Elsevier

Computers in Industry

Volume 46, Issue 3, October 2001, Pages 315-331
Computers in Industry

An agent architecture for manufacturing control: manAge

https://doi.org/10.1016/S0166-3615(01)00130-0Get rights and content

Abstract

In this paper we introduce agent concepts to implement control algorithms and application architectures for flexible control in manufacturing. We list requirements for the implementation of the agent itself, assess existing concepts of agent architectures, and give a more detailed overview about the building blocks of an adequate agent system architecture by describing the “manAge” agent system implementation.

Introduction

There is a variety of manufacturing control paradigms, which are investigating solutions for systems of high flexibility, from the point of view of operating the system (systems operation aspects) as well as maintaining the system structures and configurations (systems engineering aspects). According to Dilts et al. [1] flexibility is an essential property of systems, which follow the concept of distributed or partly distributed architectures. From this point of view it is understandable that agent technologies are adopted to design and implement flexible systems in manufacturing. Agents provide the structural and operational flexibility as a built-in property. In this paper we therefore describe a specific agent architecture, “manAge”, which provides generic specifications and structures for agents in manufacturing applications. As the “manAge” concept is intended to be used in real-world environments, we are considering reliability as an important aspect. For this we include persistent data management facilities into the agent architecture, which allows an agent to handle information and knowledge in a persistent way. This should enable agent-based control applications to recover from fatal system failures.

Guilfoyle and Warner [2] list the following properties, which comprise an agent architecture and must be tackled in a development process.

  • Agent organisation (span of control, roles, chain of commands);

  • Agent roles (different roles during problem solving, e.g. managers, workers, disputees);

  • Agent coupling (balance between interacting and problem solving), e.g. loosely coupled (low interaction, high problem solving), tightly coupled (high interaction, low problem solving);

  • Agent communication (organisation, common languages), e.g. low level protocols (send, receive), high level protocols (contract net, speech acts);

  • Agent interaction (how effect to each others, change common environment), e.g. co-operation, competition, hostility;

  • Agent complexity/sophistication (problem solving ability), e.g. coarse grained (few, clever agents), fine grained (many simple agents), cognitive (model of itself and others), non-cognitive (no internal model).

We use agents in application environments, where agents have to control and (at least partly) plan their local operations, cope with uncertainty by reacting to unforeseen events (disturbances), and have to recover from such situations without disturbing global goals like throughput maximisation. Planning is here considered to cover all the calculations to prepare a control strategy before execution. We therefore add additional requirements for agent architectures to those above. We distinguish between requirements, which are related to the agent system and those, which are purely agent related. In terms of agent-system related requirements we identify the following aspects.

  • Different categories of agents, specialised in ways characteristic to the categories;

  • Knowledge or access to knowledge on types of agents and availability of them to the other agents;

  • Communication of manufacturing knowledge, e.g. product knowledge (Bill of Materials) and production knowledge (process plan);

  • Communication of process knowledge (resource eligibility, production plans/programs, etc.) between agents;

  • Communication of process execution knowledge between agents (goals, state and load level of resources, etc.).

As agent-related aspects we list the following:

  • Planning/decomposition of global goals (orders) to local goals (local orders) within the resources (accept changes);

  • Management of local goals (orders), plans (production programs) and state information (load levels);

  • Connection to physical equipment within resource agents;

  • Detect disturbances by observation of abnormalities on-line (accept external events or events from the physical resource);

  • Manage the plan execution by receiving/accepting process execution events from other agents and equipment.

Section snippets

ManAge agent system

The agent system is a

Agent system implementation

The manAge allows the distribution of agent

Conclusions

This paper outlines an agent architecture for distributed manufacturing control applications. Conceptual and implementation aspects are clearly separated by interfaces and as such, openness of the system concerning scalability and extensibility is satisfied. To improve deployment, also mapping to implementation details (language constructs, operating system services, persistent data stores, etc.) is explicitly described. The architecture has been tested within the MASCADA Esprit LTR project.

Acknowledgements

This work has been funded by the European Commission and VTT Automation, as part of the MASCADA project (ESPRIT LTR Project 22728), which is greatly acknowledged by the authors. The authors would also like to thank all partners in the MASCADA project for fruitful discussions and co-operation.

