holon class are formally modeled, with special attention devoted to the behavior of each individual ADACOR component [15]. III. EXPERIMENTAL CASE STUDY An experimental case study has been used to validate the ADACOR control architecture concepts, aiming to verify their correctness and applicability, and also the merits of the proposed concepts. Cavalieri et al. [16] defines a benchmark framework for manufacturing control that proposes three design issues that must be properly specified to define the case study. The three axes are as follows [16]. 1) Production system: It provides a description of the structural features that describe the physical configuration of the production system and of the technological features that describe the process plans of the products available at the factory plant. 2) Manufacturing scenarios: These comprise the description of the plant, operational, and control scenarios. 3) Performance measures: They define the different indicators used to evaluate the performance of a single implementation.

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Fig. 3.

Plant layout of the case study production system.

The experimental case study used in this paper will be described according to these vectors. A. Production System The production system, illustrated in Fig. 3, is based on the flexible manufacturing system of the Computer Integrated Manufacturing (CIM) Center of Porto, extended with two virtual manufacturing cells (cells B and C that do not exist in the real platform), to provide the hardware/software redundancy and flexibility in accommodating alternative solutions at the production planning level. Otherwise, it would be completely impossible to take advantage of the agile and reconfigurable system control. The real flexible manufacturing platform is organized as a set of four physical cells: manufacturing cell, assembly cell, storage and transportation cell, and maintenance and setup cell. The manufacturing cell A has a turning machine Lealde TCN10 with a Siemens Sinumerik 880 T controller and a milling machine Kondia B500 with a Fanuc 16 MA numerical control. The load/unload of these machines is performed by an anthropomorphic robot Kuka IR163/30.1 with a Siemens RC3051 controller. The assembly cell is responsible for the assembly of the components to achieve the final products, using a four-axis selective compliance assembly robot arm (SCARA) robot Adept Three equipped with a charge-coupled device (CCD) camera from Pulnix associated to an artificial vision system Cognex 4200EX. The storage and transportation cell is responsible for the transportation of materials within the shop floor and for the temporary storage of materials. This cell has an automatic guided vehicle (AGV) EFAGV-200-2R-B that guarantees variable routing of the products flow and an automated storage/retrieval system (AS/RS) system, both from Efacec. The maintenance and setup cell is responsible for setup, maintenance, and recovery operations, calibration of tools and grippers, and palletizing and depalletizing of materials that circulate in the shop floor. This cell includes a tool calibration system AR2000 GA from Elbo


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Fig. 4.

Process plans for the product catalogue.

Controlli, a palletizing table, and several tools to support the maintenance operations. Each processing machine of the production system has an input/output buffer to decouple it from the transport system. A set of pallets are used to bring the material to be processed in the machine or the cell and take away the pieces produced. This experimental case study considers the production of four products, named the base, body, cover, and handle. When assembled, they can create two different final products: box and ashtray. The ashtray product comprises the assembly of the base and the body subproducts, and the box product comprises the assembly of all designed subproducts. Each product has a process plan constituted by nonpreemptable operations, as illustrated in Fig. 4. The flexibility in this manufacturing system is provided by the alternative routings plans according to the on-line factory capacity. The description of the cutting tools in the requirements of each operation uses an abbreviate notation in order to simplify their analysis. As an example, the tool tD is a roughing cutting tool that comprises a PCLNL2020K12 rigid clamp and a CNMG120404-QM415 insert. B. Manufacturing Scenarios The plant scenario describes the aspects related to the operation of the physical resources, such as setup and transportation times, resources availability, and disturbance models. In the experimental tests reported in this paper, the following are considered. 1) The execution of setups is not considered, it being assumed that each machine is equipped with a set of cutting tools that allows executing a range of operations. Thus, the turning machine of the area A is equipped with tA, tB, and tC tools, and the milling machine of the area A has tV and tH tools. The turning machine of the area B has tB, tC, and tD tools, and the milling machine of the area B has tX, tY, and tZ tools. At last, the turning machine of the area C has tB, tC, and tD tools, and the drilling machine has tK and tW tools. 2) The transport operations are performed by a single AGV and orders are queued by order of arrival. The execution of each transport operation takes five time units.

