article

Agent-Based Systems for Intelligent Manufacturing: A State-of-the-Art Survey

Authors:

Douglas H. NorrieAuthors Info & Claims

Knowledge and Information Systems, Volume 1, Issue 2

Pages 129 - 156

https://doi.org/10.1007/BF03325096

Published: 01 May 1999 Publication History

Abstract

Agent technology has been considered as an important approach for developing distributed intelligent manufacturing systems. A number of researchers have attempted to apply agent technology to manufacturing enterprise integration, supply chain management, manufacturing planning, scheduling and control, materials handling, and holonic manufacturing systems. This paper gives a brief survey of some related projects in this area, and discusses some key issues in developing agent-based manufacturing systems such as agent technology for enterprise integration and supply chain management, agent encapsulation, system architectures, dynamic system reconfiguration, learning, design and manufacturability assessments, distributed dynamic scheduling, integration of planning and scheduling, concurrent scheduling and execution, factory control structures, potential tools and standards for developing agent-based manufacturing systems. An extensive annotated bibliography is provided.

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T. Papaioannou, J. Edwards. Mobile agent technology enabling the virtual enterprise: A pattern for database query. In: Working Notes of the Agent-Based Manufacturing Workshop, Minneapolis, MN, USA, 1998, pp. 106---116. (Mobile agent technology is used for enabling the virtual enterprise. Each mobile agent is used to encapsulate a single order as an order agent responsible for completion of the Sales/Order process for that order. Mobile Order Agents work with several static agents such as Sales Agents and StockControl Agents in the Sales/Order process. A good example of how to use mobile agents in Enterprise Integration.)

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A. Saad, G. Biswas, K. Kawamura, M. E. Johnson, A. Salama. Evaluation of contract net-based heterarchical scheduling for flexible manufacturing systems. In: Proc. Intelligent Manufacturing Workshop at IJCAI'95, Montreal, 1995, pp. 310---321. (Description of the Production Reservation approach using a bidding mechanism based on the Contract Net protocol to generate the production plan and schedule. Negotiation between part agents and machine agents was established under two different negotiation conditions: machine-centered and part-centered.)

[89]

N. Sadeh, M. S. Fox. CORTES: An exploration into micro-opportunistic jobshop scheduling. In: Proc. Workshop on Manufacturing Production Scheduling at IJCAI-89, Detroit, USA, 1989. (CORTES used micro-opportunistic techniques for solving the scheduling problem through a 2-agent system. In the CORTES, each agent is responsible for scheduling a set of jobs and for monitoring a set of resources. Resources are shared in the architecture, and the sharing of a resource is coordinated by its monitor.)

[90]

T. Sandholm, V. Lesser. Advantages of a leveled commitment contracting protocol. In: Proc. Thirteenth National Conference on Artificial Intelligence (AAAI- 96), Portland, OR, USA, 1996, pp. 126---133. (Description of a Leveled Commitment Contracting Protocol that allows self-interested agents to efficiently accommodate future events by having the possibility of unilaterally decommitting from a contract based on local reasoning. Its efficiency was shown through formal analysis of several contracting settings.)

Digital Library

[91]

E. Scalabrin. Conception et Réalisation d'environnement de développement de systèmes d'agents cognitifs, PhD Thesis, Université de Technologie de Compiègne, France, 1996. (Development of a multi-agent framework and a multi-agent system development environment.)

[92]

K. Schaefer, V. Patriti, P. Charpentier, P. Martin, D. Spath. The multi-agent approach in scheduling and control of manufacturing systems. In: Proc. PAAM'96, London, UK, 1996. (Initial proposal and research work related to that presented in {85}.)

[93]

M. J. Shaw, A. B. Whinston. Distributed Planning in Cellular Flexible Manufacturing Systems, Technical Report, Management Information Research Center, Purdue University, 1983. (Appears to be the earliest proposition to use agents in manufacturing scheduling and factory control: a manufacturing cell could subcontract work to other cells through a bidding mechanism.)

[94]

M. J. Shaw. Dynamic scheduling in cellular manufacturing systems: A framework for networked decision making, J. Manufacturing Systems 7(2), 83---94, 1988. (see {93})

[95]

W. Shen, J.-P. Barthès. An experimental multi-agent environment for engineering design, Int. J. Cooperative Information Systems 5(2---3), 131---151, 1996. (A cooperative engineering design environment using autonomous agents.)