Tapio Heikkilä received the degrees of M.Sc. in control engineering, Licentiate of Technology in systems and control engineering, and Doctor of Technology in computer engineering from the University of Oulu, Finland in 1983, 1986 and 1991, respectively. Currently he works as a technology manager at NetHawk Solutions Oy, Oulu, Finland, and holds also a position of docent of systems engineering, at University of Oulu. His research interest include intelligent systems, multi-agent systems and SW

References (18)

  • Y. Shoham

    Agent-oriented programming

    Artificial Intelligence

    (1993)
  • D.M. Dilts, N.P. Boyd, H.H. Whorms, The evolution of control architectures for automated manufacturing systems, Journal...
  • C. Guilfoyle, E. Warner, Intelligent Agents: The New Revolution in Software, Ovum Ltd., London,...
  • A.S. Rao, M.P. Georgeff, BDI Agents: From Theory to Practice, ICMAS-95, San Francisco,...
  • T. Heikkilä et al.

    PEM-modelling: a framework for designing intelligent robot control

    Journal of Robotics and Mechatronics

    (1992)
  • L. Rannanjärvi, T. Heikkilä, Software development for holonic manufacturing systems, Computers in Industry (37) (1998)...
  • M.P. Georgeff, A.L. Lansky, Procedural knowledge, Proc. IEEE, Special Issue on Knowledge Representation 74 (1986)...
  • M. Wooldridge, N.R. Jennings, Agent Theories, Architecture and Languages: A Survey. Lecture Notes in Artificial...
  • H.S. Nwana, Software agents: an overview, Knowledge Engineering Review 11 (3)...
There are more references available in the full text version of this article.

Cited by (31)

  • Virtual approach to holonic control of the tyre-manufacturing system

    2014, Journal of Manufacturing Systems
    Citation Excerpt :

    Communication and cooperation among agents help to solve complex problems on the system level which exceed the capacity of a single agent. In the field of agent research, many studies have been performed which focus on design of agent systems and methodology of agent system implementation into manufacturing systems [9–13]. Bussmann and Schild [9] implemented agent-based approach to the control of flexible production system within DimlerChrysler.

  • IMAQCS: Design and implementation of an intelligent multi-agent system for monitoring and controlling quality of cement production processes

    2013, Computers in Industry
    Citation Excerpt :

    A review of all related work to agent-based systems in manufacturing is provided in [20]. In addition, some other researchers have proposed different models of MAS and deployed them in manufacturing [18,21–24]. Finally, Behdani et al. in [25] proposed an agent-based system for modeling a complex network of a chemical manufacturing enterprise which can capture the interactions among the various constituents including the plants, functional departments, and external entities.

  • Design and implementation of a process-oriented intelligent collaborative product design system

    2004, Computers in Industry
    Citation Excerpt :

    After the Shaft2 Agent and the designer resolve design changes, changed interface parameters are transmitted to the affected Task Agents. Up to now, many multi-agent systems have been developed to fulfil different requirements of different applications [12–14,31,37]. The reasoning of these agents is almost implemented using a set of operational functional components [24].

View all citing articles on Scopus

Tapio Heikkilä received the degrees of M.Sc. in control engineering, Licentiate of Technology in systems and control engineering, and Doctor of Technology in computer engineering from the University of Oulu, Finland in 1983, 1986 and 1991, respectively. Currently he works as a technology manager at NetHawk Solutions Oy, Oulu, Finland, and holds also a position of docent of systems engineering, at University of Oulu. His research interest include intelligent systems, multi-agent systems and SW testing technologies.

Martin Kollingbaum received the degree of Diplom Ingenieur in Computer Science from the Vienna University of Technology in 1990. From 1990 till 1994 he worked as a software engineer in industry. From 1994 till 1996 he was a research assistant at the Vienna University of Technology, in 1997 he worked as a research scholar at the University of South Carolina, and from 1997 as a research associate at the University of Cambridge.

Paul Valckenaers received the applied mathematics engineering degree in 1983, the computer science engineering degree in 1985, and the mechanical engineering PhD degree in 1993, all from the Katholieke Universiteit Leuven, Belgium. Since 1986, he is with the mechanical engineering department, division PMA, of the Katholieke Universiteit Leuven. His main research interests are in programming, scheduling and control of flexible production systems and design theory for the development of flexible and complex production systems. His current research activities are widening the focus toward multi-agent coordination and control as a technolgy domain by itself.

Geert-Jan Bluemink received the degree of Master of Science in Mechanical Engineering with a specialisation in Production and Operations Management from the University of Twente, Enschede, the Netherlands in 1998. After graduation he was employed as a researcher by the VTT Automation, Oulu, Finland. Since the beginning of 2000 he works as a logistics consultant at B-SIM B.V., Enschede, the Netherlands.

1

Present address: B-SIM B.V., Hengelosestraat 513, 7521 AG Enschede, The Netherlands.

View full text