3) There are three different plant scenarios: a) the first plant scenario considers that no unexpected disturbance will occur; b) the second plant scenario considers the occurrence of failures in the turning machine of cell B, with a probability of 25%, and that in case of failure the part is destroyed (which means that it is necessary to restart the execution of the part) and the machine is down during 60 s for the recovery procedures; and c) the third plant scenario considers the occurrence of failures in the turning machines of cells B and C, with the same disturbance model of the previous scenario. The operational scenario defines the order mix and plant load. Each individual book of orders comprises the production of six production orders: two bodies, two bases, one handle, and one cover, involving 17 operations (see Fig. 4). The experimental test reported in this paper has considered a plant load of three books of orders, i.e., 18 production orders (51 operations), to obtain a deeper analysis of the control systems performance. Each operational scenario considers that all the production orders belonging to the same book of orders arrive to the production system at the same time, but different books of orders arrive sequentially to the production system.

C. Performance Measures The evaluation of the control system is performed by analyzing some quantitative indicators that are based on different production performance measures, such as the lead time (i.e., the total time required to process a given product through the factory plant), the tardiness (i.e., the difference between the order completion date and the due date when this difference is positive), the throughput (i.e., the ratio between the number of parts produced in the experience and the batch time necessary to execute the experience), and the repeatability (i.e., the mean value of the standard deviation of the percentage of utilization of all resources of the system over the several experiences). The resource utilization, which is system behavior indicator and reflects the percentage of processing time during a time interval, is also evaluated. A single qualitative indicator, the agility, is also analyzed, which is of a more subjective nature and reflects the dynamic response of the manufacturing system. The agility of a control system can be defined as the capability to react in a short period of time to the occurrence of unexpected disturbances. The method used to determine the agility is to analyze the loss of productivity in presence of disturbances, which indirectly reflects how agile the system is: the smaller the loss of productivity value, the higher the agility of the system will be. The method provides a good relative indicator about the agility, which is not a time-based value but rather a simple percentage, avoiding the complexity of measuring periodically a performance indicator. The loss of productivity is given by the percentage of reduction of the throughput of the system running under a disturbance scenario in relation to the throughput of the system running in a scenario with no disturbances.



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IV. IMPLEMENTATION OF THE ADACOR CONTROL SYSTEM The development of the ADACOR holonic control application for the described case study requires the implementation of ADACOR holon classes, followed by the holonification of the manufacturing components, i.e., the identification and parameterization of the operational, product, task, and supervisor holons from the case study production system. A. Implementation of ADACOR Components The ADACOR control system prototype was implemented using multiagent systems technology, to take advantage of the modularity, flexibility, decentralization, and reusability inherent to the multiagent approach. The Java agent development framework (JADE) (see http://jade.cselt.it) was chosen from the set of commercial and academic agent development platforms currently available to support the development of multiagent systems, since JADE provides a set of system services and agents in compliance with the Foundation of Intelligent Physical Agents (FIPA) specifications [17], such as naming service and yellow-page service, message transport and parsing service, and a library of FIPA interaction protocols ready to be used. An interesting comparative study of the available agent development platforms is presented in [18]. The logical (information processing) part of an ADACOR holon is an agent, basically implemented by a Java class that extends the Agent class provided by the JADE framework, inheriting basic functionalities (e.g., registration services and mechanisms for sending/receiving messages), and extending them with features that represent the specific behavior of each ADACOR holon. Its structure is based on the JADE “behavior” model, where the tasks are implemented as “behavior” objects [19], each one mapped in a Java class. For example, the Wait4MessagesBehaviour behavior, embodied in all ADACOR holon classes, is a Java class that is waiting for the arrival of messages, using the blockingReceive() method to block the behavior until a message arrives. The arrival of a message will trigger a new behavior (i.e., the HandleReceiveMessage behavior) to handle the message. The communication between autonomous holons is done via message passing over the network. The messages are encoded using the FIPA agent communication language (ACL) [20] and their content is formatted according to the FIPA-SL0 language, both specified by FIPA [17]. Holons may register their functionalities in form of services, so they can be discovered by other holons. For this purpose, JADE offers a directory facilitator (DF) agent, specified by FIPA, which acts in a similar way to the yellow pages. In distributed and heterogeneous environments, the communication between autonomous agents requires a common understanding of the concepts of their knowledge domain, i.e., the usage of a proper ontology. An ontology defines the vocabulary that will be used in the communication between agents, and the knowledge relating to these terms. This knowledge includes the definition of the concepts and the relationships between these concepts. The meaning of the message content is captured in the message ontology. The developed ontology was designed using

Fig. 5.