[96]

W. Shen, J.-P. Barthès. Computer Supported Cooperative Environments for Engineering Design: A Survey, Technical Report 96-122, CNRS UMR Heudiasyc, Université de Technologie de Compiègne, France, 1996. (An extensive survey of cooperative environments for engineering design, with discussions on some related key issues. About 40 related projects were reviewed.)

[97]

W. Shen, D. H. Norrie. An agent-based approach for dynamic manufacturing scheduling. In: Working Notes of the Agent-Based Manufacturing Workshop, Minneapolis, MN, USA, 1998, pp. 117---128. (Combination of a bidding mechanism based on Contract Net protocol with a mediation mechanism based on Mediator architecture for dynamic manufacturing scheduling/rescheduling.)

[98]

W. Shen, D. Xue, D. H. Norrie. An agent-based manufacturing enterprise infrastructure for distributed integrated intelligent manufacturing systems. In: Proc. PAAM'98, London, UK, 1998, pp. 533---548. (A hybrid agent-based approach for integrating manufacturing enterprise activities with its suppliers, partners and customers within an open and dynamic environment. Description of its functional architecture, main features and a prototype implementation.)

[99]

W. Shen, F. Maturana, D. H. Norrie. Learning in agent-based manufacturing systems. In: Proc. AI & Manufacturing Research Planning Workshop, Albuquerque, NM, USA, The AAAI Press, 1998, pp. 177---183. (A detailed discussion on learning in agent-based manufacturing system with an example from the MetaMorph I project {59}.)

[100]

R. G. Smith. The contract net Protocol: High-level communication and control in a distributed problem solver, IEEE Transactions on Computers C-29(12), 1104---1113, 1980. (This classic paper introduces the Contract Net.)

Digital Library

[101]

P. Sousa, C. Ramos. A dynamic scheduling holon for manufacturing orders, J. Intelligent manufacturing, 9(2), 107---112, 1997. (A dynamic scheduling system architecture composed of the holons representing tasks together with the holons representing manufacturing resources. The contract net protocol was used to handle temporal constraints and deal with conflicts.)

[102]

F. Sprumont, J. P. Muller. AMACOIA: A multi-agent system for designing flexible assembly lines. In: Proc. PAAM'96, London, UK, 1996, pp. 573---585. (Using simulated annealing to search the problem space so as to optimize the technological cost of the design of flexible assembly lines.)

[103]

Stanford. JATLite. CDR, Stanford University, 1997. http://java.stanford.edu/ (Web site with information about JATLite, including JATLite Beta version for downloading, online demos, examples, source codes and more.)

[104]

J. M. Swaminathan, S. F. Smith, N. M. Sadeh. A multi-agent framework for supply chain dynamics. In: Proc. of NSF Research Planning Workshop on AI & Manufacturing, Albuquerque, NM, USA, 1996. (A similar approach to that presented in {32}. Its supply chain library with structural elements (agents) and control elements for coordination is unique.)

[105]

K. P. Sycara, S. F. Roth, N. Sadeh, M. S. Fox. Resource allocation in distributed factory scheduling, IEEE Expert, 6(1), 29---40, 1991. (An interesting example of using agents in manufacturing scheduling. See also {89}.)

Digital Library

[106]

M. M. Tseng, M. Lei, C. Su. A collaborative control system for mass customization manufacturing, Annals CIRP 46(1), 373---376, 1997. (A market-like model for collaborative control with intelligent agent representation of manufacturing resources. Similar to the approach by {54}.)

[107]

E. H. Van Leeuwen, D. H. Norrie. Intelligent manufacturing: Holons and holarchies, Manufacturing Engineer 76(2), 86---88, 1997. (Outlines holonic concepts and architecture and describes the scope of the HMS research consortium.)

[108]

L. Wang S. Balasubramanian, D. H. Norrie. Agent-based intelligent control system design for real-time distributed manufacturing environments. In: Working Notes of the Agent-Based Manufacturing Workshop, Minneapolis, MN, USA, 1998, pp. 152---159. (Adoption of the PDCH {11} and IEC-1499 Function Block architecture {44} for implementing real-time distributed manufacturing environments. Each Function Block is modeled as an agent.)