Decision component.

the Protégé tool (see http://protege.stanford.edu) [21] and translated into Java classes using the OntologyBeanGenerator plug-in, according to the JADE guidelines that follow the FIPA ontology service recommendations specifications [17]. The decision component embedded in an ADACOR holon, illustrated in Fig. 5, includes a rule-based system, which uses declarative knowledge expressed in terms of rules, to regulate the holon’s behavior [22]. For this purpose, the Java Expert System Shell (JESS) tool, which is a rule-oriented programming infrastructure based in the C Language Integrated Production System (CLIPS) language [23], is used. Each ADACOR holon class has a clp file containing its knowledge base, comprising the definition of the domain declaration and the behavioral rules, and which is dependent on its type, objectives, skills, and behavior. The decision mechanisms that are common to all the ADACOR holons classes, such as the active notification, are placed in a special common clp file. The decision component also uses procedural knowledge to represent the holon’s knowledge and behavior. This type of knowledge is embodied in procedures, mapped in JADE’s behaviors, that are triggered as actions by some rules, each one being responsible for the execution of a particular set of actions. The scheduling algorithm is an example of this type of knowledge representation. Scheduling is not a central issue in ADACOR control architecture and different scheduling strategies can be found, even within the same implementation. As the ADACOR architecture is built upon functional blocks, as Lego pieces, the scheduling performance can be improved easily by plugging more powerful scheduling algorithms. In the prototype, the scheduling mechanism embedded within the supervisor holon deals with the multiple machines and multiple jobs problem, and uses a simple algorithm that guarantees a rapid and reliable scheduling. In a similar way, for each operational holon, a scheduling engine was developed that addresses the scheduling of multiple jobs to a single machine, based on scheduling heuristics such as earliest due date (EDD) and shortest processing time (SPT). The features and capabilities of each ADACOR holon are parameterized using an eXtensible Markup Language (XML) based configuration file, which is loaded during the holon startup. Each type of ADACOR holon requires a different type of configuration data, e.g., the product holon requires the introduction of the product data model and the process plan associated to


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the product, and the operational holon requires the introduction of attributes that reflect, among others, the resource type, the list of skills that the resource possesses, and its location.

TABLE I LIST OF ATTRIBUTES OF EACH OPERATIONAL HOLON

B. Identification and Parameterization of ADACOR Components The first step in the holonification of ADACOR components is to find the set of resources available at the shop floor. Analyzing the description of the production system case study, the following resource elements were found: h1 (human operator), t1 (AGV device), t2 (AS/RS system), toi (several tools available in the factory), m1 (assembly robot), a1 (CCD camera), pm1 (turning machine of area A), pm2 (milling machine of area A), m2 (handling robot of area A), pm3 (turning machine of area B), pm4 (milling machine of area B), m3 (handling robot of area B), pm5 (turning machine of area C), pm6 (drilling machine of area C), and ai (several buffers available in the factory plant). Analyzing the physical dependencies between the identified objects, it is possible to verify the following. 1) The CCD camera is dependent on the assembly robot, it being possible to aggregate both resources. 2) The tools are permanently stored in the machines and they are not shared, allowing aggregating each tool to the appropriate machine. 3) The existing buffers should not be considered in the final set of resources, since it is assumed that each buffer serves only one machine. After the analysis of the physical dependencies, the following set of aggregated manufacturing components is achieved: {h1, t1, t2, m1, pm1, pm2, m2, pm3, pm4, m3, pm5, and pm6}. Each element of this set is mapped into an operational holon, parameterized by means of a XML-based file that contains the description of its main attributes. Table I illustrates the main attributes of each operational holon, focusing on its type and set of tools stored in its tool magazine. Each identified operational holon represents a physical automation resource, requiring the development of wrapper interfaces, each one integrating the resource within the holon. Since ADACOR uses the virtual resource concept, inspired in the Virtual Manufacturing Device (VMD) concept from Manufacturing Message Specification (MMS) protocol [22], it is necessary to develop a virtual resource for each manufacturing device. The resource encompasses the implementation of the services at the server side (the real automation resource), which are invoked by the client side (the logical part of the operational holon). The client ignores the details of this implementation and each virtual resource can be reused by other similar resources or holonic control applications. Leitão [13] describes the implementation of two different virtual resources to integrate two different automation resources, one for a PLC and another one for an industrial robot, using Common Object Request Broker Architecture (CORBA) to support the client–server interaction. These two virtual resources implement the same services in a different way according to the particularities of each resource.

The available products at the factory plant are mapped into product holons. According to the description of the case study, six product holons are identified: box-Ho, ashtray-Ho, bodyHo, base-Ho, handle-Ho, and cover-Ho. For each one of these product holons, a configuration XML-based file is elaborated, containing the information related to the product data model and the process plan. The identification of supervisor holons can be done by analyzing the description of the hierarchical levels presented in the control structure. In this case, only one supervisor holon, named FactoryControl-Ho, for the shop floor control is considered. The organizational structure of the factory plant is described in an XML-based file that is loaded and interpreted by the supervisor holon during its startup. The scheduling algorithms embedded in the supervisor and operational holons are tuned to minimize the lead time by using a SPT heuristic. V. HOLONIC CONTROL SYSTEM IN OPERATION The developed ADACOR-based control system for the described flexible manufacturing system is illustrated in Fig. 6. The set of holons presented in the system, in a total of 37 agents, are distributed by several different personal computers, running in different platforms, such as Windows XP, Windows 2000, and Linux. This demonstrates that ADACOR control system supports the heterogeneity often presented in industrial automation scenarios. Fig. 6 also illustrates the graphical user interfaces of the operational holon, the supervisor holon, the factory plant supervisor agent, which monitors, in an integrated way, the production activities in the factory plant, and the product manager agent, which allows introducing new products in the system and launching batches of production orders to the factory plant. The factory plant supervisor agent and the product manager agent are auxiliary tools that were developed to support the



Fig. 6.

ADACOR control system prototype.

configuration, operation, and debugging of the manufacturing control application. The experience gained during the prototype implementation and debugging showed that the use of agent technology to implement manufacturing control systems made the software necessary to develop the application simpler to write, debug, and maintain, due to the smaller size of each distributed component. Comparing with the traditional control approaches, the use of multiagent systems simplifies the expansibility and reconfiguration of the control system. The real operation of the control system prototype, under different test scenarios, proved the correctness and applicability of the ADACOR control system, since it was showed that it works as specified, either in normal operation or in presence of disturbances. Namely, the adaptability of the ADACOR control system was verified, since the system reacted correctly to the introduction of a new component (e.g., a new machine, product, or supervisor), removal or breakdown of a centralized component (e.g., a supervisor or central scheduler), removal or breakdown of a local component (e.g., a resource or an order), and modification of manufacturing components (e.g., new scheduling or learning algorithms). Especially, it was shown that when a supervisor or operational holon breaks down or leaves the system, the other holons continue their way to find alternative solutions to execute the production plan. Additionally, the adaptive production control mechanism was also proven once the several distributed entities reorganize themselves into different control structures during the system life cycle according to the occurrence of disturbances, as illustrated in the collection of screenshots of Fig. 7 that shows the ADACOR adaptive production control mechanism working in practice. In fact, in the startup, and since the system is running in a predictable way, the holons are organized in a hierarchical structure, with a supervisor holon coordinating the several operational holons of the production system. In this scenario, task holons interact with supervisor holons to announce operations. The supervisor holons elaborate, periodically, optimized schedules for the coordinated resources, decompose them into indi-

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vidual operations, and propose them to the operational holons. Operational holons take these proposed schedules as an advice, having autonomy to accept or reject them, according to their knowledge and status. In the presence of an unexpected disturbance, in the case of Fig. 7 a failure in the turning machine of area B, the system reorganizes itself in a heterarchical-like control structure, running without the presence of coordination levels and allowing the agile reaction to disturbances (as illustrated in Fig. 7, operational holons deregister from the supervisor holon and register in the system top level). The reorganization is made possible by the self-organization capabilities of each holon, and implies an increase of its level of autonomy and the propagation of the disturbance to the neighbor holons using ant-based techniques [15]. During the abnormal situation, the scheduling is distributed by the holons in the system, requiring high degree of interaction between the entities that have tasks to be executed and entities that have skills and resources to execute them. Each operational holon is responsible for its own schedule, which is built dynamically from its local knowledge. The global (distributed) scheduling mechanism is achieved by the interaction between the operational holons and the task holons, using a task allocation interaction mechanism based on a multiround Contract Net Protocol (CNP) [24]. The system returns to the stationary state after the disturbance recovery, i.e., reorganizes again in a hierarchical control structure. The supervisor holon returns to its function by optimizing the schedule achieved during the transient state. It is also possible that the system evolves itself to a new normal situation, according to the learning capabilities embedded in each holon and as a result of the previous history of abnormal events. VI. EXPERIMENTAL RESULTS In order to evaluate the merits of the proposed ADACOR control approach, its performance was compared with the performance of two other control approaches, a centralized control approach and a completely heterarchical control approach, that are the two radicals in terms of control structures. Having in mind to guarantee a fair comparison, all of the three referred control approaches were implemented in the developed prototype platform. 1) In the traditional centralized control approach, the system is organized in a hierarchical control structure, using the supervisor holon as the shop floor controller. 2) In the heterarchical-like control approach, the holons run on a completely decentralized control structure, without the presence of supervisor holons, similar to that presented in [7]. In this approach, the task and operational holons interact directly for every (re)scheduling decision. 3) On the other hand, in the ADACOR control approach, the holons are organized in a hierarchical control structure, using the supervisor holon as the shop floor controller, but enabling the self-organization capability of the operational holons to support the agile reorganization of the control structure in case of emergency.