[109]

M. Wooldridge, N. Jennings. Intelligent agents: Theory and practice, Knowledge Engineering Review 10(2), 115---152, 1995. (An excellent earlier survey on intelligent agents.)

[110]

M. Wunderli, M. C. Norrie, W. Schaad. Multidatabase agents for CIM systems, Int. J. Computer Integrated Manufacturing 9(4), 293---298, 1996. (A general coordination architecture proposed for CIM systems based on multi-database and agent technologies.)

[111]

Y. Yan, T. Kuphal, J. Bode. Application of multi-agent systems in project management. In: Working Notes of the Agent-Based Manufacturing Workshop, Minneapolis, MN, USA, 1998, pp. 160---171. (Using mobile agents to implement service agents for realizing project management functions such as determination of the critical path, time calculation and activity scheduling. Each activity or resource needed in a project is represented by an agent. The resource agents and activity agents reside at the site of the project team members who own the resources or implement the activities. Its feasibility needs to be further studied and proved.)

[112]

M. Zweben, M. S. Fox (eds.). Intelligent Scheduling, Morgan Kaufman Publishers: San Francisco, CA, 1994. (A collection of papers, spanning a decade of research and development, written by experts in the field of AI planning and scheduling. Some papers describing intelligent scheduling systems using agent-based approaches have been reviewed and cited in this survey.)

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cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 1, Issue 2

May 1999

140 pages

ISSN:0219-1377

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Copyright © Copyright © 1999 Springer-Verlag Singapore Pte. Ltd.

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Published: 01 May 1999

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Yang YZhang BWu C(2024)Decision synthesis framework approach for dynamic dispatching and preventive maintenance in large flexible manufacturing systemsAdvanced Engineering Informatics10.1016/j.aei.2024.10241761:COnline publication date: 1-Aug-2024
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Li YWang J(2024)Decision-making in low-carbon supply chain networks considering demand uncertaintyNeural Computing and Applications10.1007/s00521-024-09595-036:17(9891-9901)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00521-024-09595-0
Trescak TLera-Leri RBistaffa FRodriguez-Aguilar JPelachaud CTaylor MFaliszewski PMascardi V(2022)Agent-Assisted Life-Long Education and LearningProceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems10.5555/3535850.3536117(1819-1823)Online publication date: 9-May-2022
https://dl.acm.org/doi/10.5555/3535850.3536117
Zhao HZaini Amat AMigovich MSwanson AWeitlauf AWarren ZSarkar N(2022)INC-Hg: An Intelligent Collaborative Haptic-Gripper Virtual Reality SystemACM Transactions on Accessible Computing10.1145/348760615:1(1-23)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3487606
Lin SZhang LLi JJi LSun Y(2022)A survey of application research based on blockchain smart contractWireless Networks10.1007/s11276-021-02874-x28:2(635-690)Online publication date: 1-Feb-2022
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Baothman F(2021)An Intelligent Big Data Management System Using Haar Algorithm-Based Nao Agent Multisensory CommunicationWireless Communications & Mobile Computing10.1155/2021/99777512021Online publication date: 1-Jan-2021
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Spirig JGarcia KMayer S(2021)An Expert Digital Companion for Working EnvironmentsProceedings of the 11th International Conference on the Internet of Things10.1145/3494322.3494326(25-32)Online publication date: 8-Nov-2021
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Zhou TTang DZhu HZhang Z(2021)Multi-agent reinforcement learning for online scheduling in smart factoriesRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2021.10220272:COnline publication date: 1-Dec-2021
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Derigent WCardin OTrentesaux D(2021)Industry 4.0: contributions of holonic manufacturing control architectures and future challengesJournal of Intelligent Manufacturing10.1007/s10845-020-01532-x32:7(1797-1818)Online publication date: 1-Oct-2021
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Lee WKim D(2019)Dynamic scheduling using a pheromone-based approach in multi-agent systemsApplied Soft Computing10.1016/j.asoc.2019.10587585:COnline publication date: 1-Dec-2019
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