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Fig. 7.

ADACOR adaptive production control mechanism working in practice.

This means that the results obtained from the experimental tests will be consequence of the merits of the control system approach and not derived from the software development quality, scheduling strategies, or decision techniques. A. Analysis of Quantitative Indicators The first experiment scenario uses the operational scenario of 18 production orders and considers that no unexpected disturbance occurs. In this scenario, the ADACOR control system runs, during its life cycle, in the stable state, the holons being organized in a hierarchical structure. Thus, it presents the same behavior as the hierarchical-like control approach. The results obtained from this experimental test, summarized in Fig. 8, show that, in this kind of scenario, the hierarchical-like

and ADACOR control approaches present smaller values of lead time and higher values of the throughput than the heterarchicallike control approach, reflecting a better production optimization. This is justified by the presence of a centralized entity, i.e., a supervisor holon that has a global view of the system and elaborates optimized production plans. In spite of the scheduling algorithms being tuned to minimize the lead time, the hierarchicallike and ADACOR control approaches present lower values for the tardiness indicator, which also confirms the better production optimization presented by those control approaches. By analyzing the repeatability of the production planning, it is clear that the hierarchical-like and ADACOR control approaches present better predictability than in the heterarchical-like control approach. Indeed, in the heterarchical-like control approach, the



Fig. 8.

Fig. 9.

Performance indicators for a scenario with no disturbances.

Performance indicators for the first disturbance model.

global schedule is achieved by the interaction of operational and task holons that have just a partial view of the entire system, not guaranteeing the repeatability of the production plans over the experimental tests. The analysis of the resource utilization reinforces the idea that hierarchical-like and ADACOR control approaches present better production optimization than the heterarchical-like control approach, since they present higher percentage of resource utilization than the heterarchical-like control approach. The analysis of the standard deviation of the percentage of resource utilization allows verifying that the heterarchical-like control approach presents, as expected, the worst behavior in the load distribution variability. The second experimental scenario uses the first disturbance model described in Section III-B, which considers the occurrence of unexpected disturbances in the turning machine of cell B. The results obtained in this experimental test, summarized in Fig. 9, show, as expected, a degradation of all performance indicators for all the control approaches in the presence of disturbances. The analysis of the lead time and throughput indicators shows that the ADACOR control approach presents better response to disturbance, illustrated by smaller values of lead time and tardiness and higher value of throughput, than do the hierarchicallike and heterarchical-like control approaches. The presence of disturbances strongly affects the hierarchical-like control approach when compared with the heterarchical-like approach. Nevertheless, the difference between the two control approaches is significantly reduced, especially in terms of the throughput parameter. As expected, the occurrence of disturbances increases the unpredictability of the control system, as illustrated by the higher values of repeatability parameter presented by all evaluated con-

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Fig. 10.

Performance indicators for the second disturbance model.

trol approaches. Also, the differences between the predictability exhibited by the several evaluated control approaches are reduced in this experimental scenario, the ADACOR control approach being the one that presents better values. The analysis of the resource utilization confirms that the ADACOR system presents the better performance, with higher values of resource utilization than the other control approaches. Comparatively to the stable scenario, the ADACOR control approach presents values for the standard deviation that are even smaller than those presented by the hierarchical-like control approach. An interesting observation is related to the higher values of the percentage of resource utilization obtained in disturbance scenarios in relation to the ones obtained in the stable scenario. This is explained by the need to execute additional operations due to the occurrence of machine failures that destroyed the part. The third experimental scenario considers a more severe disturbance model to compare the response of the three control approaches to different levels of entropy. It is assumed that failures can occur in turning machines of cells B and C, as described in Section III-B. The results obtained in this experimental test, illustrated in Fig. 10, confirm the observations done for the first disturbance model, it being clear that the performance of each control approach is degraded with the increase of entropy associated to the disturbance model. However, ADACOR is the control approach that presents a better performance, supported by the better results in terms of lead time, throughput, tardiness, repeatability, and resource utilization. B. Qualitative Indicators The dynamic behavior of the ADACOR and the other two control approaches is also evaluated by analyzing a single qualitative performance parameter, the agility. The results obtained comparing the loss of productivity of the evaluated control approaches for the two disturbance models are illustrated in Fig. 11. The results show that the ADACOR control approach presents similar values of loss of productivity to those exhibited by the heterarchical-like control approach. On the other hand, and as expected, the hierarchical-like control approach presents the higher loss of productivity. As the agility is inversely proportional to the loss of productivity, it is possible to conclude that the


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Fig. 11.

Loss of productivity for the evaluated control approaches.

ADACOR control approach exhibits the same levels of agility of those of the heterarchical-like control approach. In more severe disturbance scenarios, the levels of agility presented by the several control approaches are reduced, with special incidence to the hierarchical-like control approach, which is significantly affected by the occurrence of disturbances. This allows to conclude that the more drastic the disturbance model is, the more significant the performance of ADACOR and heterarchical-like control approaches in terms of agility will be. Analyzing simultaneously the qualitative and quantitative indicators, the experimental results reveal that the ADACOR approach presents promising performance indications, and confirm that the ADACOR control system combines the hierarchical and heterarchical best features, presenting similar values of agility to the heterarchical approach, but better production optimization. In other words, the ADACOR architecture is adequate for control systems satisfying the requirement of dealing with important disturbances during a long period of time, as is the case of industries competing in modern dynamic environments, where decentralized decision-making may have clear advantages over centralized realizations. Finally, it was also clear that, in unpredictable scenarios, the heterarchical control approach presents better performance than the traditional centralized control approach, it being more visible as the more severe, in terms of disturbance model, is the scenario. VII. CONCLUSION ADACOR is a holonic manufacturing control architecture designed to improve the agility and reconfigurability of production systems by introducing an adaptive production control approach, which evolves dynamically through different control structures, supported by the self-organization capabilities of individual ADACOR entities. A crucial point to prove the applicability and merits of control architectures is related to its implementation and operation. Bearing this idea in mind, this paper describes the implementation of ADACOR concepts for a real flexible manufacturing system using multiagent technology, namely the JADE agent development framework. The correctness and applicability of the proposed architecture were experimentally tested through the observation of its operation, which allowed concluding that the ADACOR concepts are sound and ready to be used in real

situations. This is a crucial step to prove the applicability and merits of ADACOR control architecture in particular and the agent-based control approaches in general. Additionally, the behavior of the ADACOR control approach was evaluated and compared with other control approaches, which presents different degrees of heterarchy and agility. Three scenarios were devised, with different levels of entropy, using the same book of orders, to evaluate the static and dynamic performance of the control architectures. The overall evaluation showed that, in fact, ADACOR combines the best features of hierarchical and heterarchical control approaches, simultaneously combining the good production optimization with the agile reaction to disturbances. The complete evaluation and comparison of control systems performance requires the definition of benchmark frameworks that define benchmark scenarios and normalized performance indicators, decoupling the control system performance from the particular implementation of the architecture concepts and the production system scenarios. REFERENCES [1] S. Bussmann, N. Jennings, and M. Wooldridge, Multiagent Systems for Manufacturing Control. New York: Springer-Verlag, 2004. [2] R. Brennan and D. Norrie, “From FMS to HMS,” in Advances in the Holonic Approach to Agent-Based Manufacturing, S. M. Deen, Ed. New York: Springer-Verlag, 2003, pp. 31–49. [3] Manufuture, “Manufuture, a vision for 2020, assuring the future of manufacturing in Europe,” High-Level Group, Eur. Comm., Brussels, Belgium, Rep., 2004. Available: http://www.manufuture.org/documents/ manufuture_vision_en%5B1%5D.pdf [4] H. Van Brussel, J. Wyns, P. Valckenaers, L. Bongaerts, and P. Peeters, “Reference architecture for holonic manufacturing systems: PROSA,” Comput. Ind., vol. 37, pp. 255–274, 1998. [5] F. Maturana and D. Norrie, “Multi-agent mediator architecture for distributed manufacturing,” J. Intell. Manuf., vol. 7, pp. 257–270, 1996. [6] J.-L. Chirn and D. McFarlane, “A holonic component-based approach to reconfigurable manufacturing control architecture,” in Proc. Int. Workshop Ind. Appl. Holonic Multi-Agent Syst., 2000, pp. 219–223. [7] J. Solberg and G. Lin, “Integrated shop floor control using autonomous agents,” IIE Trans., vol. 24, no. 3, pp. 57–71, 1992. [8] M. Pechoucek, M. Rehák, P. Charvát, T. Vlcek, and M. Kolár, “Agentbased approach to mass-oriented production planning: Case study,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 37, no. 3, pp. 386–395, May 2007. [9] P. Tichy, P. Slechta, R. J. Staron, F. Maturana, and K. H. Hall, “Multiagent technology for fault tolerance and flexible control,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 36, no. 5, pp. 700–705, Sep. 2005. [10] W. Shen, S. Lang, and L. Lang, “iShopfloor: An Internet-enabled agentbased intelligent shop floor,” IEEE Trans. Syst., Man Cybern. C, Appl. Rev., vol. 35, no. 3, pp. 371–381, Aug. 2005. [11] P. Leitão and F. Restivo, “ADACOR: A holonic architecture for agile and adaptive manufacturing control,” Comput. Ind., vol. 57, no. 2, pp. 121– 130, 2006. [12] V. Marik and D. McFarlane, “Industrial adoption of agent-based technologies,” IEEE Intell. Syst., vol. 20, no. 1, pp. 27–35, Jan./Feb. 2005. [13] P. Leitão, “An agile and adaptive holonic architecture for manufacturing control,” Ph.D. dissertation, Univ. Porto, Porto, Portugal, 2004. [14] K. Hall, R. Staron, and P. Vrba, “Experience with holonic and agent-based control systems,” in Holonic and Multi-Agent Systems for Manufacturing (Lecture Notes in Artificial Intelligence 3593), V. Mar´ık, R. Brennan, and M. Pechoucek, Eds. Heidelberg, Germany: Springer-Verlag, 2005, pp. 1–10. [15] P. Leitão, A. W. Colombo, and F. Restivo, “ADACOR, a collaborative production automation and control architecture,” IEEE Intell. Syst., vol. 20, no. 1, pp. 58–61, Jan./Feb. 2005. [16] S. Cavalieri, L. Bongaerts, M. Taisch, M. Macchi, and J. Wyns, “A benchmark framework for manufacturing control,” presented at the 2nd Int. Workshop Intell. Manuf. Syst., Leuven, Belgium, 1999.



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Francisco J. Restivo (M’72–M’81) received the D.Phil. degree in electrical and computer engineering from the University of Sussex, Brighton, U.K., in 1981. Since 1988, he has been with the Department of Informatics Engineering, School of Engineering, University of Porto, Porto, Portugal, where he is currently an Associate Professor. Since 1999, he has also been a Scientific Director and a Member of the Board of the Institute for Development and Innovation in Technology, Santa Maria da Feira, Portugal. His current research interests include digital signal processing, intelligent production systems, complexity management, and e-learning. Dr. Restivo was the recipient of the IEEE Third Millennium Medal in 2000.

Paulo Leitão (M’99–SM’08) received the M.Sc. and Ph.D. degrees in electrical and computer engineering from the University of Porto, Porto, Portugal, in 1997 and 2004, respectively. During 1993–1999, he was with the CIM (Computer Integrated Manufacturing) Center of Porto. During 1999–2000, he was with the Institute for Development and Innovation in Technology (IDIT), Santa Maria da Feira, Portugal. Since 1995, he has been with the Polytechnic Institute of Bragança, Bragança, Portugal, where he is currently a Coordinator Professor in the Department of Electrical Engineering. His current research interests include intelligent production systems, agent-based and holonic control, and reconfigurable factory automation. Dr. Leitão is a senior member of the IEEE Industrial Electronics Society and the Systems, Man, and Cybernetics Societies.
